Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

John Searle distinguished between weak and strong artificial intelligence (AI). This essay discusses a third alternative, mild AI, according to which a machine may be capable of possessing a species of mentality. Using James Fetzer's conception of minds as semiotic systems, the possibility of what might be called ``mild AI'' receives consideration. Fetzer argues against strong AI by contending that digital machines lack the ground relationship required of semiotic systems. In this essay, the implementational nature of semiotic processes posited by (...) Charles S. Peirce's triadic sign relation is re-examined in terms of the underlying dispositional processes and the ontological levels they would span in an inanimate machine. This suggests that, if non-human mentality can be replicated rather than merely simulated in a digital machine, the direction to pursue appears to be that of mild AI. (shrink)

We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castañeda, namely, that the simple rule -/- `(A knows that P) implies P' -/- apparently does not hold if P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore the difference between reasoning (...) in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (for example, written narrative) in order to fully capture certain features of their semantics. (shrink)

Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have been successfully distinguished (...) from other kinds of things, mental states will not just be the implementations of the right symbol systems, because of the symbol grounding problem: The interpretation of a symbol system is not intrinsic to the system; it is projected onto it by the interpreter. This is not true of our thoughts. We must accordingly be more than just computers. My guess is that the meanings of our symbols are grounded in the substrate of our robotic capacity to interact with that real world of objects, events and states of affairs that our symbols are systematically interpretable as being about. (shrink)

Cognitive science has been dominated by the computational conception that cognition is computation across representations. To the extent to which cognition as computation across representations is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from their interpretation by human users. Strictly speaking, computers as such — apart (...) from human users — are not only incapable of cognition, but even incapable of computation, properly construed. If we want to understand the nature of thought, then we have to study thinking, not computing, because they are not the same thing. (shrink)

It is argued in this paper that robots are just quasi-autonomous beings, which must be understood, within an emergent systems view, as intrinsically linked to and presupposing human beings as societal creatures within a technologically mediated world. Biosemiotics is introduced as a perspective on living systems that is based upon contemporary biology but reinterpreted through a qualitative organicist tradition in biology. This allows for emphasizing the differences between an organism as a general semiotic system with vegetative and self-reproductive capacities, an (...) animal body also with sentience and phenomenal states, and higher forms of anthroposemiotic systems such as humans, machines and robots. On all three levels, representations are crucial processes. The “representationalism” invoked by critiques of cognitive science and robotics tends to focus only on simplistic notions of representation, and must be distinguished from a Peircean or biosemiotic notion of representation. Implications for theorizing about the physical, biological, animate, phenomenal and social body and their forms of autonomy are discussed. (shrink)

It is argued in this paper that robots are just quasi-autonomous beings, which must be understood, within an emergent systems view, as intrinsically linked to and presupposing human beings as societal creatures within a technologically mediated world. Biosemiotics is introduced as a perspective on living systems that is based upon contemporary biology but reinterpreted through a qualitative organicist tradition in biology. This allows for emphasizing the differences between an organism as a general semiotic system with vegetative and self-reproductive capacities, an (...) animal body also with sentience and phenomenal states, and higher forms of anthroposemiotic systems such as humans, machines and robots. On all three levels, representations are crucial processes. The “representationalism“ invoked by critiques of cognitive science and robotics tends to focus only on simplistic notions of representation, and must be distinguished from a Peircean or biosemiotic notion of representation. Implications for theorizing about the physical, biological, animate, phenomenal and social body and their forms of autonomy are discussed. (shrink)

Charles Sanders Peirce has been characterized as the greatest American philosophic genius. He is the creator of pragmatism and one of the founders of modern logic. James, Royce, Schroder, and Dewey have acknowledged their great indebtedness to him. A laboratory scientist, he made notable contributions to geodesy, astronomy, psychology, induction, probability, and scientific method. He introduced into modern philosophy the doctrine of scholastic realism, developed the concepts of chance, continuity, and objective law, and showed the philosophical significance of the theory (...) of signs and mathematical logic. The present series is the first published edition of his systematic works. (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 ..

Cognitive science has been dominated by the computational conception that cogniton is computation across representations. To the extent to which cognition is supposed to be a purposive, meaningful, algorithmic, problem-solving activity, however, computers appear to be incapable of cognition. They are devices that can facilitate computations on the basis of semantic grounding relations as special kinds of signs. Even their algorithmic, problem-solving character arises from ther interpretation by human users. Strictly speaking, computers as such–apart from human users–are not only incapable (...) of cognition but even incapable of computation, properly construed. If we want to understand the nature of thought, we are going to have to study thinking, not computing, because they are not the same thing. (shrink)