\documentclass{rspublic}
\usepackage{verbatim}
\begin{document}
\title{Artificial intelligence and the future}
\author[W.F. Clocksin]{William F. Clocksin}
\affiliation{School of Computing and Mathematical Sciences, \\
Oxford Brookes University,\\
Wheatley, Oxford OX33 1HX}
\label{firstpage}
\maketitle

\begin{abstract}{artificial intelligence, problem solving, social constructionism}
We consider some of the ideas influencing current artificial intelligence research and propose an alternative conceptual framework that gives priority to social relationships as a key component and constructor of intelligent behaviour. It starts from Weizenbaum's observation that intelligence manifests itself only relative to specific social and cultural contexts. This is in contrast to one prevailing view, which sees intelligence as an abstract capability of the individual mind based on a mechanism for rational thought. The new approach is not based on the conventional idea that the mind is a rational processor of symbolic information, nor does it require the idea that thought is a kind of abstract problem-solving with a semantics that can be understood independently of its embodiment. Instead, priority is given to affective and social responses that serve to engage the whole agent in the life of the communities in which it participates. Intelligence is seen not as the deployment of capabilities for problem-solving, but as constructed by the continual, ever-changing and unfinished engagement with the social group within the environment. The construction of the identity of the intelligent agent involves the appropriation or `taking up' of positions within the conversations and narratives in which it participates. Thus, the new approach argues that the intelligent agent is shaped by the meaning ascribed to experience, by its situation in the social matrix, and by practices of self and of relationship into which intelligent life is recruited. This has implications for the technology of the future, as for example, classic AI models such as goal-directed problem solving are seen as special cases of narrative practices instead of as ontological foundations.
\end{abstract}

\section{Introduction}
Artificial Intelligence (AI) is a branch of computer science with the objective to equip machines with reasoning and perceptual abilities. This overall objective is pursued through two routes, which one might crudely divide into applied and theoretical:
\begin{itemize}
\item
Applied: to engineer systems capable of performing tasks which, if performed by a human, would be said to require intelligence, and
\item
Theoretical: to pursue a  scientific understanding of the computational principles underlying intelligent behaviour, as manifested in humans and other animals. 
\end{itemize}
Both routes need to propose and understand operational principles of thought and action in clear and unambiguous (computational) terms. These principles may form the foundations of computer implementations of thought and action and, if suitably grounded in experimental method, may in some ways contribute to explanations of human thought and behaviour.

Few definitions of intelligence adequately capture the richness of human experience. AI research has tended to use fluid boundaries of definition. For example, one traditional focus has been on problem solving and with the sensory and symbol processing capabilities that support problem solving. Yet, the concern is not restricted to humans: AI researchers have long been intrigued by the behaviour of animals that seems to show complex capabilities for sensory-motor coordination and planning, even though reptiles and insects are not considered intelligent in the way humans are.

AI received its name at a workshop at Dartmouth College in New Hampshire in 1956. There the founders of the field set the agenda for artificial intelligence research for the next 40 years. From this workshop, goal-directed problem solving implemented by search algorithms and supported by methods of automatic logical inference became the dominant conceptual framework, and continues to be so for most of the AI research community. 

From an engineering perspective, AI research has been applied in useful ways. Computer programs using methods derived from AI research play chess at the expert level, assess insurance and credit risks, schedule gates at airports, and search the contents of the Internet. From the scientific perspective however, the aim of understanding intelligence from a computational point of view remains elusive. Current programs for automatic reasoning can prove useful theorems concerning the correctness of large-scale digital circuitry, but exhibit little or no common sense. Current language processing programs can translate simple sentences into database queries, but the programs are misled by the kind of idioms, metaphors, conversational ploys or ungrammatical expressions that we take for granted. Current vision programs can recognise a simple set of human faces in standard poses, but are misled by changes of illumination, or natural changes in facial expression and pose, or changes in cosmetics, spectacles or hairstyle. Current knowledge-based medical expert systems can diagnose an infectious disease and prescribe an antibiotic therapy, but if you describe your motorcar to the system, it will tell you what kind of meningitis your car has: the system does not know that cars don't get diseases. Current learning systems based on neural networks can forecast financial trends given historical data, but cannot predict the date of Easter nor prime numbers given a large set of examples. 

Computer scientists have known for many years that it is relatively easy to program computers to accomplish skilled tasks that are difficult for people to do (such as solving huge systems of equations), while at the same time we are unable to design a computer program to do things that people can do easily (such as recognise faces reliably under changing conditions). This distinction is unexpected, paradoxical, and a block to further progress in AI. Software based on AI methods can land a jumbo jet safely, but there are no convincing demonstrations of computers carrying out the range of perception and motor-control tasks that even a year-old infant can do easily. Yet, serious and informed thinkers had predicted that the problem of AI would be essentially solved by now. Turing (1950) predicted that within 50 years, computers would be able to take part convincingly in human conversation. Minsky (1967) predicted that, `Within a generation, the problem of creating `artificial intelligence' will be substantially solved'. Several years later, the then Stanford Research Institute published the results of a study on the future of artificial intelligence technology (Firschein, Fischler, Coles and Tenenbaum, 1973).  Using the Delphi technique of technological forecasting, they put a large and carefully designed battery of questions concerning future trends to an international panel of experts. The replies were analysed, and summaries fed back to the experts, each of whom was asked for comments on and revision of his earlier estimates. After the third iteration, an additional questionnaire was used to calibrate each individual's error in an unrelated numerical estimation task, and a self-rating scheme was employed to assess professional expertise over the various topics involved.  A few of the results are listed in Table 1; Michie (1974) gives a fuller account.  In his commentary on this report, Michie was even more optimistic: `If the authors' survey results can be criticized, it is on the grounds of conservatism'. Yet, although a few supporting components of the products listed are in the development stage, the state of the art is nowhere near the expected targets. 
\begin{table}
\caption{A selection of Delphi study results taken from Firschein, Fischler, Coles and Tenenbaum (1973)}
\begin{tabular}{lcc}
\hline
Product & Median prototype date & Median commercial date \\
\hline
Voice response order-taker & 1978 & 1983 \\
Insightful economic model & 1984 & 1990 \\
Universal game player & 1980 & 1985 \\
Automatic language translator & 1987 & 1995 \\
Robot chauffeur & 1992 & 2000 \\
General factotum & 2000 & 2010 \\
\hline
\end{tabular}
\end{table}

Why is it that AI research has become impeded or limited? And is this related to which tasks are relatively harder or easier to implement? 
I have previously (Clocksin, 1998, p 105) considered some possible reasons for impediments to AI research;  Brooks (2001) has arrived at similar conclusions independently. The following is a selection of possible reasons why progress in AI has been much slower than expected:
\begin{itemize}
\item
\textit{Resource limitations.} That is, our theories are right, but our computers do not have enough processing power or memory capacity to do the job properly. Chess playing is one example where it is important to have enough processing power. Current programs employ brute force search of possible board positions to arrive at a good move, and more computing power and memory space means that more variations can be searched more thoroughly. However, we don't know precisely how people play chess, but it is clear that people do not play chess by brute force search. It is not obvious that resource limitations are relevant if we don't have a theory that specifies the resource requirements.
\item
\textit{Complexity limitations.}  Perhaps intelligence is a property that emerges when the underlying process becomes sufficiently complex. The complexity might be so great that the process is not understandable. According to this reason, AI research is impeded because we are unable to arrange a sufficient degree of complexity. For example, complexity could take the form of a massive number of indiscernable connections between large numbers of processes, or a massive number of constraints that influence the solution to an ill-conditioned problem having a massive number of unknown parameters. Yet, we have no reason to support a complexity argument if we do not possess a theory that explains precisely why the complexity is necessary, and why the operational principles of the problem in question cannot be discerned. 
\item
\textit{The impossibility of AI.} Dreyfus (1972), Penrose (1990) and Searle (1990) argue that AI is impossible. Against this one has to face the fact that intelligent beings exist, at least one of them is reading this paper, and intelligent behaviour can be assumed to be based on physically realisable principles of operation that it is the task of science to understand and articulate in computational form. Understanding these principles -- which we do not yet do -- must come prior to attempts to implement them as computer programs, so I believe we are not yet in a position to pronounce on the impossibility of AI. 
\item
\textit{Conceptual limitations.} That is, AI is not impossible, but we are not working within a suitable conceptual framework. Or, we are not paying sufficient attention to investigations made in other fields where other potentially significant conceptual frameworks obtain.
\end{itemize}
There are several types of conceptual limitations, and several authors have attempted to explain such limitations by what Brooks calls a `new stuff' hypothesis: that there may be some extra form of `stuff' in intelligent systems outside our current scientific understanding. `New stuff' theories are prevalent in popular writings about the problem of consciousness and AI. For example, Penrose (1990) suggests that quantum effects in the microtubules of neurons might be the locus of consciousness. He has not worked out a theory of how this might work, but claims that it is an essential element. Another `new stuff' hypothesis has been advanced by Chalmers (1996, p 127). Just as Maxwell's theory related electric fields with magnetic fields in a way inconceivable before Maxwell (to simplify the history), Chalmers suggests the possibility of yet undiscovered psychophysical laws that relate physical stimulation with psychological percepts. Such laws would not be additional physical laws, but would be supervenience laws, describing how experience arises from physical processes. I shall not consider `new stuff' hypotheses further in this paper.

