In a paper in this journal, Neil Levy challenges Nicholas Agar’s argument for the irrationality of mind-uploading. Mind-uploading is a futuristic process that involves scanning brains and recording relevant information which is then transferred into a computer. Its advocates suppose that mind-uploading transfers both human minds and identities from biological brains into computers. According to Agar’s original argument, mind-uploading is prudentially irrational. Success relies on the soundness of the program of Strong AI—the view that it may someday be possible to (...) build a computer that is capable of thought. Strong AI may in fact be false, an eventuality with dire consequences for mind-uploading. Levy argues that Agar’s argument relies on mistakes about the probability of failed mind-uploading and underestimates what is to be gained from successfully mind-uploading. This paper clarifies Agar’s original claims about the likelihood of mind-uploading failure and offers further defense of a pessimistic evaluation of success. (shrink)

This is the guest editor's introduction to a JETAI special issue on philosophical foundations of AI.

Epistemologists have debated at length whether scientific discovery is a rational and logical process. If it is, according to the Artificial Intelligence hypothesis, it should be possible to write computer programs able to discover laws or theories; and if such programs were written, this would definitely prove the existence of a logic of discovery. Attempts in this direction, however, have been unsuccessful: the programs written by Simon's group, indeed, infer famous laws of physics and chemistry; but having found no new (...) law, they cannot properly be considered discovery machines. The programs written in the Turing tradition, instead, produced new and useful empirical generalization, but no theoretical discovery, thus failing to prove the logical character of the most significant kind of discoveries. A new cognitivist and connectionist approach by Holland, Holyoak, Nisbett and Thagard, looks more promising. Reflection on their proposals helps to understand the complex character of discovery processes, the abandonment of belief in the logic of discovery by logical positivists, and the necessity of a realist interpretation of scientific research. (shrink)

The tension between rule and judgment is well known with respect to the meaning of substantive legal commands. The same conflict is present in fact finding. The law penetrates to virtually all aspects of human affairs; irtually any interaction can generate a legal conflict. Accurate fact finding about such disputes is a necessary condition for the appropriate application of substantive legal commands. Without accuracy in fact finding, the law is unpredictable, and thus individuals cannot efficiently accommodate their affairs to its (...) commands. The need for accuracy and predictability in legal fact finding has generated a search for formal tools to apply to the task. Among the tools that have been examined are Bayes' Theorem and expected utility theory (Bayesian or statistical decision theory). These tools do not map well onto trials, which in turn has generated an examination of alternative approaches, in particular the story model and the relative plausibility theory. This paper discusses these issues in turn. It elaborates the basic structure of trials in the American tradition; examines the uneasy relationship between trials and such formalisms as Bayes' Theorem and expected utility theory; and introduces the relative plausibility theory as an explanation of the nature of juridical proof. (shrink)

This paper explores the assumption of autonomy. Several arguments are presented against the assumption of runtime autonomy as a principle of design for artificial intelligence systems. The arguments vary from being theoretical, to practical, and to analytic. The latter parts of the paper focus on one strategy for building non-autonomous systems (the practice view). One critical theme is that intelligence is not located in the system alone, it emerges from a history of interactions among user, builder, and designer over a (...) given set of data as mediated by the system. A second critical theme is that artificially intelligent systems are ongoing projects that must be continuously adapted and revised using joint person-machine efforts. (shrink)

Description: The massive redeployment hypothesis (MRH) is a theory about the functional organization of the human cortex, offering a middle course between strict localization on the one hand, and holism on the other. Central to MRH is the claim that cognitive evolution proceeded in a way analogous to component reuse in software engineering, whereby existing components—originally developed to serve some specific purpose—were used for new purposes and combined to support new capacities, without disrupting their participation in existing programs.

