A sequel to the author's How to Build a Person, this work builds upon that theoretical groundwork for the implementation of rationality through artificial intelligence. It argues that progress in AI has stalled because of its creators' reliance upon unformulated intuitions about rationality. Instead, the author bases the OSCAR architecture upon an explicit philosophical theory of rationality, encompassing principles of practical cognition, epistemic cognition and defeasible reasoning. One of the results is the first automated defeasible reasoner capable of reasoning in a rich, logical environment.
Recent work in cognitive science, much of it placed in opposition to a computational view of the mind, has argued that the concept of representation and theories based on that concept are not sufficient to explain the details of cognitive processing. These attacks on representation have focused on the importance of context sensitivity in cognitive processing, on the range of individual differences in performance, and on the relationship between minds and the bodies and environments in which they exist. In each case, models based on traditional assumptions about representation have been assumed to be too rigid to account for the effects of these factors on cognitive processing. In place of a representational view of mind, other formalisms and methodologies, such as nonlinear differential equations (or dynamical systems) and situated robotics, have been proposed as better explanatory tools for understanding cognition. This book is based on the notion that, while new tools and approaches for understanding cognition are valuable, representational approaches do not need to be abandoned in the course of constructing new models and explanations. Rather, models that incorporate representation are quite compatible with the kinds of complex situations being modeled with the new methods. This volume illustrates the power of this explicitly representational approach--labeled "cognitive dynamics"--in original essays by prominent researchers in cognitive science. Each chapter explores some aspect of the dynamics of cognitive processing while still retaining representations as the centerpiece of the explanations of the key phenomena. These chapters serve as an existence proof that representation is not incompatible with the dynamics of cognitive processing. The book is divided into sections on foundational issues about the use of representation in cognitive science, the dynamics of low level cognitive processes (such as visual and auditory perception and simple lexical priming), and the dynamics of higher cognitive processes (including categorization, analogy, and decision making).
This book combines virtue reliabilism with knowledge first epistemology to develop novel accounts of knowledge and justified belief. It is virtue reliabilist in that knowledge and justified belief are accounted for in terms of epistemic ability. It is knowledge first epistemological in that, unlike traditional virtue reliabilism, it does not unpack the notion of epistemic ability as an ability to form true beliefs but as an ability to know, thus offering a definition of justified belief in terms of knowledge. In addition, the book aims to show that this version of knowledge first virtue reliabilism serves to provide novel solutions to a number of core epistemological problems and, as a result, compares favourably with alternative versions of virtue reliabilism both in the traditionalist and in the knowledge first camp. This is the first ever book-length development of knowledge first virtue reliabilism, and it will contribute to recent debates in these two growing areas of epistemology.
Argumentation, which has long been a topic of study in philosophy, has become a well-established aspect of computing science in the last 20 years. This book presents the proceedings of the fifth conference on Computational Models of Argument (COMMA), held in Pitlochry, Scotland in September 2014. Work on argumentation is broad, but the COMMA community is distinguished by virtue of its focus on the computational and mathematical aspects of the subject. This focus aims to ensure that methods are sound – that they identify arguments that are correct in some sense – and provide an unambiguous specification for implementation; producing programs that reason in the correct way and building systems capable of natural argument or of recognizing argument. The book contains 24 long papers and 18 short papers, and the 21 demonstrations presented at the conference are represented in the proceedings either by an extended abstract or by association with another paper. The book will be of interest to all those whose work involves argumentation as it relates to artificial intelligence.