One type of conceptual limitation can be described as `going against the grain' of physical reality. That is, AI is limited because we are not identifying and exploiting known properties of physical reality that mediate and facilitate intelligence in ways that are as yet unknown. It is felt among some in the AI research community that when the problem of AI is finally 'cracked', the solution will turn out to be much simpler that we might have guessed it to be, and this will be because we have found a solution that somehow `goes along the grain' of what is already known about physical reality. As naive as this hope may appear, AI practitioners might draw some inspiration from past situations where simple solutions have been found for difficult problems by going `along the grain' of Nature.  Here are two examples: making the perfect `french fry', and starting a gas turbine jet engine.   French fries, known in the UK as chips, are made by cooking cut rods of potato (with approximately square cross-section) in a deep fat fryer. The problem is to cook the potato so that the finished product has a consistent appearance and texture, and this involves knowing the optimal cooking time. Once a rule determining the optimal cooking time is found, it can be automated. The difficulty is that potatoes vary widely in their water and sugar content, requiring significant variation in cooking time (although the sugar content can be regulated to an extent by storing the potatoes at a warm temperature for several weeks in order to convert sugars to starch, and water content can be regulated to an extent by pre-cooking at a certain temperature).  The problem remains of how to control the cooking time when sugar and water content are known only to within a fairly wide range. The engineer is tempted to design a system that measures the water and sugar content of a batch of chips, then sets a timer based on the measurement. Two shortcomings of this approach are that measuring the water and sugar content of the potato pieces immediately before frying is not practicable, and the process is not controlled after the timer is set.  The actual solution adopted is based on the observation that when a batch of cold raw potato pieces is dumped into a vat of hot cooking oil, the temperature of the oil will immediately drop and then slowly rise. Once the oil temperature has risen about 3 degrees from the lowest point, the chips are ready (Gladwell, 2001). The solution requires only a temperature sensor and simple electrical circuitry; it turns out not to be necessary to control anything at all during the cooking process.

The second example is starting or re-starting a gas turbine jet engine. One type of simple jet engine consists of a compressor fan and a turbine wheel connected by an axial rotor. Atmospheric air is admitted and compressed by the compressor fan, and is then mixed with fuel. The fuel/air mixture ignites and expands rearwards through the blades of the turbine wheel, rotating it and therefore the compressor fan, which in turn sucks in more air to mix with the fuel to continue the cycle. A self-sustaining combustion can be produced under the right conditions. The problem is starting the engine, or possibly quickly re-starting an engine that has lost combustion (or `flamed out') in flight. An electric starter motor is provided to spin the compressor to a given speed. Once combustion is self-sustaining, a centrifugal clutch disengages the starter motor. The difficulty is that combustion will only occur and self-sustain under the right conditions of ambient and internal temperatures, atmospheric and internal pressures, compressor speed, and ratio of fuel-air mixture. The only variable that can be controlled is the rotational speed of the starter motor (fuel input is controllable, but can be set to a known value). The engineer is tempted to design a system that measures all these parameters and sets the optimum speed of the starter motor to spin the compressor. However, the solution that was adopted in early jet aircraft was simply to start the compressor to spin at maximum speed, then disengage the clutch and switch off the starter motor while starting the fuel feed. The compressor would gradually slow down, and when the optimum air pressure was achieved, self-sustaining combustion would occur, spinning the compressor up to operating levels. I hasten to reassure the reader that the modern jet engines used in commercial airliners have highly sophisticated engine management systems that are far more reliable and robust than their predecessors.

Both solutions involve arranging a naturally occurring optimisation of a single variable by sequential `search': in the first case by a monotonic increase cooking oil temperature after the potatoes are immersed, and in the second case by a monotonic decrease of compressor speed.
It is no accident that these two problems were dealt with during the middle 1950s, a time during which technology was sufficiently advanced and user requirements were sufficiently demanding to engender the problems in the first place, but microcomputer and sensor technology were not sufficiently advanced to tempt the engineer to adopt more complicated solutions.

Another type of conceptual limitation can be described as the `wrong technology'. Since its beginning, AI has assumed that the principles of intelligence could be implemented in the form of suitably designed algorithms with associated data structures, and that sequential execution of the algorithm by a digital computer would suffice to implement the particular principle that was being programmed. If intelligence is based on principles of operation that are computable -- and there is no convincing reason why this should be otherwise -- then execution on a sequential digital computer would at least demonstrate the principle of operation. However, a brief glance at the architecture of animal brains suggests a quite different organisation: a densely interconnected network having comparatively low transmission rate exhibiting alarmingly high levels of stochasticity.  Connectionism -- the study of computer simulated neural networks -- has been heralded as a fresh start to AI (Churchland, 1996) by advocating a technology that is considered to be closer to natural neural systems than the sequential digital computer is. However, despite its significant and important advances in the theory and technology of pattern recognition, connectionism does not propose a change in our ideas about what processing is for and what the intelligent computer should do. Connectionism has simply advocated different ways to do it: essentially, an implementation technique based on approximating an unknown input/output relationship with or without a training set of exemplars. Whether a particular problem is being solved by a sequential algorithm or by a neural network, the computer is still carrying out an abstract task in isolation, defined and parameterised in a way judged appropriate by its author. Whether the computer is programmed by explicit specification of an algorithm or by iterative improvement of a matrix of numerical weights is not a sufficiently deep distinction upon which to claim a new approach to AI. For this reason one may happily adopt implementation techniques based on connectionist principles, but at the same time one must remain unconvinced by `wrong technology' arguments.

The type of conceptual limitation considered in the remainder of this paper is of the `wrong framework' variety. To prepare the ground, let us reconsider the paradox that it is relatively easy to program computers to accomplish skilled tasks that are difficult for people to do, while at the same time we are unable to design a computer program to do things that people can do easily.
The root of this paradox lies in the distinction between mundane and expert tasks. People perform mundane tasks automatically, yet these seem to require complex reasoning. The principles of washing dishes or changing babies' diapers or hammering nails or installing automobile windshields can be taught within minutes to people who have widely varying intellectual abilities, creativity and insight. These tasks involve the handling of imprecise quantities of intractable materials such as soap suds, cloth, slippery dishes and glass sheets, all difficult to model mathematically.  In these tasks no precise measurement is called for (`squirt about this much soap into the sink'), specifications are incomplete (`then jiggle it round until it fits';  `then pin these two bits together') and circular (`you know it fits when it snaps into place'), yet dextrous perceptual-motor operations and sensitive judgements are employed.  The fact that apparently easy tasks may conceal a large amount of unconscious sophisticated analysis and processing is not the point.  Whether these tasks need `insight' or `representations' or `knowledge' or `problem solving' is also not the point.  The point to make here is that these tasks are taught by `showing how' and learned `by doing', relying on social interaction, with performance parameters circumscribed by the embodiment of the participants in the social interaction.

By contrast, expert tasks require specialised skill and training, for example medical diagnosis, playing expert chess, and financial decision making. Paradoxically, mundane tasks are the harder to automate, and expert tasks are the easier to automate. Why is it hard to program computers to do things people find easy to do? And why is it easy to program computers to do things that people find hard to do? The answer is that implementing expert tasks does not require theories of the person. It requires a theory of medical diagnosis, or chess, or financial markets, or whatever the task domain may be. We can formulate such theories, and the implemented system can function with reference to and perform relative to such theories, and this is often sufficient to perform the task. On the other hand, implementing the mundane tasks seems to involve knowing how the embodied person relates to things or other persons, and this is one of the theories we lack today.

