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  • Cited by 12
  • Print publication year: 2008
  • Online publication date: June 2012

5 - Declarative/Logic-Based Cognitive Modeling

from Part II - Cognitive Modeling Paradigms

Summary

This chapter provides an overview of the book on Introduction to Computational Cognitive Modeling. The first part of the book provides a general introduction to the field of computational cognitive modeling. The second part, Cognitive Modeling Paradigms, introduces the reader to broadly influential approaches in cognitive modeling. The third part, Computational Modeling of Various Cognitive Functionalities and Domains, describes a range of computational modeling efforts that researchers in this field have undertaken regarding major cognitive functionalities and domains. This part surveys and explains computational modeling research, in terms of detailed computational mechanisms and processes, on memory, concepts, learning, reasoning, decision making, skills, vision, motor control, language, development, scientific explanation, social interaction, and so on. The final part, Concluding Remarks, explores a range of issues associated with computational cognitive modeling and cognitive architectures, and provides some perspectives, evaluations, and assessments.

References

Amir, E., & Maynard-Reid, P. (1999). Logic-based subsumption architecture. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence(pp. 147–152). San Francisco, CA: Morgan Kaufmann.
Amir, E., & Maynard-Reid, P. (2000). Logic-based subsumption architecture: Empirical evaluation. In Proceedings of the AAAI Fall Symposium on Parallel Architectures for Cognition.
Amir, E., & Maynard-Reid, P. (2001). LiSA: A robot driven by logical subsumption. In Proceedings of the Fifth Symposium on the Logical Formalization of Commonsense Reasoning. http://cs.nyu.edu/faculty/davise/commonsense01.
Anderson, J., & Lebiere, C. (2003). The newell test for a theory of cognition. Behavioral and Brain Sciences, 26, 587–640.
Anderson, J. R. (1993). Rules of mind. Hillsdale, NJ: Lawrence Erlbaum.
Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum.
Arkoudas, K. (2000). Denotational proof languages. PhD thesis, MIT.
Arkoudas, K., & Bringsjord, S. (2005). Metareasoning for multi-agent epistemic logics. In Fifth International Conference on Computational Logic In Multi-Agent Systems(CLIMA 2004), (Vol. 3487 (pp. 111–125). New York: Springer-Verlag.
Ashcraft, M. (1994). Human memory and cognition. New York: HarperCollins.
Barwise, J., & Etchemendy, J. (1994). Hyperproof. Stanford, CA: CSLI.
Barwise, J., & Etchemendy, J. (1999). Language, proof, and logic. New York: Seven Bridges.
Boolos, G. S., & Jeffrey, R. C. (1989). Computability and logic. Cambridge, UK: Cambridge University Press.
Bourbaki, N. (2004). Elements of mathematics: theory of sets. New York: Verlag. (Original work published 1939.)
Brachman, R. J., & Levesque, H. J. (2004). Knowledge representation and reasoning. San Francisco, CA: Morgan Kaufmann/Elsevier.
Braine, M. (1998a). How to investigate mental logic and the syntax of thought. In M. Braine & P. O’Brien (eds.), Mental logic(pp. 45–61). Mahwah, NJ: Lawrence Erlbaum.
Braine, M. (1998b). Steps toward a mental predicate-logic. In M. Braine & D. O’Brien (Eds.), Mental logic(pp. 273–331). Mahwah, NJ: Lawrence Erlbaum Associates.
Braine, M. D. S. (1990). On the relation between the natural logic of reasoning and standard logic. Psychological Review, 85, 1–21.
Bringsjord, S. (1995). In defense of impenetrable zombies. Journal of Consciousness Studies, 2(4), 348–351.
Bringsjord, S. (1997). Abortion: A dialogue. Indianapolis, IN: Hackett.
Bringsjord, S. (1998). Chess is too easy. Technology Review, 101(2), 23–28.
Bringsjord, S. (1999). The zombie attack on the computational conception of mind. Philosophy and Phenomenological Research, 59.1, 41– 69.
Bringsjord, S. (2000). A contrarian future for minds and machines. Chronicle of Higher Educationp. B5. Reprinted in The Education Digest, 66.6: 31–33.
Bringsjord, S. (2001). Is it possible to build dramatically compelling interactive digital entertainment (in the form, e.g., of computer games)? Game Studies 1(1). http://www.gamestudies.org.
Bringsjord, S. (in press). Artificial intelligence. In E. Zalta (ed.), The stanford encyclopedia of philosophy. Palo Alto, CA: CLSI. http://plato.stanford.edu.
Bringsjord, S., Arkoudas, K., & Bello, P. (2006). Toward a general logicist methodology for engineering ethically correct robots. IEEE Intelligent Systems 21(4), 38–44.