Embodied cognition (EC) is growing up, and How the Body Shapes the Mind is both a sign of, and substantive contributor to, this ongoing development. Born in or about 1991 (the year of publication of seminal works by Brooks, Dreyfus, and Varela, Thompson & Rosch), EC is only now emerging from a tumultuous but exciting childhood marked in particular by the size and breadth of the extended family hoping to have some impact on its early education and upbringing. As family (...) members include computer science, phenomenology, developmental and cognitive psychology, analytic philosophy of mind, linguistics, neuroscience, and eastern mysticism—just to name a few—EC has both benefited and suffered from a wealth of different and often incompatible ideas about who and what it is, what it should do with its life, even what language it should speak. Gallagher brings some cohesion and consistency to this situation, not by surveying and synthesizing these competing approaches, but by carefully marshalling the evidence and developing the vocabulary to thoroughly consider a few fundamental issues. (shrink)

Maintaining adequate performance in dynamic and uncertain settings has been a perennial stumbling block for intelligent systems. Nevertheless, any system intended for real-world deployment must be able to accommodate unexpected change—that is, it must be perturbation tolerant. We have found that metacognitive monitoring and control—the ability of a system to self-monitor its own decision-making processes and ongoing performance, and to make targeted changes to its beliefs and action-determining components—can play an important role in helping intelligent systems cope with the perturbations (...) that are the inevitable result of real-world deployment. In this article we present the results of several experiments demonstrating the efficacy of metacognition in improving the perturbation tolerance of reinforcement learners, and discuss a general theory of metacognitive monitoring and control, in a form we call the metacognitive loop. (shrink)

In this article I develop a case for a theory of intelligence incorporating transpersonal dimensions, namely integrated intelligence. Some recent expanded theories of intelligence move into concepts like creativity, wisdom, and emotional intelligence. Yet they remain embedded within mainstream intelligence theory and its reductionist and materialist presuppositions. Although various theorists in consciousness theory have developed transpersonal models that are beginning to be discussed in some mainstream circles, mainstream intelligence theory is yet to address the broader implications of this. Recent changes (...) in the global economy and the needs of populations have created a need for an expanded theory of intelligence, and more intuitive thinking. (shrink)

Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for these components.In (...) recent CBR research in Artificial Intelligence (AI), five paradigmatic approaches have emerged: statistically-oriented, model-based, planning/design-oriented, exemplar-based, and adversarial or precedent-based. The paradigms differ in the assumptions they make about domain models, the extent to which they support symbolic case comparison, and the kinds of inferences for which they employ cases. (shrink)

In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a (...) possible bridge between the humanities and the natural sciences, philosophy and neurophysiology, psychology and integrated circuits-including systems that today are taken for granted, such as the computer interface with mouse pointer and windows. This writing describes a few results of AI that have modified the scientific world, as well as the way a layman sees computers: thetechnology of programming languages, such asLISP-witness the unique excellence of academic departments that have contributed to them-thecomputing workstations-of which our modern PC is but a vulgarised descendant-theapplications to the educational field-e.g., the realisation of some ideas of genetic epistemology-and tointerdisciplinary philosophy-such as Hofstadter's associations between the arts and mathematics-and the use ofAI techniques in music and musicology. All this has led to a generalisation of AI towards Negrotti's overallTheory of the Artificial, which encompasses further specialisation such asartificial reality, artificial life, and applications ofneural networks among others. (shrink)

In What Is Thought? Eric Baum proposes a computational explanation of thought.

In CyberPhilosophy: The Intersection of Philosophy and Computing, edited by James H. Moor and Terrell Ward Bynum (Oxford, UK: Blackwell, 2002), 66-77. Also in Metaphilosophy 33.1/2 (2002): 70-82.

Abstract An attempt is made to examine the main principles that underlay the ?Rhinean? school of parapsychology. Five such principles are discussed: (1) that psi can best be assessed using quantitative measures and forced?choice tests; (2) that psi is a function of the unconscious with the implication that objective performance alone is important, not the state of mind of the subject; (3) that psi ability is, to some degree, present in everyone; (4) that only those problems deserve attention for which (...) an unambiguous answer is attainable, with the implication that survival research should be shelved indefinitely and (5) that psi is non?physical. These five principles are then reviewed from our present vantage point and all are found to require qualification to a greater or lesser extent. Some critics, however, want to go further and allege that Rhine led psychical research into a blind alley and that the time has come to revert to the earlier investigative tradition. This, we contend, is defeatist, the more especially since the advent of computers has greatly extended the scope and power of the experimental approach. Despite its inherent difficulties, it remains the indispensable mainstay of our continuing efforts to make sense of the paranormal. (shrink)