Chess playing was once thought to be the paradigm example of the application of intelligence, and chess-playing automata have been proposed through the centuries.  Modern precursors to AI include Shannon's and Turing's proposals for chess-playing computer programs, and game playing has long been an interest of AI research. 
The claims of a game-playing approach to AI are that playing a game demonstrates intelligence, much intelligent behaviour can be represented as (or reduced to) a game, and various activities of interest to military funders of AI can be represented as a game. These claims were particularly prominent during the Cold War `think tank' years of the 1950s-70s (Edwards, 1996). 
Yet, though programs can now play expert chess, the process of developing these programs has taught us very little about intelligence. McCarthy (1997) remarks
\begin{quotation}\noindent
In 1965 the Russian mathematician Alexander Kronrod said, ``Chess is the \textit{Drosophila} of artificial intelligence.'' However, computer chess has developed much as genetics might have if the geneticists had concentrated their efforts starting in 1910 on breeding racing \textit{Drosophila}. We would have some science, but mainly we would have very fast fruit flies. 
\end{quotation}
The substantial effort in game playing research has taught us only what a limited model it is for human activity. The simplifications and abstractions it offers are misleading, but the technical effort devoted towards this aim has been useful for the development of new algorithms and data structures.
\begin{comment}
DON'T USE THE FOLLOWING
Returning for a moment to the problem of recognising faces, we have an extraordinary ability to recognise faces and can do so
despite changes in viewing angle, lighting, age and hairstyle. This
should make human operators very successful at detecting the fraudulent
use of photographic identity cards, at recognising the perpetrator of a
crime and at matching the face of a suspect to video surveillance
footage. However, psychological research has shown that we tend to make very
inaccurate eyewitnesses and, more surprisingly, cannot reliably perform the
simple matching tasks involved with checking photographic identity cards and identifying
suspects from CCTV footage. This has led to the conclusion that we are
good at recognizing familiar faces and poor at recognizing unfamiliar
faces. This suggests a strong social element in face recognition, as the process by which a person's face becomes familiar is based on social participation.
\end{comment}

Artificial intelligence as a scientific enterprise is in a vulnerable position partly because there are no ready-made theories, and partly because it is not clear that what we \textit{can} theorise and write computer program about is relevant to the concern. If there were a reasonably complete theory of intelligence, current technology might put us in a position to implement at least parts of the theory in a productive way. Other fields benefit from the existence of ready-made theories developed within the last 150 years. For example, a theory of electromagnetism offers a relationship between an electric field and a magnetic field, and can be used to simulate, design and analyse electrical circuits as well as to develop better theories of electromagnetism. A theory of aerodynamics offers a relationship between lift, thrust, drag and gravity. This helps to design aircraft, to understand natural flight, and to develop better theories of aerodynamics. By contrast, artificial intelligence research has collected a body of theory about algorithms for problem solving, but these theories are not about intelligence nor are they about persons. Problem-solving has been seen as an abstract capability independent from the needs and desires of the individual, and independent from the cultural and social context. Theories of mind and intelligence have been considered by philosophers and psychologists, but generally these are not phrased in terms that can be translated to effective algorithms.

I have argued elsewhere (Clocksin, 1995, 1998) that AI is limited not by processing power nor in handling complexity, but by taken-for-granted concepts that form an implicit normative load.  Historically, AI research has been freighted by a �command and control� paradigm (Edwards, 1996) in which intelligent behaviour is seen as the application of decision processes, represented using formal reasoning, to abstract well-specified tasks.  This can be described as a `rationalist' approach,  from the logician's definition of rationalism being a chain of deduction that does not result in a contradiction. Indeed, the effort to make progress in AI through one style of knowledge representation and automated reasoning, namely logic programming and the Prolog programming language (e.g. Kowalski, 1979; Clocksin and Mellish, 1981; Baral and Gelfond, 1994), culminating in Japan's Fifth Generation Programme of the 1980s (Feigenbaum and McCorduck, 1983; Unger, 1987), was one of the more ambitious manifestations of this approach.

It may be necessary to break away from the rationalist tradition in order to do justice to alternative models of minds -- models based on persons, identity and social context -- that may ultimately provide a practical foundation for AI. There are signs that this `re-framing' of AI is already underway to a limited extent.  The remainder of this paper is concerned with a framework for AI informed by four strands of thought emerging from social and developmental psychology. First, there has been an increasing concern with personhood: with persons, agency and action, rather than causes, behaviour and objects (Shotter and Gergen, 1989). Second, there has been an increasing emphasis on the self as a social construct, that persons are the result of interactions with significant others, and that the nature of these interactions is in turn shaped by the settings in which these interactions occur (Levine, 1992). Third, the self and identity are no longer considered to be prior givens (Luntley, 1995). Though the self may lack an objective centre, the self engages in the performance of a centralised focus that can assert itself as a prior given. Fourth, logical reasoning is neither a good model for the functioning mind nor for human performance. We come to value logical reasoning not because it mirrors the operation of the mind (which it doesn't), but for the benefits it confers, such as accountability of discourse. 
 
\section{Reframing Rationality}

In the wake of the 1956 Dartmouth workshop, the conceptual framework for artifical intelligence research and the rational problem solving approach to intelligence began to be mapped out (McCarthy, 1959; Minsky, 1961; Newell and Simon, 1961). Almost twenty years passed before this framework became more clearly articulated. The `physical symbol system hypothesis' (Newell and Simon, 1976) holds that the processes required to produce intelligent action can be simulated with a collection of physical symbols and a set of mechanisms that produce structures built from those symbols.
According to this hypothesis, intelligence is realised through a representational system based on a physical symbol system, and that intelligent activity is goal-orientated, symbolic activity. McCarthy (1977) states
\begin{quotation}\noindent
The epistemological part of AI studies what kind of \textit{facts} about the world are available to an observer with given opportunities to observe, how these facts can be represented in the memory of a computer, and what \textit{rules}  permit legitimate conclusions to be drawn from these facts. [his emphasis]
\end{quotation}
The two essential ingredients are representation -- the idea that relevant aspects of the world need to be encoded in symbolic form in order to be usable -- and the symbolic activity of inference, by which rational conclusions can be made by applying rules of inference to axioms. As Waltz (1999) has expressed it,
\begin{quotation}
\noindent
A substantial community within AI has long held the view that the human brain somehow implements a perfect logic machine and that all intelligence is built on top of this. A corollary to this view is that in order to create a fully intelligent system all we need to do is create a perfect logic machine and then build everything on top of it. (p 32)
\end{quotation}
While AI's emphasis on logical symbol-processing as the basis of intelligence had been criticised by Weizenbaum (1976) and by Dreyfus (1972), Winograd and Flores (1985) were probably the first to call for a re-examination of the rationalistic tradition and its influence on artificial intelligence research.  According to Dreyfus and Dreyfus (1986),
\begin{quotation}
\noindent
the failure of AI research will continue until intelligence ceases to be understood as abstract reason and computers cease to be used as reasoning machines.
\end{quotation}
Furthermore, McDermott  (1987) argues that
\begin{quotation}
\noindent
...the skimpy progress observed so far is no accident, and in fact it is going to be very difficult to do much better in the future.  The reason is that the unspoken premise..., that a lot of reasoning can be analysed as deductive or approximately deductive, is erroneous. (p 151)
\end{quotation}
Objections to AI's equation of commonsense reasoning with `approximately deductive' reasoning were anticipated from the start. In his response to McCarthy's paper `Programs with common sense' (1959) printed following the paper, Oliver Selfridge replied
\begin{quotation}\noindent
I have never yet heard of someone drawing correct conclusions from correct premises. I mean this seriously. ... Most women have never inferred it, but they get along pretty well, marrying happy husbands, raising happy children, without ever using deductive logic at all.
\end{quotation}
No doubt it is a mark of social progress that Selfridge's point would be phrased slightly differently were it to be expressed nowadays. Moving from the problems of deductive (or approximately deductive) reasoning to the problems of representation and problem solving, Brooks (1991a,b) has given a powerful critique of traditional AI research. From AI's origins in the late 1950s,
\begin{quotation}\noindent
Traditional artificial intelligence has adopted a style of research where the agents that are built to test theories of intelligence are basically problem solvers that work in a symbolic abstract domain. (1991b, p 583)
\end{quotation}
Brooks argues that abstract knowledge representation and problem solving methods `cannot account for large aspects of what goes into intelligence', and that the field `concentrates much of its energies on anomalies within formal systems which are never used for any practical task'.