Bringsjord, S., Arkoudas, K., Clark, M., Shilliday, A., Taylor, J., Schimanski, B., et al. (2007). Reporting on some logic-based machine reading research. In O. Etzioni (Ed.), Proceedings of the 2007 AAAI Spring Symposium: Machine Reading(SS–07–06) (pp. 23–28). Menlo Park, CA: AAAI Press.
Bringsjord, S., Bringsjord, E., & Noel, R. (1998). In defense of logical minds. In Proceedings of the 20th Annual Conference of the Cognitive Science Society(pp. 173–178). Mahwah, NJ: Lawrence Erlbaum.
Bringsjord, S., & Ferrucci, D. (1998a). Logic and artificial intelligence: Divorced, still married, separated. . . ? Minds and Machines, 8, 273– 308.
Bringsjord, S., & Ferrucci, D. (1998b). Reply to Thayse and Glymour on logic and artificial intelligence. Minds and Machines, 8, 313–315.
Bringsjord, S., Khemlani, S., Arkoudas, K., McEvoy, C., Destefano, M., & Daigle, M. (2005). Advanced synthetic characters, evil, and E. In M. Al-Akaidi & A. E. Rhalibi (Eds.), 6th International Conference on Intelligent Games and Simulation(pp. 31–39). Ghent-Zwijnaarde, Belgium: European Simulation Society.
Bringsjord, S., & Zenzen, M. (1991). In defense of hyper-logicist AI. In IJCAI 91(pp. 1066– 1072). Mountain View, CA: Morgan Kaufman.
Brooks, R. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.
Brooks, R. A., Breazeal, C., Marjanovic, M., Scassellati, B., & Williamson, M. M. (1999). The cog project: Building a humanoid robot Lecture Notes in Computer Science, 1562, 52– 87.
Bucciarelli, M., & Johnson-Laird, P. (1999). Strategies in syllogistic reasoning. Cognitive Science, 23, 247–303.
Bumby, D., Klutch, R., Collins, D., & Egbers, E. (1995). Integrated mathematics course 1. New York: Glencoe/McGraw-Hill.
Cassimatis, N. (2002). A Polyscheme: A cognitive architecture for integrating multiple representation and inference schemes, Unpublished doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA.
Cassimatis, N. (2006). Cognitive substrate for human-level intelligence. AI Magazine, 27(2), 71–82.
Cassimatis, N., Trafton, J., Schultz, A., & Bugajska, M. (2004). Integrating cognition, perception and action through mental simulation in robots. In C. Schilenoff & M. Uschold (Eds.), Proceedings of the 2004 AAAI Spring Symposium on Knowledge Representation and Ontology for Autonomous Systems(pp. 1–8). Menlo Park, CA: AAAI.
Charniak, E. (1993). Statistical language learning. Cambridge, MA: MIT Press.
Charniak, E., & McDermott, D. (1985). Introduction to artificial intelligence. Reading, MA: Addison-Wesley.
Chisholm, R. (1966). Theory of knowledge. Engle-wood Cliffs, NJ: Prentice Hall.
Chisholm, R. (1977). Theory of knowledge (2nd ed.). Englewood Cliffs, NJ: Prentice Hall.
Chisholm, R. (1978). Is there a mind-body problem? Philosophic Exchange, 2, 25–32.
Chisholm, R. (1987). Theory of knowledge(3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
Claessen, K., & Sorensson, N. (2003). New techniques that improve Mace-style model finding. In Model computation: Principles, algorithms, applications(Cade-19 Workshop). Miami, Florida.
Collins, A. (1978). Fragments of a theory of human plausible reasoning. In D. Waltz (Ed.), Theoretical issues in natural language processing II(pp. 194–201). Urbana: University of Illinois Press.
Collins, A., & Michalski, R. (1989). The logic of plausible reasoning: A core theory. Cognitive Science, 82, 1–49.
Dennett, D. (1978). Conditions of personhood. In Brainstorms: Philosophical essays on mind and psychology. (pp. 267–285). Montgomery, VT: Bradford Books.
Dickmann, M. A. (1975). Large infinitary languages. The Netherlands: North-Holland, Amsterdam.
Ebbinghaus, H. D., Flum, J., & Thomas, W. (1984). Mathematical logic. New York: Springer-Verlag.
Ebbinghaus, H. D., Flum, J., & Thomas, W. (1994). Mathematical logic(2nd ed.) New York: Springer-Verlag.
Eisenstadt, S., & Simon, H. (1997). Logic and thought: Minds and Machines, 7(3), 365–385.
Ericsson, K. A., & Simon, H. (1984). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.
Fagin, R., Halpern, J., Moses, Y., & Vardi, M. (2004). Reasoning about knowledge. Cambridge, MA: MIT Press.