In this paper I shall discuss the notion of argument, and the importanceof argument in AI and Law. I shall distinguish four areas where argument hasbeen applied: in modelling legal reasoning based on cases; in thepresentation and explanation of results from a rule based legal informationsystem; in the resolution of normative conflict and problems ofnon-monotonicity; and as a basis for dialogue games to support the modellingof the process of argument. The study of argument is held to offer prospectsof real progress (...) in the field of AI and law, and the purpose of this paperis to provide an overview of work, and the connection between the various strands. (shrink)

The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by (...) the data — how are we to restrict the space of inductive hypotheses and choose effectively some rules that will probably perform well on future examples? We analyze if and how this problem is approached in standard accounts of induction and show the difficulties that are present. Finally, we suggest that the explanation-based learning approach and related methods of knowledge intensive induction could be, if not a solution, at least a tool for solving some of these problems. (shrink)

This interdisciplinary collection of classical and contemporary readings provides a clear and comprehensive guide to the many hotly-debated philosophical issues at the heart of artificial intelligence.

John Searle has argued that the aim of strong AI of creating a thinking computer is misguided. Searle’s Chinese Room Argument purports to show that syntax does not suffice for semantics and that computer programs as such must fail to have intrinsic intentionality. But we are not mainly interested in the program itself but rather the implementation of the program in some material. It does not follow by necessity from the fact that computer programs are defined syntactically that the implementation (...) of them cannot suffice for semantics. Perhaps our world is a world in which any implementation of the right computer program will create a system with intrinsic intentionality, in which case Searle’s Chinese Room Scenario is empirically (nomically) impossible. But, indeed, perhaps our world is a world in which Searle’s Chinese Room Scenario is empirically (nomically) possible and that the silicon basis of modern day computers are one kind of material unsuited to give you intrinsic intentionality. The metaphysical question turns out to be a question of what kind of world we are in and I argue that in this respect we do not know our modal address. The Modal Address Argument does not ensure that strong AI will succeed, but it shows that Searle’s challenge on the research program of strong AI fails in its objectives. (shrink)

Computer simulations can be useful tools to support philosophers in validating their theories, especially when these theories concern phenomena showing nontrivial dynamics. Such theories are usually informal, whilst for computer simulation a formally described model is needed. In this paper, a methodology is proposed to gradually formalise philosophical theories in terms of logically formalised dynamic properties. One outcome of this process is an executable logic-based temporal specification, which within a dedicated software environment can be used as a simulation model to (...) perform simulations. This specification provides a logical formalisation at the lowest aggregation level of the basic mechanisms underlying a process. In addition, dynamic properties at a higher aggregation level that may emerge from the mechanisms specified by the lower level properties, can be specified. Software tools are available to support specification, and to automatically check such higher level properties against the lower level properties and against generated simulation traces. As an illustration, three case studies are discussed showing successful applications of the approach to formalise and analyse, among others, Clark’s theory on extended mind, Damasio’s theory on core consciousness, and Dennett’s perspective on intertemporal decision making and altruism. (shrink)

This article reports on a newly discovered bug in the original simulation argument. Two different ways of patching the argument are proposed, each of which preserves the original conclusion.

_This paper outlines the case for believing that we will have superhuman artificial intelligence_ _within the first third of the next century. It looks at different estimates of the processing power of_ _the human brain; how long it will take until computer hardware achieve a similar performance;_ _ways of creating the software through bottom-up approaches like the one used by biological_ _brains; how difficult it will be for neuroscience figure out enough about how brains work to_ _make this approach work; (...) and how fast we can expect superintelligence to be developed once_ _there is human-level artificial intelligence._. (shrink)

This paper proposes a model ofratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics ofratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model ofratio decidendi fails to (...) account for the dependency of precedential effect on the theory of decision. (shrink)

Though it''s difficult to agree on the exact date of their union, logic and artificial intelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI find themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer these questions (...) via a review of six books. Encapsulated, our answer is that (i) logic and AI, despite tabloidish reports to the contrary, still enjoy matrimonial bliss, and (ii) only their future robotic offspring (as opposed to the children of connectionist AI) will mark real progress in the attempt to understand cognition. (shrink)

At the MIT Arti cial Intelligence Laboratory we have been working on technologies for an Intelligent Room. Rather than pull people into the virtual world of the computer we are trying to pull the computer out into the real world of people. To do this we are combining robotics and vision technology with speech understanding systems, and agent based architectures to provide ready at hand computation and information services for people engaged in day to day activities, both on their own (...) and in conjunction with others. We have built a layered architecture where at the bottom level vision systems track people and identify their activities and gestures, and through word spotting decide whether people in the room are talking to each other or to the room itself. At the next level an agent architecture provides a uniform interface to such specially built systems, and to other o the shelf software, such as web browsers, etc. At the highest level we are able to build application systems that provide occupants of the room with specialized services; examples we have built include systems for command and control situations rooms and as a room for giving presentations. (shrink)

This important book, which results from a series of presentations at American Philosophical Association conferences, explores the major ways in which computers ...