AI's assumptions about rationality as the seat of intelligence come from a philosophical tradition that goes back more than 2000 years. However, is man, as Aristotle put it, really a `rational animal'? Rationality in the logician's terms is related to consistency: an argument is rational if it does not produce a contradiction. However, consistency of argument is difficult to achieve in the face of assumptions and rules that change over time. Beginning with Doyle (1979) and McCarthy (1980), much effort has been devoted to `truth maintenance': methods for automatic deductive reasoning that prevent the derivation of contradictions in the presence of changing assumptions. This is still an area of active research. And yet human performance in problem solving is marked by two characteristics that suggest that people sit rather more lightly to logical consistency: people can happily entertain contradictory views (often without being aware of it), and when put to the test, human `rationality' is frail and fallible. People do not rely solely on deductive inference in their everyday thinking. Human experience is marked by incompetence, blunders, and acts of misjudgement. The average AI programmer (but not the average psychologist) might be surprised to learn that for normal people, irrational (in the technical sense) behaviour is the norm rather than the exception. The more accessible background literature on the `normality' of irrationality includes Sutherland (1992), Manktelow and Over (1990), Dixon (1976), and Piattelli-Palmerini (1994). When put to the test, human rationality is frail and fallible. Human reasoning thrives on the basis of actions and beliefs that cannot be justified nor supported logically. We often make profoundly irrational assumptions, then argue rationally to reach conclusions that are irrational but desirable. We sometimes proceed not by what we discover to be true, but by what we want to be true. Yet nonrationality works in everyday situations. Nonrationality has worked also in matters of historical and political significance. Judd (1999) describes the British constitution as
\begin{quotation}
\noindent
...that wonderfully various, flexible, colourful, moth-eaten brocade, at best only semi-rational, but the product of centuries of hard won wisdom in how to manage ourselves, more responsive to our inchoate desires and needs, and more adaptable to change...
\end{quotation}
One will not be able to find a copy of the British constitution: it has what one can call a `decentred' identity. Its identity is distributed, and subsists in collections of stories, cases, practices, assumptions and precedents, each with their own exceptions, each admitting plural and contested interpretations. In such a context there is no point in truth maintenance or in trying to find consistent interpretations. 

So is the human mind based on a capacity for rational thought? If so, then why does there seem to be a lack of connection between rationality and everyday behaviour? And, if a rational mind is based on a rational brain, then why should we have a rational brain at all, as it seems to find little employment as an engine of logic? It is important to stress that in drawing attention to the inadequacy of rationality as an explanation of intelligence, we are not advocating unreason, nor is this an attempt to doubt rationalism as a means of making scientific progress. There is no questioning the fact that humans are users of rational discourse to the extent possible under limiting circumstances, and that we value the benefits that such discourse brings. The question is concerned only with a foundational assumption of AI research: whether intelligence relies on a rational problem-solving capability based in the individual's brain.
So which is the more likely hypothesis:
Intelligence derives from a rational mind, operating according to rules of logic (with truth-maintenance and the rest), but can hardly ever be used in the way `intended';
or, the mind is not based on principles of rationality, but as people in society we have come to perform and to value a developed but imperfect rational discourse because it has tangible benefits. The benefits of rational discourse include mutual intelligibility and accountability. 

The traditional conceptual framework for AI decomposes the problem of intelligence into a set of abstract, isolated, context-free problems that the brain is assumed to solve. Yet, surely problems take this form simply because it is attractive (for theoretical and practical reasons) for researchers to specify and represent them in that way. By contrast, an alternative conceptual framework sees intelligence as a set of context-bound, situated, socially and historically contingent and provisional performances. It is not obvious that mechanised reasoning and problem solving are appropriate, effective and usable within such a framework. I have previously argued (Clocksin, 1998) that logical reasoning is a normative product of, rather than a foundation for, such a framework. Waltz (1999) has independently come to similar conclusions: `My view is that logic is really the fruit of learning, one of the \textbf{last} things we learn, not the system on which everything is based' (p 32, his emphasis).


\section{Constructionism}
A new framework for AI can make use of a way of thinking about persons known as `social constructionism'.
To reduce confusion it is useful to make a distinction between construc\textit{tivism} and construc\textit{tionism}. Constructivism, a term coined probably by Nelson Goodman and also called cognitive constructivism, describes the work of renowned psychologists Jean Piaget and Ulric Neisser. The main idea is that understanding is created through the operation of a variety of mental schemata and procedures for analysis and synthesis of schemata. This is the main assumption behind cognitive psychology, and it is safe to say this is closely related to the prime -- if unexpressed -- assumptions of most AI research. The  interaction between cognitive science and AI has been fruitful (e.g. Johnson-Laird, 1983). In this context, AI has been concerned with the design of data structures to implement mental schemata, and the design of algorithms to implement the analysis and synthesis procedures. 
 
By contrast, constructionism describes the work of Gergen (1991, 1994), Potter (1996), Shotter (1993) and Harr\'{e} (1992). It has also been called discursive constructivism, which is probably the source of the confusion of terminology.  The main concern of social constructionism are the processes by which human abilities, experiences, common sense and scientific knowledge are both produced in, and reproduce, human communities (Shotter and Gergen, 1994, p.~\textit{i}). The idea is that constructional processes are to be found in relationships, often discursive, between persons. These relationships embody situated reasoning: that is, a contextualised meaning-producing performance. 

The pioneering AI researcher Seymour Papert (1993) is informed by the constructivist tradition, but has begun to address constructionist concerns (thus leading some commentators to confuse the terminology further). Papert has a special concern with the role of physical artifacts, or objects, in thinking. Papert writes that
\begin{quotation}\noindent
construction that takes place in the head often happens especially felicitiously when it is supported by construction of a more public sort `in the world' ... Part of what I mean by `in the world' is that the product can be shown, discussed, examined, probed, and admired. It is out there.
\end{quotation}

Papert argues that we learn through interacting with artifacts:  that we create an understanding of the world by creating artifacts, experimenting with them to see how they work, and modifying them to work better. Since artifacts are in the world and can provide an anchor for understanding among a group of people, they can also address a problem that faces the constructivist cognitive scientists. Namely, if we construct knowledge as an individual act, how can understandings be shared?

Constructionist approaches stress the way mind and action are contingent on specific cultural forms. They see minds not as having fixed essences from which behaviour flows, but as being built from the symbolic resources of cultures by means of participation in human relationships. Discourse in narrative form -- examples are conversations and stories -- is seen as the central organising principle of construction. Thus, understandings are never fully shared, but they are negotiated among conversants, and this process itself is the meaning-bearing act.

Constructionist approaches offer an alternative concept of the self. The tradition we have inherited from recent centuries places a central emphasis on the individual as autonomous agent. In this tradition,
\begin{quotation}\noindent
Individuals are the fundamental units of society; relationships are secondary or artificial, a byproduct of interacting individuals. (Gergen, 1991, p. 156)
\end{quotation}
The inherited tradition has acted to foster an area of AI research activity known variously as intelligent agent or autonomous agent research (e.g. Maes, 1990; Wooldridge and Jennings, 1995).
However, constructionist approaches question these concepts
\begin{quotation}\noindent
...of the individual as the centre of knowledge (``one who knows''), as possessor of rationality, as author of his or her own words; as one who creates, decides, manipulates or intends. (op sit, p. 156)
\end{quotation}
Instead, one's own role becomes that of a participant in social processes. The constructionist concept of the self and identity is discussed further in the following section on narrative. 

Constructionist approaches provide a different concept of language and communication. The tradition we have inherited assumes the transmission theory of communication, in which messages are transmitted from one person to another. These messages are encoded and decoded so that they represent either the world of objects that exists outside the communication process, or the internal world of meanings and emotions of those who communicate. By contrast, the constructionist view sees communication as
\begin{quotation}\noindent
Patterns of social action that are co-constructed in sequences of evocative and responsive acts; these patterns comprise an ecology that is our social worlds... [Communication] calls into being and reproduces the events and objects of our social worlds; it is a process of making and doing. (Pearce, 1994, p. 19).