Friedland, N., Allen, P., Matthews, G., Witbrock, M., Baxter, D., Curtis, J., et al. (2004). Project halo: Towards a digital aristotle. AI Magazine, 25(4).
Fuchs, N. E., Schwertel, U., & Schwitter, R. (1999). Attempto controlled English(ACE) language manual(Version 3.0, Technical Report 99.03). Zurich, Switzerland: Department of Computer Science, University of Zurich.
Gabbay, D. (Ed.). (1994). What is a logical system? Oxford, UK: Clarendon Press.
Genesereth, M., & Nilsson, N. (1987). Logical foundations of artificial intelligence. Los Altos, CA: Morgan Kaufmann.
Glymour, C. (1992). Thinking things through. Cambridge, MA: MIT Press.
Goble, L. (Ed.). (2001). The Blackwell guide to philosophical logic. Oxford, UK: Blackwell Publishers.
Goldstein, E. B. (2005). Cognitive psychology: Connecting mind, research, and everyday experience. Belmont, CA: Wadsworth.
Halpern, J., Harper, R., Immerman, N., Kolaitis, P., Vardi, M., & Vianu, V. (2001). On the unusual effectiveness of logic in computer science. The Bulletin of Symbolic Logic, 7(2), 213– 236.
Hamming, R. (1980). The unreasonable effectiveness of mathematics. The American Mathematical Monthly, 87, 81–90.
Hintikka, J. (1962). Knowledge and belief: An introduction to the logic of the two notions. Ithaca, NY: Cornell University Press.
Inhelder, B., & Piaget, J. (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books.
Johnson-Laird, P. (1997). Rules and illusions: A criticial study of Rips’s The psychology of proof. Minds and machines, 7(3), 387–407.
Johnson-Laird, P. N. (1983). Mental models. Cambridge, MA: Harvard University Press.
Johnson-Laird, P. N., Legrenzi, P., Girotto, V., & Legrenzi, M. S. (2000). Illusions in reasoning about consistency. Science, 288, 531–532.
Johnson-Laird, P., & Savary, F. (1995). How to make the impossible seem probable. In M. Gaskell & W. Marslen-Wilson (Eds.), Proceedings of the 17th Annual Conference of the Cognitive Science Society(pp. 381–384). Hillsdale, NJ: Lawrence Erlbaum.
Kahneman, D., & Tversky, A. (Eds.). (2000). Choices, values, and frames. Cambridge, UK: Cambridge University Press.
Kautz, H., & Selman B. (1999). Unifying SAT-based and graph-based planning. In J. Minker, (Ed.), Workshop on Logic-Based Artificial Intelligence, Washington, DC, June 14–16, 1999, Computer Science Department, University of Maryland. http://citeseer.ist.psu.edu/kautz99unifying.html
Langley, P., McKusick, K. B., Allen, J. A., Iba, W., & Thompson, K. (1991). A design for the icarus architecture. SIGART Bulletin, 2(4), 104–109.
Marr, D. (1982). Vision: A computational approach. San Francisco, CA: Freeman and Company.
McKeon, R. (Ed.). (1941). The basic works of aristotle. New York: Random House.
Metzler, J., & Shepard, R. (1982). Transformational studies of the internal representations of three-dimensional objects. In R. Shepard & L. Cooper (Eds.), Mental images and their transformations(pp. 25–71). Cambridge, MA: MIT Press.
Moravec, H. (1999), Robot: Mere machine to transcendant mind. Oxford, UK: Oxford University Press.
Newell, A. (1973). You can’t play 20 questions with nature and win: Projective comments on the papers of this symposium. In W. Chase (Ed.), Visual Information Processing(pp. 283– 308). New York: Academic Press.
Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.
Newstead, S. E., & Evans, J. S. T. (Eds.). (1995). Perspectives on thinking and reasoning. Engle-wood Cliffs, NJ: Lawrence Erlbaum.
Nilsson, N. (1991). Logic and Artificial Intelligence. Artificial Intelligence, 47, 31–56.
Nilsson, N. (1995). Eye on the prize. AI Magazine, 16(2), 9–16.
Nilsson, N. (2005). Human-level artificial intelligence? Be serious! AI Magazine, 26(4), 68–75.
Nute, D. (1984). Conditional logic. In D. Gabay & F. Guenthner (Eds.), Handbook of Philosophical Logic Volume II: Extensions of Classical Logic(pp. 387–439). Dordrecht, The Netherlands: D. Reidel.
Pollock, J. (1974). Knowledge and justification. Princeton, NJ: Princeton University Press.
Pollock, J. (1989). How to build a person: A prolegomenon. Cambridge, MA: MIT Press.
Pollock, J. (1995). Cognitive carpentry: A blueprint for how to build a person. Cambridge, MA: MIT Press.
Pollock, J. (2001). Defasible reasoning with variable degrees of justification. Artificial Intelligence, 133, 233–282.