The idea that we might be robots is no longer the stuff of science fiction; decades of research in evolutionary biology and cognitive science have led many esteemed scientists to the conclusion that, according to the precepts of universal Darwinism, humans are merely the hosts for two replicators (genes and memes) that have no interest in us except as conduits for replication. Richard Dawkins, for example, jolted us into realizing that we are just survival mechanisms for our own genes, sophisticated robots in service of huge colonies of replicators to whom concepts of rationality, intelligence, agency, and even the human soul are irrelevant. Accepting and now forcefully responding to this decentering and disturbing idea, Keith Stanovich here provides the tools for the "robot's rebellion," a program of cognitive reform necessary to advance human interests over the limited interest of the replicators and define our own autonomous goals as individual human beings. He shows how concepts of rational thinking from cognitive science interact with the logic of evolution to create opportunities for humans to structure their behavior to serve their own ends. These evaluative activities of the brain, he argues, fulfill the need that we have to ascribe significance to human life. We may well be robots, but we are the only robots who have discovered that fact. Only by recognizing ourselves as such, argues Stanovich, can we begin to construct a concept of self based on what is truly singular about humans: that they gain control of their lives in a way unique among life forms on Earth—through rational self-determination.
Argumentation is all around us. Letters to the Editor often make points of cons- tency, and “Why” is one of the most frequent questions in language, asking for r- sons behind behaviour. And argumentation is more than ‘reasoning’ in the recesses of single minds, since it crucially involves interaction. It cements the coordinated social behaviour that has allowed us, in small bands of not particularly physically impressive primates, to dominate the planet, from the mammoth hunt all the way up to organized science. This volume puts argumentation on the map in the eld of Arti cial Intelligence. This theme has been coming for a while, and some famous pioneers are chapter authors, but we can now see a broader systematic area emerging in the sum of topics and results. As a logician, I nd this intriguing, since I see AI as ‘logic continued by other means’, reminding us of broader views of what my discipline is about. Logic arose originally out of re ection on many-agent practices of disputation, in Greek Ant- uity, but also in India and China. And logicians like me would like to return to this broader agenda of rational agency and intelligent interaction. Of course, Aristotle also gave us a formal systems methodology that deeply in uenced the eld, and eventually connected up happily with mathematical proof and foundations.
Since the 1970s the cognitive sciences have offered multidisciplinary ways of understanding the mind and cognition. The MIT Encyclopedia of the Cognitive Sciences (MITECS) is a landmark, comprehensive reference work that represents the methodological and theoretical diversity of this changing field. At the core of the encyclopedia are 471 concise entries, from Acquisition and Adaptationism to Wundt and X-bar Theory. Each article, written by a leading researcher in the field, provides an accessible introduction to an important concept in the cognitive sciences, as well as references or further readings. Six extended essays, which collectively serve as a roadmap to the articles, provide overviews of each of six major areas of cognitive science: Philosophy; Psychology; Neurosciences; Computational Intelligence; Linguistics and Language; and Culture, Cognition, and Evolution. For both students and researchers, MITECS will be an indispensable guide to the current state of the cognitive sciences.
This volume contains newly-commissioned articles covering the development of modern logic from the late medieval period (fourteenth century) through the end of the twentieth-century. It is the first volume to discuss the field with this breadth of coverage and depth. It will appeal to scholars and students of philosophical logic and the philosophy of logic.
Twelve leading philosophers explore and apply a particular methodology in epistemology, which might be called purposeful epistemology. The idea is that considerations about the point and purpose of our concepts (or epistemic norms) promise to yield important insights for epistemological theorizing.
In recent years, there has been a growing interest in the need for designing intelligent systems to address complex decision systems. One of the most challenging issues for the intelligent system is to effectively handle real-world uncertainties that cannot be eliminated. These uncertainties include various types of information that are incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, deficient, and overloading. The uncertainties result in a lack of the full and precise knowledge of the decision system, including the determining and selection of evaluation criteria, alternatives, weights, assignment scores, and the final integrated decision result. Computational intelligent techniques (including fuzzy logic, neural networks, and genetic algorithms etc.), which are complimentary to the existing traditional techniques, have shown great potential to solve these demanding, real-world decision problems that exist in uncertain and unpredictable environments. These technologies have formed the foundation for intelligent systems.