Ape species-specific communication is grounded on the present, possesses some referential qualities and is mostly used to request objects or actions from others. Artificial systems of communication borrowed from humans transform apes' communicative exchanges by freeing them from the present (i.e. displaced reference) although requests still predominate as the main reason for communicating with others. Symbol use appears to enhance apes' relational abilities and their inhibitory control. Despite these substantial changes, it is concluded that even though artificial communication enhances thought (...) and enables its expression more openly, it does not create it or modify the motivation behind communicative exchanges. (shrink)

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can (...) be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

The ideas of continental philosopher Martin Heidegger have been influential in cognitive science and artificial intelligence, despite the fact that there has been no effort to analyze these ideas empirically. The experiments reported here are designed to lend empirical support to Heidegger’s phenomenology and more specifically his description of the transition between ready-to-hand and unready-to-hand modes in interactions with tools. In experiment 1, we found that a smoothly coping cognitive system exhibits 1/fβ type positively correlated noise and that its (...) correlated character is reduced when the system is perturbed. This indicates that the participant and tool constitute a self-assembled, extended device during smooth coping and this device is disrupted by the perturbation. In experiment 2, we examine the re-organization of awareness that occurs when a smoothly coping, self-assembled, extended cognitive system is perturbed. We found that the disruption is accompanied by a change in attention which interferes with participants’ performance on a simultaneous cognitive task. Together these experiments show that a smoothly coping participant-tool system can be temporarily disrupted and that this disruption causes a change in the participant’s awareness. Since these two events follow as predictions from Heidegger’s work, our study offers evidence for the hypothesized transition from readiness-to-hand to unreadiness-to-hand. (shrink)

In this chapter we consider unsupervised learning from two perspectives. First, we briefly look at its advantages and disadvantages as an engineering technique applied to large corpora in natural language processing. While supervised learning generally achieves greater accuracy with less data, unsupervised learning offers significant savings in the intensive labour required for annotating text. Second, we discuss the possible relevance of unsupervised learning to debates on the cognitive basis of human language acquisition. In this context we explore the implications of (...) recent work on grammar induction for poverty of stimulus arguments that purport to motivate a strong bias model of language learning, commonly formulated as a theory of Universal Grammar (UG). We examine the second issue both as a problem in computational learning theory, and with reference to empirical work on unsupervised Machine Learning (ML) of syntactic structure. We compare two models of learning theory and the place of unsupervised learning within each of them. Looking at recent work on part of speech tagging and the recognition of syntactic structure, we see how far unsupervised ML methods have come in acquiring different kinds of grammatical knowledge from raw text. (shrink)

wide variety of things. It covers the capacity to carry out deductive inferences, to make.

Computer science is an engineering science whose objective is to determine how to best control interactions among computational objects. We argue that it is a fundamental computer science value to design computational objects so that the dependencies required by their interactions do not result in couplings, since coupling inhibits change. The nature of knowledge in any science is revealed by how concepts in that science change through paradigm shifts, so we analyze classic paradigm shifts in both natural and computer science (...) in terms of decoupling. We show that decoupling pervades computer science both at its core and in the wider context of computing at large, and lies at the very heart of computer science’s value system. (shrink)

The aim of this project was to explore the possibility of constructing a psychologic inference engine that might enhance introspective self-awareness by delivering inferences about a user based on what he said in interactive dialogues about his closest opposite-sex relation. To implement this aim, we developed a computer program (guru) with the capacity to simulate human conversation in colloquial natural language. The psychologic inferences offered represent the authors' simulations of their commonsense psychology responses to expected user-input expressions. The heuristics of (...) the natural language processor and its relation to output responses are described in enough detail for the operations of the implementation to be understood. Evaluation of this new cognitive agent presents, we hope, puzzles for artificial intelligence and cognitive science. (shrink)

Applied Artificial Intelligence, 21, 2007, pp. 259-279.