Pollock, J. L. (1992). How to reason defeasibly. Artificial Intelligence, 57(1), 1–42.
Pylyshyn, Z. (1984). Computation and cognition. Cambridge, MA: MIT Press.
Quaife, A. (1988). Automated proofs of löb’s theorem and gödel’s two incompleteness theorems. Journal of Automated Reasoning, 4, 219–231.
Reiter, R. (2001). Knowledge in action: Logical foundations for specifying and implementing dynamical systems. Cambridge, MA: MIT Press.
Rinella, K., Bringsjord, S., & Yang, Y. (2001). Efficacious logic instruction: People are not irremediably poor deductive reasoners. In J. D. Moore & K. Stenning (Eds.), Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society(pp. 851–856). Mahwah, NJ: Lawrence Erlbaum.
Rips, L. (1994). The psychology of proof. Cambridge, MA: MIT Press.
Rosenbloom, P., Laird, J., & Newell, A. (Eds.). (1993). The Soar papers: Research on integrated intelligence. Cambridge, MA: MIT Press.
Russell, S., & Norvig, P. (2002). Artificial intelligence: A modern approach. Upper Saddle River, NJ: Prentice Hall.
Shankar, N. (1994). Metamathematics, machines, and Gödel’s proof. Cambridge, UK: Cambridge University Press.
Shapiro, E. (Ed.). (1987). Concurrent prolog: Collected papers(vols. 1–2). Cambridge, MA: MIT Press.
Shapiro, S., & Rapaport, W. (1987). SNePS considered as a fully intensional propositional semantic network. In N. Cercone & G. McCalla (Eds.), The knowledge frontier: Essays in the representation of knowledge(pp. 262–315). New York: Springer-Verlag.
Sieg, W., & Field, C. (2005). Automated search for gödel’s proofs. Annals of Pure and Applied Logic, 133, 319–338.
Skyrms, B. (1999). Choice and chance: An introduction to inductive logic. Belmont, CA: Wadsworth.
Sloman, S. (1998). Category inference is not a tree: The myth of inheritance hierarchies. Cognitive Psychology, 35, 1–33.
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate. Behavioral and Brain Sciences, 23(5), 645–665.
Steele, G. (1984). Common LISP, second edition: The language. Woburn, MA: Digital Press.
Stillings, N., Weisler, S., Chase, C., Feinstein, M., Garfield, J., & Rissland, E., (1995). Cognitive science. Cambridge, MA: MIT Press.
Sun, R. (1995). Robust reasoning: Integrating rule-based and similarity-based reasoning. Artificial Intelligence, 75, 241–295.
Sun, R. (1999). Accounting for the computational basis of consciousness: A connectionist approach. Consciousness and Cognition, 8, 529–565.
Sun, R. (2002). Duality of the Mind. Mahwah, NJ: Lawrence Erlbaum.
Sun, R., & Zhang, X. (2006). Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, 18(2), 157–168.
Turing, A. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.
Voronkov, A. (1995). The anatomy of vampire: Implementing bottom-up procedures with code trees. Journal of Automated Reasoning 15(2).
Wason, P. (1966). Reasoning. In Brian Foss (Ed.), New horizons in psychology. Hammondsworth, UK: Penguin.
Wigner, E. (1960). The unreasonable effectiveness of mathematics in the natural sciences. In Communications in pure and applied mathematics(pp. 1–14)., New York: John Wiley and Sons.
Wos, L. (1996). The automation of reasoning: An experimenter’s notebook with otter Tutorial, San Diego, CA: Academic Press.
Wos, L., Overbeek, R., Lusk, E., & Boyle, J. (1992). Automated reasoning: Introduction and applications. New York: McGraw-Hill.
Yang, Y., Braine, M., & O’Brien, D. (1998). Some empirical justification of one predicate-logic model. In M. Braine & D. O’Brien (Eds.), Mental logic(pp. 333–365). Mahwah, NJ: Lawrence Erlbaum.
Yang, Y., & Bringsjord, S. (2001). Mental metalogic: A new paradigm for psychology of reasoning. In Proceedings of the Third International Conference on Cognitive Science(ICCS 2001) (pp. 199–204). Hefei, China: Press of the University of Science and Technology of China.
Yang, Y., & Bringsjord, S. (2003). Newell’s program, like Hilbert’s, is dead; let’s move on. Behavioral and Brain Sciences, 26(5), 627.
Yang, Y., & Bringsjord, S. (2006). The mental possible worlds mechanism and the lobster problem: An analysis of a complex GRE logical reasoning task. Journal of Experimental and Theoretical Artificial Intelligence, 18(2), 157– 168.
Yang, Y., & Bringsjord, S. (in press). Mental metalogic: A new, unifying theory of human and machine reasoning. Mahway, NJ: Erlbaum.