Search results for 'Artificial intelligence' (try it on Scholar)

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  1. Joseph Y. Halpern, International Business Machines Corporation, American Association of Artificial Intelligence, United States & Association for Computing Machinery (1986). Theoretical Aspects of Reasoning About Knowledge Proceedings of the 1986 Conference, March 19-22, 1986, Monterey, California. [REVIEW] Monograph Collection (Matt - Pseudo).
     
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  2. Vincent C. Müller & Nick Bostrom (2016). Future Progress in Artificial Intelligence: A Survey of Expert Opinion. In Fundamental Issues of Artificial Intelligence. Springer
    There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bring- ing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they (...)
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  3.  67
    Vincent C. Müller (2014). Editorial: Risks of General Artificial Intelligence. Journal of Experimental and Theoretical Artificial Intelligence 26 (3):297-301.
    This is the editorial for a special volume of JETAI, featuring papers by Omohundro, Armstrong/Sotala/O’Heigeartaigh, T Goertzel, Brundage, Yampolskiy, B. Goertzel, Potapov/Rodinov, Kornai and Sandberg. - If the general intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity – so even if we estimate the probability of this event to be fairly low, it is necessary to think about it now. We need to estimate what progress (...)
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  4.  88
    James H. Fetzer (1990). Artificial Intelligence: Its Scope and Limits. Kluwer.
    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 ..
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  5.  48
    Hans F. M. Crombag (1993). On the Artificiality of Artificial Intelligence. Artificial Intelligence and Law 2 (1):39-49.
    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, (...)
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  6.  66
    Marcus Hutter (2012). One Decade of Universal Artificial Intelligence. In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer 67--88.
    The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid (...)
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  7.  65
    Vincent C. Müller (2011). Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011). The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
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  8.  22
    Douglas N. Walton (2008). Witness Testimony Evidence: Argumentation, Artificial Intelligence, and Law. Cambridge University Press.
    Recent work in artificial intelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificial intelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject (...)
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  9. Margaret A. Boden (ed.) (1990). The Philosophy of Artificial Intelligence. Oxford University Press.
    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.
     
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  10. John M. Preston & John Mark Bishop (eds.) (2002). Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press.
    The most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument.
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  11.  47
    Hutan Ashrafian (2015). AIonAI: A Humanitarian Law of Artificial Intelligence and Robotics. Science and Engineering Ethics 21 (1):29-40.
    The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised (...)
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  12. Viola Schiaffonati (2003). A Framework for the Foundation of the Philosophy of Artificial Intelligence. Minds and Machines 13 (4):537-552.
    The peculiarity of the relationship between philosophy and Artificial Intelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must (...)
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  13. Jack Copeland (1993). Artificial Intelligence: A Philosophical Introduction. Wiley-Blackwell.
    Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial (...) and neuroscience. John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)
     
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  14.  61
    Selmer Bringsjord (2010). Meeting Floridi's Challenge to Artificial Intelligence From the Knowledge-Game Test for Self-Consciousness. Metaphilosophy 41 (3):292-312.
    Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up (...)
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  15.  88
    David Marr (1977). Artificial Intelligence: A Personal View. Artificial Intelligence 9 (September):37-48.
  16.  33
    Gary L. Drescher (1991). Made-Up Minds: A Constructivist Approach to Artificial Intelligence. Cambridge: MIT Press.
    Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive ...
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  17. Murat Aydede & Guven Guzeldere (2000). Consciousness, Intentionality, and Intelligence: Some Foundational Issues for Artificial Intelligence. Journal of Experimental and Theoretical Artificial Intelligence 12 (3):263-277.
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  18. Donald Gillies (1996). Artificial Intelligence and Scientific Method. Oxford University Press.
    Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies shows how current views on scientific method are challenged by this recent research, and suggests a new framework for the study of logic. Finally, he draws on work by such seminal thinkers as Bacon, Gdel, Popper, Penrose, and Lucas, to address the hotly-contested question (...)
     