Lecture Notes in Artificial Intelligence, vol. 4155, Springer, Berlin-Heidelberg, 2006, pp. 1-23. PDF.

In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. (...) The author, S. Bent Russell, was an engineer deeply influenced by the neurological hypotheses on nervous conduction of Herbert Spencer, Max Meyer and Edward L. Thorndike. The second is the project, published in 1929, of an electromechanical machine in which the author, the psychologist J.M. Stephens, believed it was possible to embody Thorndike's law of effect. Thus both Bent Russell and Stephens referred to the principles of learning that Thorndike defined as connectionist . Their attempt was that of simulating by machines at least certain simple aspects of inhibition, association and habit formation that are typical of living organisms. I propose to situate their projects within the frame of thediscovery of a simulative (modelling) methodology which I believe might be considered an important topic of the Culture of the Artificial . Certain more recent steps toward such a methodology made by both connectionism of the 1950s and present-day connectionism are briefly pointed out in the paper. (shrink)

In this article the question is raised whether artificial intelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificial intelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificial intelligence of the connectionist kind may have psychological relevance, not because the brain is a neural network, but because connectionist networks (...) exhibit operating characteristics which mimic operant behavior. Finally it is concluded that, since most of the work done so far in AI and Law is of the symbolic kind, it has as yet contributed little to our understanding of the legal mind. (shrink)

Cognitive science is the interdisciplinary study of minds and intelligent behavior in human beings and animals. The challenge of integrating a study of the mind with a scientific world view has only recently attracted sustained effort. In this course, we will examine the various scientific methodologies that have been brought to bear to uncover the nature of the mind. We will critically assess the data and theoretical results that define contemporary cognitive science. Beyond the results of experiments and the theories (...) that are based on them, cognitive science proposes that there is an underlying framework that unifies the diverse methodologies we appeal to. We seek to understand this foundation. (shrink)

Philosophy and AI presents invited contributions that focus on the different perspectives and techniques that philosophy and AI bring to the theory of ...

Shaun Gallagher and Dan Hutto claim that those once bitter rivals, simulation theory and theory-theory, are now to be treated as partners in crime. It's true that the debate has become more nuanced, with detailed suggestions abroad as to how these two approaches might peaceably divide the field. And there is common ground between them, at least to the extent that they agree on what needs to be explained. But I see no fatal flaw in what they share. In particular, (...) I reject the idea that most interpersonal understanding can be accounted for without the postulation of mechanisms for inferring beliefs and desires. I also query the claim that simulation mechanisms have a very limited explanatory scope, and argue for the existence of such mechanisms at sub-personal levels. I suggest that Gallagher and Hutto's strictures against false belief tests are unwarranted, and their conclusions about the role of narrative in interpersonal understanding are unfounded. (shrink)

The “Tower of London” puzzle was adapted to tablet PCs to be used as a clinical bedside test. “Iso-problems”, a specific class of problems, require identical moves but ball colours are permuted. Thus difficulty is the same even if the appearance is different. We wanted to determine the impact of these as yet little-studied tasks and hypothesised that there may be a learning effect specific to them (the “iso-effect”). We interspersed a set of six iso-problems within one selection of 22 (...) tasks and analysed problem solving by 81 healthy adults (mean age 41.6 years). Participants showed learning across iso-problems (less time, fewer moves, increasingly efficient solutions). This effect was distinct from general learning, as was obvious from comparison with a series of non-isomorphic tasks. However, participants seem not to be aware of solving such problems. This “iso-effect” may be related to implicit memory, a domain that so far has not been assessed using the Tower of London. (shrink)

I have been asked to discuss how computers have affected my work in philosophy. This paper discusses the use of artificial intelligence (AI) models to investigate both the representation of scientific knowledge and reasoning strategies for scientific change. The focus is on the reasoning strategies used to revise a theory, given an anomaly, which is a failed prediction of the theory.