...Instead of thinking of conversation as the exchange of messages between conversants, the social constructionist perspective sees conversants as within an interlocking matrix of conversations. Each conversant is the product of previous conversations and the producer of the present and future conversations. (p. 23)
\end{quotation}
According to familiar AI models, the intelligent agent operates a perception-processing-action loop in order to solve problems that are presented to it. Meaning is constructed in the mind as a result of internal symbol-processing capability. By contrast, the constructionist approach privileges social interaction as the meaning-constructing process. Here the mind is for social development, engagement with persons, and the institution and appropriation of personhood and identity.
\begin{quotation}\noindent
In this way, meaning is born of interdependence. And because there is no self outside a system of meaning, it may be said that relation[ship]s precede and are more fundamental than self. Without relationship there is no language which which to conceptualize the emotions, thoughts, or intentions of the self. (Gergen, 1991, p. 157)
\end{quotation}
For the social constructionist, the reality of relationships is persons in conversation.
\begin{quotation}\noindent
Instead of taking it for granted that we understand another person's speech simply by grasping the inner ideas they have supposedly put into words, that picture of how we understand each other is coming to be seen as the exception rather than the rule. Most of the time, we realize, we do not fully understand what another person says. Indeed, in practice, shared understandings occur only occasionally, if they occur at all. And when they do, it is by people testing and checking each other's talk, by them questioning and challenging it, reformulating and elaborating it, and so on. For in practice, shared understandings are developed or negotiated over a period of time, in the course of an ongoing conversation.
\end{quotation}
By contrast, AI research has concentrated on practical applications such as question answering and information retrieval, typified by this extract from the following dialogue (the human's part is introduced by H, and the computer's reply is introduced by C):
\begin{verbatim}
     H:  All cats are mammals.
     C:  I understand.
     H:  There is a cat in the box.
     C:  I understand.
     H:  Is there a mammal in the box?
     C:  Yes.
\end{verbatim} 
Statements made by the human are treated as facts to be stored in a database. The response `I understand' is simply an acknowledgement that the statement has been converted to an internal representation and database storage has taken place. Questions posed by the human are treated as theorems that need to be proved from the information in the database together with basic rules of inference. Though this style of interaction was popularised by the impressive SHRDLU program of Winograd (1972), even current state-of-the-art systems (e.g. Shapiro, 2000) offer little more than technical improvements in the handing of linguistic and logical details. Such a model of communication is far removed from the richness of the social constructionist approach, and much conceptual adjustment needs to be made in AI research before the constructionist model of communication can be realised.
%
\section{Emotions}
Conventional models of AI do not include emotions.  The separation of intelligence from emotions goes back at least as far as Plato, with the separation of the rational soul from the body with its skills, emotions and appetites. Aristotle continued this dichotomy when he defined man as the rational animal, and Descartes further refined this type of dualism.   By now the separation of intelligence and emotion pervades our thinking about computers, with the computer representing pure intelligence unaffected by emotion.  This view has been challenged more recently. Picard (1997) argues that AI has long ignored the importance of emotions, and that emotions are part of the essential foundation for intelligence.

An example of way that emotions are marginalised by the conventional understanding of the mind is illustrated by Mr Data, the android character on the popular television programme \textit{Star Trek: The Next Generation}. Mr Data can function perfectly well as an intelligent being without emotions, but has an optional plug-in `emotion chip' that makes him more human. Most of the time he prefers to function without the emotion chip. With the chip at his disposal, Mr Data is at least more versatile than Mr Spock of the original \textit{Star Trek} series. Half-human, half-Vulcan (an alien humanoid species), Spock has been trained since infancy to repress emotion because the Vulcan culture prizes logic above all things. Much of the dramatic content of these programs is concerned with how Data and Spock function in a human culture: there is plenty of scope for misunderstanding and the `comedy of manners'. Given that these accounts are after all science fiction, these characters will appeal to adolescent males who themselves can find difficulty coming to terms with their emotions and the complexities of the social realities in which they are beginning to find themselves.  How much more comforting to retreat to a simplified world by identifying with heroes who succeed by applying pure logic and calculation.

By contrast, the constructionist finds such accounts incoherent. Not only are there glaring inconsistencies in the model -- the Data and Spock characters actually behave in ways that use the affective skills they are reputed not to require -- but also the `optionality' of affect is associated with individuals one sees only in the human population as profoundly disordered. It is probably intuitions of this type that lead Picard and others to reject the optionality of affect, and instead to adopt a view that treats the world of affect as something prior to intelligence and problem-solving. Developmentally this could imply an early requirement for social relationship between the mother and newborn. This is borne out by studies (Sluckin, 1999; Aitken and Trevarthen, 1997) that demonstrate that humans are conversational from birth. The inescapable conclusion from this work is that mothers and neonates are able to coordinate their actions so as to take account of each other. Both act into what Wittgenstein would call emergent rule games.

There are good reasons to suppose that AI of the future will be emotional. Arguing that intelligence must be situated in the totality of human life and experience, Dreyfus (1992, p 63) condemns the traditional separation of rationality from needs and desires:
\begin{quotation}
\noindent
If one thinks of the importance of the sensory-motor skills in the development of our ability to recognize and cope with objects, or of the role of needs and desires in structuring all social situations, or finally of the whole cultural background of human self-interpretation in our simply knowing how to pick out and use chairs, the idea that we can simply ignore this know-how while formalizing our intellectual understanding as a complex system of facts and rules is highly implausible.
\end{quotation}

By contrast to the \textit{Star Trek} stories about Mr Data, two recent science fiction films start from a perspective that sees intelligence as depending upon and formed by human relationships. The film \textit{Bicentennial Man} (1999) was based on the short story of the same name by Isaac Asimov and the novel \textit{The Positronic Man} by Isaac Asimov and Robert Silverberg. The basic theme is a robot's 200-year long journey from household appliance to human being. The humanoid robot Andrew is designed as a simple domestic servant, but has been equipped with a sense of curiosity, creativity and enquiry.  Put into the service of a suburban family, the friendships he forms within the family teach him what it is to be human. Andrew decides to buy his freedom using the proceeds of earnings from the sale of his original woodcarvings, and strikes out on his own. Outliving his original owners, over a period of decades Andrew gradually replaces his robot parts with new organic technology. After 100 years he falls in love with a descendent of his original owners.  He fights a lengthy legal battle for status as a human. Humanity also means death, so he gratefully dies known as the Bicentennial Man. 

The film \textit{A.I.} (2001), directed by Stephen Spielberg, depicts a society largely dependent on humanoid robots who are highly skilled at various tasks but who are not considered truly intelligent because they lack human emotion.   In a quest to develop the truly intelligent robot, Professor Hobby designs a small boy robot David to be placed with a family who will bring up the boy as their own. The chosen father is a colleague whose wife Monica has sunk into remorse because their natural son Martin has fallen into a coma and is not expected to live. Monica comes to appreciate David's virtues and presents him with a teddybear who becomes his faithful companion. Programmed to give and elicit affection, David is soon calling Monica `Mommy', and bereft of her only natural child, she accepts and warms to the artificial boy. A medical advance restores Martin, who is resentful of David. Eventually Monica is persuaded to abandon David in a forest, where he must make his way in a harsh dystopia where vengeful humans destroy hapless robots. Eventually David makes his home and pursues his dream not just to love a human but to be loved by one.

Whatever one might think of the derivative and sentimental nature of these films, they depict a new idea of the intelligent robot. Instead of the robot as the emotionless supreme calculating engine such as Mr Data who will always work with human society but who will never be a true part of it, these films present the truly intelligent robot as one whose identity and capabilities are developed within the context of a social group, with all the propinquity of relationships this implies. Such an account coheres with Weizenbaum's (1976) observation, which is of profound significance for a new conceptual framework for AI:
\begin{quotation}
\noindent
�intelligence manifests itself only relative to specific social and cultural contexts.
\end{quotation} 
A similar observation is expressed by Dreyfus (1984): `since intelligence must be situated it cannot be separated from the rest of human life'.

These two films offer another way to consider the `strong AI' versus `weak AI' controversy. 
Searle (1980) has distinguished `strong' and `weak' forms of AI in the following way. In weak AI, the computer is considered to be simply a tool in the study of the mind that enables us to test psychological explanations. In strong AI, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have cognitive states. These two films depict robot protagonists that exhibit the `strong AI' model: they really do have minds. Many AI researchers are unwilling to adopt a `strong AI' position because they can't imagine how a rational logic engine could be said to be or have a mind. The weak AI position on the other hand is a safe harbour that also offers the comfort of intellectual credibility. However, if one starts from another perspective, as these films do, of the computer as constructing its social reality by participation in human relationships, then the strong AI position may not seem so implausible. These films are of course pure fiction, but often our stories can change our expectations of future technology and gradually replace outmoded `thought experiments' with newer ones.
%
\section{Coordination}
The intelligent robot of the future will account for itself in negotiating the episodes that are occurring and for negotiating the meanings of particular acts within episodes. For example, within a conversation, conversants need to handle scripts that describe or prescribe how certain things should be done, and their own goals for which episodes they want to achieve or what to prevent.  When understanding a story, the listener/reader needs to discern the scripts, plans and goals being enacted by the characters in the story. There has been some effort within AI research on formulating the principles of scripts and goals to address the problem of  understanding simple stories and conversations. 

Schank and Abelson (1977) start from the premise that knowledge of specific situations is stored in the human mind as scripts. A script is a structure that describes appropriate sequences of events in a particular context. Encountering a familiar situation such as entering a restaurant can activate a stored script of this situation that might include sitting at a table, ordering a meal, paying for the meal, and leaving the restaurant. When encountering a new situation, people resort to plans, the mechanisms that underlie scripts. A plan is a repository for general information that will connect events that cannot be connected by the use of an available script or by standard rules of inference. The purpose of plan understanding is to find out the goals of the actors in a story. Goals, intentions underlying action, can be grouped into themes. Themes contain the background information upon which we base our predictions that an individual will have a certain goal. Themes can be categorised into role themes, interpersonal themes, and life themes. Goals are needs like sleep or hunger, which in turn are explained by themes, such as success or love.