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  19.  90
    Vincent C. Müller (2012). Introduction: Philosophy and Theory of Artificial Intelligence. Minds and Machines 22 (2):67-69.
    The theory and philosophy of artificial intelligence has come to a crucial point where the agenda for the forthcoming years is in the air. This special volume of Minds and Machines presents leading invited papers from a conference on the “Philosophy and Theory of Artificial Intelligence” that was held in October 2011 in Thessaloniki. Artificial Intelligence is perhaps unique among engineering subjects in that it has raised very basic (...)
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  20.  67
    Vincent C. Müller (2016). Editorial: Risks of Artificial Intelligence. In Risks of artificial intelligence. CRC Press - Chapman & Hall 1-8.
    If the intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity. Time has come to consider these issues, and this consideration must include progress in AI as much as insights from the theory of AI. The papers in this volume try to make cautious headway in setting the problem, evaluating predictions on the future of AI, proposing ways to ensure that AI systems will be beneficial to humans – (...)
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  21. Selmer Bringsjord (2000). Animals, Zombanimals, and the Total Turing Test: The Essence of Artificial Intelligence. Journal of Logic Language and Information 9 (4):397-418.
    Alan Turing devised his famous test (TT) through a slight modificationof the parlor game in which a judge tries to ascertain the gender of twopeople who are only linguistically accessible. Stevan Harnad hasintroduced the Total TT, in which the judge can look at thecontestants in an attempt to determine which is a robot and which aperson. But what if we confront the judge with an animal, and arobot striving to pass for one, and then challenge him to peg which iswhich? (...)
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  22.  44
    Craig DeLancey (2001). Passionate Engines: What Emotions Reveal About the Mind and Artificial Intelligence. Oxford University Press.
    The emotions have been one of the most fertile areas of study in psychology, neuroscience, and other cognitive disciplines. Yet as influential as the work in those fields is, it has not yet made its way to the desks of philosophers who study the nature of mind. Passionate Engines unites the two for the first time, providing both a survey of what emotions can tell us about the mind, and an argument for how work in the cognitive disciplines can help (...)
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  23.  29
    Hutan Ashrafian (2015). Artificial Intelligence and Robot Responsibilities: Innovating Beyond Rights. Science and Engineering Ethics 21 (2):317-326.
    The enduring innovations in artificial intelligence and robotics offer the promised capacity of computer consciousness, sentience and rationality. The development of these advanced technologies have been considered to merit rights, however these can only be ascribed in the context of commensurate responsibilities and duties. This represents the discernable next-step for evolution in this field. Addressing these needs requires attention to the philosophical perspectives of moral responsibility for artificial intelligence and robotics. A contrast (...)
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  24.  15
    Carlotta Piscopo & Mauro Birattari (2008). The Metaphysical Character of the Criticisms Raised Against the Use of Probability for Dealing with Uncertainty in Artificial Intelligence. Minds and Machines 18 (2):273-288.
    In artificial intelligence (AI), a number of criticisms were raised against the use of probability for dealing with uncertainty. All these criticisms, except what in this article we call the non-adequacy claim, have been eventually confuted. The non-adequacy claim is an exception because, unlike the other criticisms, it is exquisitely philosophical and, possibly for this reason, it was not discussed in the technical literature. A lack of clarity and understanding of this claim had a major impact (...)
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  25. John Haugeland (1985). Artificial Intelligence: The Very Idea. Cambridge: MIT Press.
    The idea that human thinking and machine computing are "radically the same" provides the central theme for this marvelously lucid and witty book on...
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  26.  38
    Marco Ernandes (2005). Artificial Intelligence & Games: Should Computational Psychology Be Revalued? Topoi 24 (2):229-242.
    The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer (...)
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  27.  5
    Richard Ennals (1987). Socially Useful Artificial Intelligence. AI and Society 1 (1):5-15.
    Artificial intelligence is presented as a set of tools with which we can try to come to terms with human problems, and with the assistance of which, some human problems can be solved. Artificial intelligence is located in its social context, in terms of the environment within which it is developed, and the applications to which it is put. Drawing on social theory, there is consideration of the collaborative and social problem-solving processes (...)
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  28.  29
    Selmer Bringsjord & David A. Ferrucci (1998). Logic and Artificial Intelligence: Divorced, Still Married, Separated ...? [REVIEW] Minds and Machines 8 (2):273-308.
    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 (...)
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  29.  7
    Jay Liebowitz (1989). Artificial Intelligence: New Jobs From Old. AI and Society 3 (1):61-70.
    The age of artificial intelligence (AI) is upon us, and its effect upon society in the coming years will be noteworthy. Artificial intelligence is a field that encompasses such applications as robotics, expert systems, natural language understanding, speech recognition, and computer vision. The effect of these AI systems upon existing and future job occupations will be important. This paper takes a look at artificial intelligence in terms of the (...)
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  30.  7
    Daniel Memmi (1990). Connectionism and Artificial Intelligence as Cognitive Models. AI and Society 4 (2):115-136.
    The current renewal of connectionist techniques using networks of neuron-like units has started to have an influence on cognitive modelling. However, compared with classical artificial intelligence methods, the position of connectionism is still not clear. In this article artificial intelligence and connectionism are systematically compared as cognitive models so as to bring out the advantages and shortcomings of each. The problem of structured representations appears to be particularly important, suggesting likely research directions.
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  31.  18
    Tracy B. Henley (1990). Natural Problems and Artificial Intelligence. Behavior and Philosophy 18 (2):43-55.
    Artificial Intelligence has become big business in the military and in many industries. In spite of this growth there still remains no consensus about what AI really is. The major factor which seems to be responsible for this is the lack of agreement about the relationship between behavior and intelligence. In part certain ethical concerns generated from saying who, what and how intelligence is determined may be facilitating this lack of agreement.
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  32.  27
    Evan Selinger (2008). Collins's Incorrect Depiction of Dreyfus's Critique of Artificial Intelligence. Phenomenology and the Cognitive Sciences 7 (2):301-308.
    Harry Collins interprets Hubert Dreyfus’s philosophy of embodiment as a criticism of all possible forms of artificial intelligence. I argue that this characterization is inaccurate and predicated upon a misunderstanding of the relevance of phenomenology for empirical scientific research.
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  33.  15
    Frederick Kile (2013). Artificial Intelligence and Society: A Furtive Transformation. [REVIEW] AI and Society 28 (1):107-115.