This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on (...) the identical problem. An interpretation of the trained network revealed a perfect mapping from its internal structure to the decision tree, representing a precise translation of the classical theory to the connectionist model. In Study 3, a second network was trained on the mushroom problem without using extra output learning. An interpretation of this second network revealed a different algorithm for solving the mushroom problem, demonstrating that the Study 2 network was indeed a proper theory translation. (shrink)

I want to try to do something rather more speculative than the rest of you have done. I have been thinking recently about how one might explain some features of human reflective consciousness that seem to me to be very much in need of an explanation. I'm trying to see if these features could be understood as solutions to design problems, solutions arrived at by evolution, but also, in the individual, as a result of a process of unconscious self-design. I've (...) been trying to think of this in the context of work in AI on the attempt to design intelligent robots – not "bed-ridden" expert systems, but systems that have to act in real time in the real world. If you want to think about something like this, you have to stray fairly far from experiments and hard empirical data; you have to get fairly speculative. Nevertheless the design efforts of people in AI do seem to bring home to conviction – if not to prove – various design constraints looming large and inescapable. If we can come to see why a system – or an organ or a behavior-pattern – must have certain features or a certain structure in order to do its task, this may help us ask the right questions, or at least keep us from dwelling on some of the wrong questions when we try to explain the machinery in the brain that is responsible for intelligent action. (shrink)

In her presentation at the Monte Verità workshop, Maja Mataric showed us a videotape of her robots cruising together through the lab, and remarked, aptly: "They're flocking, but that's not what they think they're doing." This is a vivid instance of a phenomenon that lies at the heart of all the research I learned about at Monte Verità: the execution of surprisingly successful "cognitive" behaviors by systems that did not explicitly represent, and did not need to explicitly represent, what they (...) were doing. How "high" in the intuitive scale of cognitive sophistication can such unwitting prowess reach? All the way, apparently, since I want to echo Maja's observation with one of my own: "These roboticists are doing philosophy, but that's not what they think they're doing." It is possible, then, even to do philosophy--that most intellectual of activities--without realizing that that is what you are doing. It is even possible to do it well, for this is a good, new way of addressing antique philosophical puzzles. (shrink)

Cognitive science, as an interdisciplinary school of thought, may have recently moved beyond the bandwagon stage onto the throne of orthodoxy, but it does not make a favorable first impression on many people. Familiar reactions on first encounters range from revulsion to condescending dismissal--very few faces in the crowd light up with the sense of "Aha! So that's how the mind works! Of course!" Cognitive science leaves something out, it seems; moreover, what it apparently leaves out is important, even precious. (...) Boiled down to its essence, cognitive science proclaims that in one way or another our minds are computers, and this seems so mechanistic, reductionistic, intellectualistic, dry, philistine, unbiological. It leaves out emotion, or what philosophers call qualia, or value, or mattering, or . . . the soul. It doesn't explain what minds are so much as attempt to explain minds away. (shrink)

How is it possible for a physical thing--a person, an animal, a robot--to extract knowledge of the world from perception and then exploit that knowledge in the guidance of successful action? That is a question with which philosophers have grappled for generations, but it could also be taken to be one of the defining questions of Artificial Intelligence. AI is, in large measure, philosophy. It is often directly concerned with instantly recognizable philosophical questions: What is mind? What is meaning? What (...) is reasoning, and rationality? What are the necessary conditions for the recognition of objects in perception? How are decisions made and justified? (shrink)

This book brings together his essays on the philosphy of mind, artificial intelligence, and cognitive ethology that appeared in inaccessible journals from 1984...

Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive ...

Actual AI research began auspiciously around 1955 with Allen Newell and Herbert Simon's work at the RAND Corporation. Newell and Simon proved that computers could do more than calculate. They demonstrated that computers were physical symbol systems whose symbols could be made to stand for anything, including features of the real world, and whose programs could be used as rules for relating these features. In this way computers could be used to simulate certain important aspects intelligence. Thus the information-processing model (...) of the mind was born. But, looking back over these fifty years, it seems that theoretical AI with its promise of a robot like HAL appears to be a perfect example of what Imre Lakatos has called a "degenerating research program". (shrink)

The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a ...