More recent work has focussed on improving the knowledge structures that are extracted from the story. Dyer's (1983) Thematic Abstraction Units (TAUs) are more abstract than the Schank-Abelson scripts and contain information for dealing with plans relating to the events at hand. They define abstract knowledge structures that aim to capture a deep level of understanding that can be used for story categorisation. Story categorisation is achieved through `cross contextual remindings' and by recalling abstract story types, called adages. Adages provide planning information:  given a particular situation, they determine which plan should be used, what its intended effect is, why the plan failed and a recovery strategy for the plan.  Given a text in story form, Dyer's system constructs a knowledge dependency structure characterises the story in terms of plans, failed plans and attempted recovery of plans. It links common sense relationships between goals, plans and events, in order to organise and package the internal components of knowledge structures.

The computer programs implemented to test portions of these theories run into a number of problems. At the level of technical linguistics, the computer programs cannot handle quantification, anaphora, and metaphor.  They have been used in only very limited settings (a restaurant setting and a divorce story). No indication is given how scripts might be constructed apart from being programmed explicitly, and there is no consideration given as to whether these ideas could be applied to longer narratives than the toy systems presented. But more serious concerns are to do with the implied conceptual framework. It is true that many of our everyday social activities are stereotyped, but no matter how stereotyped, going to the restaurant is not a self-contained context-free game. Most of the activity that goes on is a `background' of practices that do not relate specifically to restaurant-going, but that influence the particular sequence and detail of events. In fact, we understand going to a restaurant because it is a part of our many activities in which we interact with other people.

One telling point is that these systems are unable to deal with what Pearce (1994), in a textbook on human communication, refers to as coordination problems. Stories and conversations are located within a complex web of culturally defined intentions, anticipated responses and justifications which are constructed because participants experience emotions and feel empathy for one another. Fluent understanding of stories and taking part in a conversation call for the coordination of the participants' scripts and goals with their insight into other people's nature. Stories and conversations thereby have a `moral order', in which every culture and relationship develops sets of expectations for how our behaviours relate to the rights and responsibilities that we have as interlocutors (for example as authors, readers, conversants). When understanding a story, we place ourselves in the story and image how we would react, how particular episodes or characters would make us feel if we encountered them in our lives.  Finding meaning in stories and conversations therefore requires empathy, not simply the representation of facts in a database. This point is relevant to the previous discussion on emotions, for in order to have empathy, one needs to be able to feel one's own emotions. 

One possible way forward concerns the use of `accounts'. An account is an explanation that attempts to change the meanings of an action. Human conversants use accounts to solve coordination problems. Empathy is essential for the account giver, because to offer an account requires an understanding that a coordination problem has occurred or is about to occur, and such problems are recognised and validated by insight into the conversant.  Accounts can be variously volunteered, demanded, and accepted. Following Buttney (1987) and Pearce (1994), we illustrate two types of accounts: disclaimers and excuses. Disclaimers (Hewitt and Stokes, 1975) are offered before performing an action that might create a coordination problem, for example:
\begin{itemize}
  \item
`Hedging' is an explicit statement that what follows is tentative. It is a signal that the actor may not be able to defend what follows, and gives an opening for an interlocutor to inform or disagree; for example, `You might think this is a crazy idea, but...'. 
\item
A `sin licence' is a statement that acknowledges that what is about to be said violates the script for appropriate behaviour; for example, `You'll probably resent my saying so, but...'.
\item
An `appeal for suspended judgement' asks for an opportunity to explain the statement that is about to be made before being required to defend oneself or the statement from attack; for example, `No, let me finish my point...'. 
\end{itemize}
Excuses (Semin and Manstead, 1983) are given after the action, for example:
\begin{itemize}
\item
Accidents, in which the actor denies the intention to threaten the coordinated eposide; example, `I didn't mean to do that'.
\item
Evasion, in which an actor denies agency; example, `I cannot remember doing that'.
\item
Appeals for sympathy, in which actors appeal to mitigating circumstances; example, `it seemed like a good idea at the time'.
\end{itemize}
The point here is not whether accounts are convenient additional symbol processing capabilities that need to be spoon-fed to AI systems. Instead, accounts need to be learned as a result of co-constructing the meaning of actions in conversation, based upon conversants who have emotional abilities and who thereby increase their competence by using forming and using accounts.
 %
\section{Conversation and Narratives}
The new framework for AI outlined here is built upon the social constructionist priority given to conversation and narrative. 
The narrative or story provides a framework that facilitates the interpretation of experience, for it is through the narratives people have about their own lives and the lives of others that they make sense of their experience.  Narratives are not restricted to the form of a story with its initial complication followed by its denouement, although stories can be good examples of narratives.  The idea of narrative is not restricted to written texts.  Not only do narratives influence the meaning that people give to experience, they also influence which aspects of experience people select for articulation.  Narratives provide not merely a reflection or mirror of life, but provide for the shaping and structure of life.

There is a social process involved in moving from the conversational use of language to the construction of the closed set of intralinguistic references that are typical of narratives. As Shotter (1993) expresses it, there is a transition from a reliance on particular meanings negotiated `on the spot' with reference to a immediate and shared context, to a reliance on a corpus of previously determined meanings -- a body of interpretive resources into which the narrative user has been recruited. 

It is possible that traditional AI research has misinterpreted the significance of narratives. For example, it is an enormously difficult undertaking to write a computer program that can understand simple stories in a natural language, as such understanding seems to require a vast background knowledge. The following alternatives of a simple two-sentence story are given by Lenat and Feigenbaum (1991), quoted by Dreyfus (1992):
\begin{verbatim}
    Mary saw a dog in the window. She wanted it.
\end{verbatim} 
To understand the first story we need to resolve the reference of the pronoun `it'. Does `it' refer to the dog or the window? Most readers naturally assume that `it' refers to the dog. However, consider the alternative stories
\begin{verbatim}
    Mary saw a dog in the window. She smashed it.
    Mary saw a dog in the window. She pressed her nose up against it.
\end{verbatim}
In the second story, we understand that the window is being smashed by Mary. In the third story, both the dog and Mary are capable of pressing a nose against something.  Most readers would conclude that Mary is pressing her own nose against the window in order to get a closer look at the dog.

The standard AI approach to understanding stories relies on consulting facts and rules about dogs and windows and how a typical person would react. By contrast, a new conceptual framework would require the understander to imagine how it would feel in the situation, and thereby arrive at an understanding that is related to experience. From an early age, children see dogs in windows and want the dog. Children understand this story because they know what it is like to want a dog. This is related to the idea of reader-response criticism: the reading of texts is not an attempt to discover a meaning that is hidden until the necessary procedures are applied, but to construct a meaning from experience. Finding meaning in a text (story, conversation, narrative) thus requires empathy and insight into people's nature, not simply a collection of facts and rules. It is tempting to offer a traditional Schank-like solution based on thinking up a small set of facts and rules that are reckoned to determine empathy and insight into people's nature, but the lack of progress shown by this type of ad hoc procedure does not inspire confidence. 

Another aspect of dialogue and story understanding is connected with the essential provisionality of discourse. Conventional AI treats text understanding as `finding the answer to a problem', whether the problem is resolution of pronoun anaphora or finding out who did what in a story. Real story understanding is more complex, and the so called `hermenutic circle' of critical theory may be relevant. Understanding and interpretation influence each other in a circular way. To understand a text (or take part in a conversation), we need to interpret it. But a certain understanding may lead us to consider a fresh interpretation. When the text is read in the light of the new interpretation, it may change our understanding of it. This may call for a revised interpretation, and so on. Such circles may occur several times during the reading and re-reading of a text, and may also account for the way in which conversations normally progress, not as an orderly sequence of question and answer, but as a tangle of starts and re-starts as both interlocutors negotiate respective hermeneutic circles to arrive at a co-constructed meaning.

Such circles might be better described as spirals, for one never visits the same locus in hermeneutic phase-space (as it were) twice.  There is also no reason to restrict the number of trajectories through the space to one.  AI research needs to be more attentive to the continual and developmental character of the hermeneutic circle.  A system that is not committed to the hermeneutic circle cannot be said to understand. It is this capacity for continual co-evolution (in this example, of interpretation and understanding), or ontogenesis, that is characteristic of a sustaining system.  Such a sustaining system is in turn characteristic of a functioning mind. The AI thoughtworld needs to reject `one shot' models such as finding the answer to a problem and maintaining consistency of beliefs. By contrast, ontogenesis of the type proposed here may proceed for a system in which problems and answers are not neatly defined, and consistency in a logical sense is never achievable.  Thus there needs to be an emphasis on the provisionality of thought, another hallmark of a social constructionist perspective.

