MindPapers: 6.2b. Computational Semantics

Akman, Varol (1998). Situations and artificial intelligence. Minds and Machines 8 (4):475-477. (Google | Edit)

Bogdan, Radu J. (1994). By way of means and ends. In Radu J. Bogdan (ed.), Grounds for Cognition. Lawrence Erlbaum. (Google | Edit)

Abstract: This chapter provides the teleological foundations for our analysis of guidance to goal. Its objective is to ground goal-directedness genetically. The basic suggestion is this. Organisms are small things, with few energy resources and puny physical means, battling a ruthless physical and biological nature. How do they manage to survive and multiply? CLEVERLY, BY ORGANIZING

Dennett, Daniel C. (2003). The Baldwin Effect: A Crane, Not a Skyhook. In Bruce H. Weber & D.J. Depew (eds.), And Learning: The Baldwin Effect Reconsidered. MIT Press. (Cited by 6 | Google | More links | Edit)

Abstract: In 1991, I included a brief discussion of the Baldwin effect in my account of the evolution of human consciousness, thinking I was introducing to non-specialist readers a little-appreciated, but no longer controversial, wrinkle in orthodox neo-Darwinism. I had thought that Hinton and Nowlan (1987) and Maynard Smith (1987) had shown clearly and succinctly how and why it worked, and restored the neglected concept to grace. Here is how I put it then

Fodor, Jerry A. (1979). In reply to Philip Johnson-Laird's What's Wrong with Grandma's Guide to Procedural Semantics: A Reply to Jerry Fodor. Cognition 7 (March):93-95. (Google | Edit)

Fodor, Jerry A. (1978). Tom swift and his procedural grandmother. Cognition 6 (September):229-47. (Cited by 24 | Annotation | Google | Edit)

Hadley, Robert F. (1990). Truth conditions and procedural semantics. In Philip P. Hanson (ed.), Information, Language and Cognition. University of British Columbia Press. (Cited by 2 | Google | Edit)

Harnad, Stevan (2002). Darwin, Skinner, Turing and the mind. Magyar Pszichologiai Szemle 57 (4):521-528. (Google | More links | Edit)

Abstract: Darwin differs from Newton and Einstein in that his ideas do not require a complicated or deep mind to understand them, and perhaps did not even require such a mind in order to generate them in the first place. It can be explained to any school-child (as Newtonian mechanics and Einsteinian relativity cannot) that living creatures are just Darwinian survival/reproduction machines. They have whatever structure they have through a combination of chance and its consequences: Chance causes changes in the genetic blueprint from which organisms' bodies are built, and if those changes are more successful in helping their owners survive and reproduce than their predecessors or their rivals, then, by definition, those changes are reproduced, and thereby become more prevalent in succeeding generations: Whatever survives/reproduces better survives/reproduces better. That is the tautological force that shaped us

Johnson-Laird, Philip N. (1977). Procedural semantics. Cognition 5:189-214. (Cited by 37 | Google | Edit)

Johnson-Laird, Philip N. (1978). What's wrong with grandma's guide to procedural semantics: A reply to Jerry Fodor. Cognition 9 (September):249-61. (Cited by 1 | Google | Edit)

McDermott, Drew (1978). Tarskian semantics, or no notation without denotation. Cognitive Science 2:277-82. (Cited by 33 | Annotation | Google | More links | Edit)

Papineau, David (2006). The cultural origins of cognitive adaptations. Royal Institute of Philosophy Supplement. (Google | More links | Edit)

Abstract: According to an influential view in contemporary cognitive science, many human cognitive capacities are innate. The primary support for this view comes from âpoverty of stimulusâ arguments. In general outline, such arguments contrast the meagre informational input to cognitive development with its rich informational output. Consider the ease with which humans acquire languages, become facile at attributing psychological states (âfolk psychologyâ), gain knowledge of biological kinds (âfolk biologyâ), or come to understand basic physical processes (âfolk physicsâ). In all these cases, the evidence available to a growing child is far too thin and noisy for it to be plausible that the underlying principles involved are derived from general learning mechanisms. This only alternative hypothesis seems to be that the childâs grasp of these principles is innate. (Cf. Laurence and Margolis, 2001.)

Perlis, Donald R. (1991). Putting one's foot in one's head -- part 1: Why. Noûs 25 (September):435-55. (Cited by 12 | Google | More links | Edit)

Perlis, Donald R. (1994). Putting one's foot in one's head -- part 2: How. In Eric Dietrich (ed.), Thinking Computers and Virtual Persons. Academic Press. (Google | Edit)

Rapaport, William J. (1988). Syntactic semantics: Foundations of computational natural language understanding. In James H. Fetzer (ed.), Aspects of AI. Kluwer. (Cited by 44 | Google | Edit)

Rapaport, William J. (1995). Understanding understanding: Syntactic semantics and computational cognition. Philosophical Perspectives 9:49-88. (Cited by 22 | Google | More links | Edit)

Smith, B. (1988). On the semantics of clocks. In James H. Fetzer (ed.), Aspects of AI. Kluwer. (Cited by 7 | Google | Edit)

Smith, B. (1987). The correspondence continuum. Csli 87. (Cited by 34 | Google | Edit)

Tin, Erkan & Akman, Varol (1994). Computational situation theory. ACM SIGART Bulletin 5 (4):4-17. (Cited by 15 | Google | More links | Edit)

Abstract: Situation theory has been developed over the last decade and various versions of the theory have been applied to a number of linguistic issues. However, not much work has been done in regard to its computational aspects. In this paper, we review the existing approaches towards `computational situation theory' with considerable emphasis on our own research

Wilks, Y. (1990). Form and content in semantics. Synthese 82 (3):329-51. (Cited by 10 | Annotation | Google | More links | Edit)

Abstract: This paper continues a strain of intellectual complaint against the presumptions of certain kinds of formal semantics (the qualification is important) and their bad effects on those areas of artificial intelligence concerned with machine understanding of human language. After some discussion of the use of the term epistemology in artificial intelligence, the paper takes as a case study the various positions held by McDermott on these issues and concludes, reluctantly, that, although he has reversed himself on the issue, there was no time at which he was right

Wilks, Y. (1982). Some thoughts on procedural semantics. In W. Lehnert (ed.), Strategies for Natural Language Processing. Lawrence Erlbaum. (Cited by 12 | Google | Edit)

Winograd, Terry (1985). Moving the semantic fulcrum. Linguistics and Philosophy 8 (February):91-104. (Cited by 16 | Google | More links | Edit)

Woods, W. (1986). Problems in procedural semantics. In Zenon W. Pylyshyn & W. Demopolous (eds.), Meaning and Cognitive Structure. Ablex. (Cited by 2 | Annotation | Google | Edit)

Woods, W. (1981). Procedural semantics as a theory of meaning. In A. Joshi, Bruce H. Weber & Ivan A. Sag (eds.), Elements of Discourse Understanding. Cambridge University Press. (Cited by 33 | Google | Edit)

6.2b	Computational Semantics [23]

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