    During the 1950s, there was a burst of enthusiasm about whether artificial intelligence might surpass human intelligence. Since then, technology has changed society so dramatically that the focus of study has shifted toward society’s ability to adapt to technological change. Technology and rapid communications weaken the capacity of society to integrate into the broader social structure those people who have had little or no access to education. (Most of the recent use of communications by (...)
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  34.  2
    Tim Smithers (1988). Product Creation: An Appropriate Coupling of Human and Artificial Intelligence. [REVIEW] AI and Society 2 (4):341-353.
    Small batch manufacture dominates the manufacturing sector of a growing number of industrialised countries. The organisational structures and management methods currently adopted in such enterprises are firmly based upon historical developments which started with individual craftsmen. These structures and methods are primarily concerned with the co-ordination of human activities, rather than with the management of theknowledge process underlying the creation of products.This paper argues that it is the failure to understand this knowledge process and its effective integration at aKnowledge Level (...)
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  35.  20
    Mariusz Flasiński (1997). "Every Man in His Notions" or Alchemists' Discussion on Artificial Intelligence. Foundations of Science 2 (1):107-121.
    A survey of the main approaches in a mind study -oriented part of Artificial Intelligence is made focusing on controversial issues and extreme hypotheses. Various meanings of terms: "intelligence" and "artificial intelligence" are discussed. Limitations for constructing intelligent systems resulting from the lack of formalized models of cognitive activity are shown. The approaches surveyed are then recapitulated in the light of these limitations.
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  36.  21
    Richard E. Korf (1995). Heuristic Evaluation Functions in Artificial Intelligence Search Algorithms. Minds and Machines 5 (4):489-498.
    We consider a special case of heuristics, namely numeric heuristic evaluation functions, and their use in artificial intelligence search algorithms. The problems they are applied to fall into three general classes: single-agent path-finding problems, two-player games, and constraint-satisfaction problems. In a single-agent path-finding problem, such as the Fifteen Puzzle or the travelling salesman problem, a single agent searches for a shortest path from an initial state to a goal state. Two-player games, such as chess and checkers, (...)
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  37.  21
    Barbara Warnick (2004). Rehabilitating AI: Argument Loci and the Case for Artificial Intelligence. [REVIEW] Argumentation 18 (2):149-170.
    This article examines argument structures and strategies in pro and con argumentation about the possibility of human-level artificial intelligence (AI) in the near term future. It examines renewed controversy about strong AI that originated in a prominent 1999 book and continued at major conferences and in periodicals, media commentary, and Web-based discussions through 2002. It will be argued that the book made use of implicit, anticipatory refutation to reverse prevailing value hierarchies related to AI. Drawing on (...)
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  38.  12
    Amir S. Tabandeh (1994). Characterising Artificial Intelligence Technology for International Transfer. AI and Society 8 (4):315-325.
    One of the central factors influencing the process and the outcome of technology transfer is the nature of the technology being transferred. This paper identifies and discusses the main characteristics of Artificial Intelligence (AI) technology from the point of view of international technology transfer. It attempts to indicate the peculiarities of AI in this context and move towards a framework to assist recipient decision makers in optimising the formulation of their policies on AI technology transfer.
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  39.  5
    Chris Moss (1989). Artificial Intelligence and Symbols. AI and Society 3 (4):345-356.
    The introduction of massive parallelism and the renewed interest in neural networks gives a new need to evaluate the relationship of symbolic processing and artificial intelligence. The physical symbol hypothesis has encountered many difficulties coping with human concepts and common sense. Expert systems are showing more promise for the early stages of learning than for real expertise. There is a need to evaluate more fully the inherent limitations of symbol systems and the potential for programming compared (...)
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  40.  3
    Achille Ardigo (1988). Artificial Intelligence: A Contribution to Systems Theories of Sociology. [REVIEW] AI and Society 2 (2):113-120.
    The aim of my contribution is to try to analyse some points of similarity and difference between post-Parsonian social systems theory models for sociology — with special reference to those of W. Buckley, F.E. Emery and N. Luhmann — and expert systems models1 from Artificial Intelligence. I keep specifically to post-Parsonian systems theories within sociology because they assume some postulates and criteria derived from cybernetics and which are at the roots of AI. I refer in particular (...)
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  41.  3
    Dr Richard Susskind (1989). Pragmatism and Purism in Artificial Intelligence and Legal Reasoning. AI and Society 3 (1):28-38.
    The paper identifies and assesses the implications of two approaches to the field of artificial intelligence and legal reasoning. The first — pragmatism — concentrates on the development of working systems to the exclusion of theoretical problems. The second — purism — focuses on the nature of the law and of intelligence with no regard for the delivery of commercially viable systems. Past work in AI and law is classified in terms of this division. (...)
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  42.  5
    Vincent Rialle (1995). Cognition and Decision in Biomedical Artificial Intelligence: From Symbolic Representation to Emergence. [REVIEW] AI and Society 9 (2-3):138-160.
    This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies (...)
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  43.  1
    Maria Miceli, Amedo Cesta & Paola Rizzo (1995). Distributed Artificial Intelligence From a Socio-Cognitive Standpoint: Looking at Reasons for Interaction. [REVIEW] AI and Society 9 (4):287-320.
    Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence (...)
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  44.  5
    Alejandro Garcia-Rivera (1993). Artificial Intelligence and de Las Casas: A 1492 Resonance. Zygon 28 (4):543-550.
    . A comparison is made between two unlikely debates over intelligence. One debate took place in 1550 at Valladolid, Spain, between Bartolomé de las Casas and Juan Gines de Sepúlveda over the intelligence of the Amerindian. The other debate is contemporary, between John Searle and various representatives of the “strong” artificial intelligence community over the adequacy of the Turing test for intelligence. Although the contemporary debate has yet to die down, (...)
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  45. David Godden (2006). The Impact of Argumentation on Artificial Intelligence. In F. H. van Eemeren, Peter Houtlosser & M. A. van Rees (eds.), Considering Pragma-Dialectics: A Festschrift for Frans H. L. Erlbaum Associates 287-299.
    In this chapter, we explore the development and importance of the connection between argumentation and artificial intelligence. Specifically, we show that the influence of argumentation on AI has occurred within a framework that is consistent with the basic approach of Pragma-Dialectics. While the pragma-dialectical approach is typically conceived of as applying primarily to argumentation occurring between human agents, we show that the basic features of this approach can consistently be applied in a virtual context, whereby the goal-directed activities (...)
     