Alasdair MacIntyre (1981) recognises several diverse uses of narrative.  He argues that human action is narrative in form, that human life has a fundamentally narrative shape, and that people are story-tellers who position their lives and arguments within narrative histories.  Communities and traditions are invested with continuity through narrative histories, and epistemological progress is marked by the construction and reconstruction of more adequate narratives.  For Jerome Bruner (1986), narrative is one of the modes of cognitive functioning, a way of knowing that provides a distinctive way of ordering experience and constructing reality.  The other mode is logical argument, and Bruner notes that while narrative deals with `the vicissitudes of human intentions' and is built upon concern for the human condition, logical arguments are either conclusive or inconclusive.

The importance of narrative has also been stressed by the discursive psychology of Harr\'{e} and Gillett (1994) who are mainly concerned with higher cognitive functions and who see the mind as
\begin{quotation}\noindent
...embedded in historical, political, cultural, social, and interpersonal contexts.  It is not definable in isolation.  And to be a psychological being at all, one must be in possession of some minimal repertoire of the cluster of skills necessary to the management of the discourses into which one may from time to time enter. [p 25-26]
\end{quotation}
A new conceptual framework for AI might depend on the use of narratives as a foundation for memory, learning, and self-identity within the construction of meaning. The remaining points about narrative in this section are adapted from Clocksin (1998).
The main difference between narratives and other structures such as knowledge representations as used in AI (Way, 1991) is that narratives serve to engage the individual with the contingencies of identity and relationships with others.  The individual develops or constructs identity by locating itself within the discourse -- the conversations, stories, narratives -- with which it engages.  Thus, identity is not simply a matter of building up a store of knowledge or cognitive capabilities,  but the taking up of  positions within a set of discourses as they are negotiated.  Therefore, behaviour involves a certain performance and production of a `self' which is the effect of a discourse, and which is always presented from a certain point of view.  The fact that a developing self may claim to represent itself as a truth prior to the discourse must be seen as a phenomenon emerging from the engagement rather than as an ontological assumption.

This `taking up of a position' within a narrative may be identified at all levels: from the steady-state behaviour of an electro-chemical process in tissues, to the habituation of a tissue in response to a pattern of conditioning stimuli, to the formation of a reflex, to learning a pattern of behaviour.  At the highest social level it may involve the articulation, appropriation, or commitment to a particular policy, a system of beliefs and values, a family group, a football team, ways of expressing one's role in society, and even beliefs about biological imperatives such as gender (Butler, 1991, 1993). The ways in which one plays social games is the way in which the identity becomes, to use Butler's list of words, `established, instituted, circulated and confirmed' (p. 18).  

Narrative serves as a memory, not only in a collective and institutional sense of telling stories to each other (memorial), but also as the foundation for the individual's memory (Middleton and Edwards, 1990).  Memory as narrative provides an alternative to the conventional idea of memory as a storehouse of knowledge and experience, and of remembering as a process of retrieval from the storehouse.  Memory is understood not as the storage-and-retrieval manipulation of a network of entities, but rather as a particular practice within the context of a discourse. Memory is an active process in which experience can be emplotted (i.e. the lived experience is `recruited' by a prior narrative) and personified (i.e. attributed the status of a `character' in a prior narrative) in ways constrained by the usages and contexts of prior narratives.  This `circular' definition is committed to the idea of memory not as storage and retrieval of facts, but as the continual re-membering of our memories of our memories (sic).  Memories cannot be decoupled from the narrative structure of remembering, for outside a narrative they have no meaning (Shotter, 1990).  
Narratives can be edited, and this is particularly relevant to the question of memory and learning.  As Platinga (1992) puts it, `our memories are not inert, but undergo a process of editing, whereby they are regularized, rendered more retainable and reshaped with an eye to subsequent circumstances and events' (p. 45).

Learning  can then be understood as means of editing or rewriting narratives. In this context, learning begins with the assumption that the stories of people's lives, as they or others have constructed them, do not sufficiently represent their lived experience.  Learning then becomes a process of storying and re-storying the lives and experiences of people.  In this way narrative comes to play a central role and points to the AI-related concepts of  `belief revision' not as a method for guaranteeing a consistent deductive system, but as a description of what happens when re-storying takes place.

These points suggest a view of the mind that is an inversion of the conventional view taken by artificial intelligence and cognitive science researchers. The conventional view assumes that behaviour arises from basic capabilities for cognition and problem solving. The emphasis seems to be on the individual mind that generates solutions to problems. The questions of emotion and interaction with others are considered as additional -- possibly even unnecessary or irrelevant -- complications or side-issues.  By contrast, the `upside down' view sees problem solving as a practice or policy that has been constructed as a result of a basic capacity for social interaction within a cultural context, which itself is based on fundamental reciprocalities of need and desire.  Therefore, further progress in artificial intelligence research needs to be attentive to issues arising from the articulation of practices and policies that have been constructed though conversation and narrative. 
%
\section{Conclusion}

A new conceptual framework for AI will not view intelligence as a kind of abstract puzzle solving applied by an individual to arbitrarily defined problems presented by an alien environment. It is true that certain isolated episodes of intelligent behaviour can be given an interpretation as puzzle-solving, maximising of utility, and so forth.  However, we need to accept that there is a close ontogenetic connection between the nervous system and the environment.  It is possible to accept that this connection has the effect of individualising precisely which capabilities and information sources are employed by the mind as it develops in conversation, without necessarily endorsing a structuralist ontological commitment as to the precise delineations of capability and information source.

The framework outlined here has been shaped by Weizenbaum's observation: Intelligence manifests itself only relative to a matrix of social and cultural contexts.  There is a propinquity of participation in communities that are defined by historically individualised mutualities and reciprocalities of need and desire.  The ways we participate with this matrix, and construct identities that take up positions within this matrix, are through policy, contract, and institution.  This participation and construction  is made possible by a variety of means such as memorial, mimesis, ritual, education and therapy, all of which serve to expose and rewrite narratives.  When a system is said to `understand' is thus seen as an historical contingency constituted in part by policies articulated by the social group, rather than by the underlying cognitive nature of the individual brain.

The fact that emotion needs to be treated in a framework for AI grows directly from the emphasis placed on conversation and narrative. Conversations and narratives construct forms of intelligibility that furnish accounts of events over several different timescales. Individual actions gain their significance from they way in which they are found within the conversation or narrative. In the same way, emotional expressions find their meaning only when inserted into particular sequences of interaction. In effect, they are constituents of lived narratives (Gergen, 1994).