We present a computational simulation which captures aspects of negotiation as the interaction of agents searching for an agreement over their own mental model. Specifically this simulation relates the beliefs of each agent about the action of cause and effect to the resulting negotiation dialogue. The model highlights the difference between negotiating to find any solution and negotiating to obtain the best solution from the point of view of each agent. The later case corresponds most closely to what is commonly (...) called “haggling”. This approach also highlights the importance of what each agent thinks is possible in terms of actions causing changes and in what the other agents are able to do in any situation. This simulation greatly extends other simulations of bargaining which usually only focus on the case of haggling over a limited number of numerical indexes. Three detailed examples are considered. The simulation framework is relatively well suited for participatory methods of elicitation since the “nodes and arrows” representation of beliefs is commonly used and thus to be accessible to stakeholders and domain experts. (shrink)

This book is an archetypal product of the Belief-Desire-Intention (BDI) school of multi-agent systems. It presents what is now the mainstream view as to the best way forward in the dream of engineering reliable software systems out of autonomous agents. The way of using formal logics to specify, implement and verify distributed systems of interacting units using a guiding analogy of beliefs, desires and intentions. The implicit message behind the book is this: Distributed Artificial Intelligence (DAI) can be a respectable (...) engineering science. It says: we use sound formal systems; can cite established philosophical foundations; and will be able to build reliable and flexible software systems. (shrink)

I claim that in order to pass the Turing Test over any period of extended time, it will necessary to embed the entity into society. This chapter discusses why this is, and how it might be brought about. I start by arguing that intelligence is better characterised by tests of social interaction, especially in open-ended and extended situations. I then argue that learning is an essential component of intelligence and hence that a universal intelligence is impossible. These two arguments support (...) the relevance of Turing Test as a particular but appropriate test of interactive intelligence. I look to the human case to argue that individual intelligence utilises society to a considerable extent for its development. Taking a lead from the human case I outline how a socially embedded artificial intelligence might be brought about in terms of four aspects: free-will, emotion, empathy and self-modelling. In each case I try to specify what social ‘hooks’ might be required in order for the full ability to develop during a considerable period of in situ acculturation. The chapter ends by speculating what it might be like to live with the result. (shrink)

The extent to which concepts, memory, and planning are necessary to the simulation of intelligent behavior is a fundamental philosophical issue in Artificial Intelligence. An active and productive segement of the AI community has taken the position that multiple low-level agents, properly organized, can account for high-level behavior. Empirical research on these questions with fully operational systems has been restricted to mobile robots that do simple tasks. This paper recounts experiments with Hoyle, a system in a cerebral, rather than a (...) physical, domain. The program learns to perform well and quickly, often outpacing its human creators at two-person, perfect information board games. Hoyle demonstrates that a surprising amount of intelligent behavior can be treated as if it were situation-determined, that often planning is unnecessary, and that the memory required to support this learning is minimal. Concepts, however, are crucial to this reactive program's ability to learn and perform. (shrink)

Creativity has a special role in enabling humans to develop beyond the fulfilment of simple primary functions. This factor is significant for Artificial Intelligence (AI) developers who take replication to be the primary goal, since moves toward creating autonomous artificial-beings beg questions about their potential for creativity. Using Wittgenstein’s remarks on rule-following and language-games, I argue that although some AI programs appear creative, to call these programmed acts creative in our terms is to misunderstand the use of this word in (...) language. I conclude that replication is not the best way forward for AI development in matters of creativity. (shrink)

1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..

The objective of the OSCAR Project is twofold. On the one hand, it is to construct a general theory of rational cognition. On the other hand, it is to construct an artificial rational agent (an "artilect") implementing that theory. This is a joint project in philosophy and AI.

Artificial Intelligence (AI) is a core area of Cognitive Science, yet today few AI researchers attend the Cognitive Science Society meetings. This essay examines why, how AI has changed over the last 30 years, and some emerging areas of potential interest where AI and the Society can go together in the next 30 years, if they choose.

William Rapaport, in How Helen Keller used syntactic semantics to escape from a Chinese Room, (Rapaport 2006), argues that Helen Keller was in a sort of Chinese Room, and that her subsequent development of natural language fluency illustrates the flaws in Searle’s famous Chinese Room Argument and provides a method for developing computers that have genuine semantics (and intentionality). I contend that his argument fails. In setting the problem, Rapaport uses his own preferred definitions of semantics and syntax, but he (...) does not translate Searle’s Chinese Room argument into that idiom before attacking it. Once the Chinese Room is translated into Rapaport’s idiom (in a manner that preserves the distinction between meaningful representations and uninterpreted symbols), I demonstrate how Rapaport’s argument fails to defeat the CRA. This failure brings a crucial element of the Chinese Room Argument to the fore: the person in the Chinese Room is prevented from connecting the Chinese symbols to his/her own meaningful experiences and memories. This issue must be addressed before any victory over the CRA is announced. (shrink)