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  46.  1
    Alberto Oliverio (1988). Biological and Artificial Intelligence. AI and Society 2 (2):152-161.
    The paper discusses the characteristics of Biological Intelligence (BI) and its differences with artificial intelligence. In particular the plasticity of the nervous system is considered in the different forms with special attention to deterministic and localizationist views of the brain vs holistic approaches. When memory and learning are considered the localizationist views do not offer a possible solution to a number of problems while memory may be better conceptualized in terms of categorization procedures and (...)
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  47. John Mark Bishop (2003). Dancing with Pixies: Strong Artificial Intelligence and Panpsychism. In John M. Preston & Michael A. Bishop (eds.), Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press
  48. John Bryant (1991). Systems Theory and Scientific Philosophy: An Application of the Cybernetics of W. Ross Ashby to Personal and Social Philosophy, the Philosophy of Mind, and the Problems of Artificial Intelligence. Upa.
    Systems Theory and Scientific Philosophy constitutes a totally new approach to philosophy, the philosophy of mind and the problems of artificial intelligence, and is based upon the pioneering work in cybernetics of W. Ross Ashby. While science is humanity's attempt to know how the world works and philosophy its attempt to know why, scientific philosophy is the application of scientific techniques to questions of philosophy.
     
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  49.  54
    Vincent C. Müller (ed.) (2016). Fundamental Issues of Artificial Intelligence. Springer.
    PT-AI 2013: This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. The key issues this volume investigates include the relation of (...)
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  50.  62
    Rajakishore Nath (2009). Philosophy of Artificial Intelligence: A Critique of the Mechanistic Theory of Mind. Universal Publishers.
    This book deals with the major philosophical issues in the theoretical framework of Artificial Intelligence (AI) in particular and cognitive science in general.
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