One implication for future technology is the idea of  narrative architectures, built upon a foundation which is attentive to the way that signals and symbols influence (and are influenced by) the way we as communities of individuals make sense of experience, construct our identities, and produce meaning in the world as conversants and narrative users. This makes me optimistic about the prospect for what John Searle called 'strong AI'. The characteristic of an AI system that really can be said to understand is that it can perform an account of the identity it has constructed in conversation using narratives it has appropriated from the culture and society that it shares with us. Such a system negotiates its positions to become instituted among the group, participating in the social consciousness it co-constructs with members of the group.
%
\begin{acknowledgements}
\end{acknowledgements}
\begin{thebibliography}{}
\item
Aitken, K.J. and Trevarthen, C., 1997. Self/other organization in human psychological development. \textit{Development and Psychopathology} \textbf{9}, 653-677.
\item
Baral, C. and Gelfond, M., 1994. Logic programming and knowledge representation, \textit{Journal of Logic Programming} \textbf{19/20}, 73-148.
\item
Brookes, R., 1991a. Intelligence without representation. \textit{Artificial Intelligence} \textbf{47}, 139-159.
\item
Brookes, R., 1991b. Intelligence without reason. \textit{Proceedings of the Xth International Joint Conference on Artificial Intelligence}, 569-595.
\item
Brookes, R., 2001. The relationship between matter and life. \textit{Nature} \textbf{409}, 409-411.
\item
Bruner, J., 1986.  \textit{Actual Minds, Possible Worlds}.  Cambridge, MA: Harvard University Press.
\item
Butler, J., 1991. Imitation and gender subordination. In \textit{Inside/Out: Lesbian theories, gay theories}, ed D. Fuss. New York: Routledge. 13-31.
\item
Butler, J., 1993. \textit{Bodies that matter}.  New York: Routledge.
\item
Buttney, R., 1987. Blame-account sequences in therapy: The negotiation of relational meanings. \textit{Semiotica} \textbf{78}, 219-247.
\item
Chalmers, D.J., 1996. \textit{The conscious mind}, Oxford University Press.
\item
Clocksin, W.F., 1995. Knowledge representation and myth. In Cornwell, J., ed., \textit{Nature's Imagination: The frontiers of scientific vision}, Oxford University Press, 190-199.
\item
Clocksin, W.F., 1998. Artificial intelligence and human identity. In Cornwell, J., ed., \textit{Consciousness and human identity}, Oxford University Press, 101-121.
\item
Clocksin, W.F. and Mellish, C.S., 1981. \textit{Programming in Prolog}, Heidelberg: Springer Verlag.
\item
Dixon, N., 1976. \textit{On the psychology of military incompetence}, London: Futura.
\item
Doyle, J., 1979. A truth maintenance system. \textit{Artificial Intelligence} \textbf{12}, 231-272.
\item
Dreyfus, H.L., 1972.  \textit{What computers can't do}. Cambridge, MA: MIT Press.
\item
Dreyfus, H.L., 1992.  \textit{What computers \textrm{still} can't do}. Cambridge, MA: MIT Press.
\item
Dreyfus, H.L. and Dreyfus, S.E., 1986.  \textit{Mind over Machine}.  London: Macmillan.
\item
Dyer, M. G., 1983. \textit{In-depth Understanding}. Cambridge, MA: MIT Press.
\item
Edwards, P.N., 1996. \textit{The closed world: Computers and the politics of discourse in cold war America}.  MIT Press.
\item
Feigenbaum, E.A. and McCorduck, P., 1983.  \textit{The fifth generation: Artificial intelligence and Japan's computer challenge to the world}. Reading, MA.: Addison-Wesley.
\item
Firschein, O., Fischler, M.A., Coles L.S., and Tenenbaum, J.M., 1973. Forecasting and assessing the impact of artificial intelligence on society. In \textit{Proceedings of the Third International Joint Conference on Artificial Intelligence}, Stanford, CA: Stanford Research Institute Publications Department, 105-120.
\item
Gergen, K., 1991. \textit{The saturated self: Dilemmas of identity in contemporary life}. BasicBooks.
\item
Gergen, K., 1994. \textit{Realities and relationships: soundings in social construction}, Cambridge, MA: Harvard University Press.
\item
Harr\'{e}, R.,1992. \textit{Social being: A theory for social psychology}. Oxford: Blackwell.
\item
Harr\'{e}, R. and Gillett, G., 1994.  \textit{The Discursive Mind}.  London: Sage.
\item
Hewitt, J. and Stokes, R., 1975. Disclaimers. \textit{American Sociological Review} \textbf{40}, 1-11.
\item
Gladwell, M., 2001.  The trouble with fries. \textit{The New Yorker}, March 5, 2001.
\item
Johnson-Laird, P.N., 1983. \textit{Mental models: towards a cognitive science of language, inference and consciousness}. Cambridge University Press.
\item
Judd, A. 2000. Is this a new age of reason -- or should witches watch out? \textit{Daily Telegraph}, 3 April, \textit{op ed}.
\item
Kowalski, R., 1979. \textit{Logic for problem solving}, Amsterdam: North-Holland.
\item
Lenat, D. and Feigenbaum, E., 1991.  On the thresholds of knowledge, \textit{Artificial Intelligence} \textbf{47}(1-3).
\item
Luntley, M., 1995. \textit{Reason, truth and self: The postmodern reconditioned}, London: Routledge.
\item
Maes, P., 1990, ed.  \textit{Designing autonomous agents}. Cambridge, MA: MIT Press.
\item
Manktelow, K.I. and Over, D.E., 1990. \textit{Inference and understanding}, London: Routledge.
\item
MacIntyre, A., 1981.  \textit{After virtue: A study in moral theory}.  London: Duckworth.
\item
McCarthy, J., 1959. Programs with common sense. \textit{Proceedings of a Symposium on Mechanization of Thought Processes}, Vol I, 77-84, National Physical Laboratory, Teddington. London:HMSO.
\item
McCarthy, J., 1977. Epistemological problems of artificial intelligence. \textit{Proceedings of the 4th International Joint Conference on Artificial Intelligence}, 1038-1044, San Mateo, CA: Morgan Kaufmann.
\item
McCarthy, J., 1980. Circumscription � a form of non-monotonic reasoning. \textit{Artificial Intelligence} \textbf{13}, 27-39.
\item
McCarthy, J., 1997.  AI as sport. Review of \textit{Kasparov versus Deep Blue: Computer Chess comes of age}. \textit{Science} \textbf{276}(5318), 1518-1519.
\item
McDermott, D., 1987.  A critique of pure reason.  \textit{Computational Intelligence} \textbf{3}, 151-160.
\item
Michie, D., 1974. Machine intelligence as technology. In \textit{On machine intelligence}, New York: Wiley, 193-199.
\item
Middleton, D. and D. Edwards, 1990. \textit{Collective Remembering}. London: Sage.
\item
Minsky, M., 1961.  Steps toward artificial intelligence. \textit{Proceedings of the Institute of Radio Engineers} \textbf{49}(1), 8-30.
\item
Minsky, M., 1967. \textit{Computation: Finite and infinite machines}. Englewood Cliffs, NJ: Prentice-Hall.
\item
Newell, A and Simon, H.A., 1961. GPS, a program that simulates human thought. \textit{Lernende Automaten}. Munich: R. Oldenbourg KG.  Also reprinted in E. A. Feigenbaum and J. Feldman (Eds.) \textit{Computers and Thought}. New York: McGraw-Hill, 279-293.
\item
Papert, S., 1993. \textit{The Children's Machine}. New York: Basic Books.
\item
Pearce, W.B., 1994. \textit{Interpersonal communication: Making social worlds}. New York: HarperCollins.
\item
Penrose, R., 1990. \textit{The Emperor's new mind}. London: Vintage.
\item
Piattelli-Palmarini, 1994. \textit{Inevitable illusions: How mistakes of reason rule our minds}. Cambridge, MA: MIT Press.
\item
Picard, R., 1998. \textit{Affective Computing}. Cambridge, MA: MIT Press.
\item
Platinga, T., 1992.  \textit{How memory shapes narratives}. Lampeter: Mellen.
\item
Potter, J., 1996. \textit{Representing Reality: Discourse, Rhetoric and Social Construction}. London: Sage.
\item
Schank, R. and Abelson, R.� (1977).� \textit{Scripts, plans, goals, and understanding: An inquiry into human knowledge structure}.� Hillsdale, NJ: Lawrence Erlbaum Associates.
\item
Searle, J., 1980. Minds, brains, and programs, \textit{The Behavioral and Brain Sciences} \textbf{3}, 417-424.
\item
Searle, J.R., 1990. Is the brain's mind a computer program, \textit{Scientific American} \textbf{262}(1), 20-25.
\item
Semin, G.R. and Manstead, A.S.R., 1983. \textit{The accountability of conduct: A social psychological analysis}. London: Academic Press.
\item
Shapiro, S.C., 2000. An introduction to SNePS 3. In B. Ganter and G.W. Mineau, eds. \textit{Conceptual structures: logical, linguistic, and computational issues}. In series \textit{Lecture notes in computer science} \textbf{1867}, Berlin: Springer-Verlag, 510-524.
\item
Shotter, J., 1990. The social construction of remembering and forgetting. In Middleton and Edwards (1990).
\item
Shotter, J., 1993. \textit{Conversational realities: Constructing life through language}. London: Sage.
\item
Shotter, J. and Gergen, K., 1994. Series preface, in T.R. Sarbin and J.I. Kitsuse (eds) \textit{Constructing the social}. London: Sage.
\item
Sluckin, A., 1999. ``Humans are conversational from birth'': systemic therapy, developmental psychology and the artistic metaphor, \textit{Human Systems} \textbf{10}(1), 11-23.
\item
Sutherland, S., 1992. \textit{Irrationality: The enemy within}. London: Penguin.
\item
Turing, A.M., 1950. Computing machinery and intelligence, \textit{Mind}, Vol.LIX, No. 236, October.
\item
Unger, J.M., 1987. \textit{The fifth generation fallacy: Why Japan is betting Its future on artificial intelligence}, Oxford University Press.
\item
Waltz, D., 1999. The importance of importance. (AAAI-98 Presidential Address), \textit{AI Magazine} \textbf{20}(3), 19-35.
\item
Way, E.C., 1991.  \textit{Knowledge Representation and Metaphor}.  London: Kluwer.
\item
Weizenbaum, J., 1976.  \textit{Computer power and human reason}.  London: Penguin.
\item
Winograd, T., 1972. \textit{Understanding natural language}. New York: Academic Press.
\item
Winograd, T. and Flores, F., 1985.  \textit{Understanding computers and cognition: A new foundation for design}.  London: Addison-Wesley.
\item
Wooldridge, M. and Jennings, N.R., 1995. Intelligent agents: Theory and practice, \textit{The Knowledge Engineering Review} \textbf{10}(2), 115-152.
\end{thebibliography}
\end{document}