Advanced KR&R

I. Introduction to:

Knowledge Representation and Reasoning

1. What is AI?
   • 3 goals of AI research
   • 2 contrasting definitions of AI

2. My definition of KRR:

   The computational theory of how a cognitive agent (including a computer) does, can, or should represent and use information, where:

   • "use" = reason with and reason about

3. 4 quotes about KR:
   1. "The core of AI is ... knowledge representation"
      • Graeme Hirst, Canadian AI #21 (October 1989): 10.

   2. "The key to finding powerful procedures that can solve [AI] ... problems is to discover appropriate representations of the relevant information. Once a representation is given that `make[s] the right things explicit and expose[s] natural constraints' [Winston], it is a much simpler matter to devise purely computational procedures to manipulate the information, still in its encoded representation."
      • G. N. Reeke, Jr., & Gerald M. Edelman (1988), "Real Brains and Artificial Intelligence," Daedalus (Winter 1988): 146-147.

   3. "What structures must be built into a system to allow it to learn? This is a central question for current AI, and the answer depends on issues of knowledge representation: How should knowledge be represented? Out of what components ... are knowledge structures built?"
      • David Waltz, "The Prospects for Building Truly Intelligent Machines," Daedalus (Winter 1988): 192.

   4. "[A] program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows. ... In order for a program to be capable of learning something it must first be capable of being told it.
      • John McCarthy, "Programs with Common Sense" (1959); reprinted in Marvin Minsky (ed.), Semantic Information Processing (Cambridge, MA: MIT Press, 1968).

4. An interesting goal for KRR:
   • A problem:
     Reddy, Raj (2003), "Three Open Problems in AI", Journal of the ACM 50(1): 83-86. [PDF]

   • An attempted solution:
     Friedland, Noah S., et multi al. (2004), "Project HALO: Towards a Digital Aristotle", AI Magazine 25(4) (Winter): 29-47.
     [AAAI PDF] [local PDF]
     • For password, mailto:rapaport@cse.buffalo.edu

5. What is "knowledge"?
   • syllabus [PDF] for David Braun's 2008 course on "Knowing That, Knowing How, and Knowing Who"

   • Gettier, Edmund L. (1963), "Is Justified True Belief Knowledge?", Analysis 23: 121-23;
     • reprinted in A.P. Griffiths (ed.), Knowledge and Belief (Oxford: Oxford University Press, 1967).

     • An interesting follow-up article with good references to the literature:
       Floridi, L. (2004), "On the Logical Unsolvability of the Gettier Problem", Synthese 142: 61-79. [PDF]

   • Kinds of knowledge:
     • Knowing Who:
       • Boër, Stephen E., & Lycan, William G. (1976), "Knowing Who", Philosophical Studies 28: 299-344.

       • Boër, Stephen E., & Lycan, William G. (1985), Knowing Who (Cambridge, MA: MIT Press); Lockwood BD236 .B64 1986

     • On perceptual symbol systems:
       • Barsalou, Lawrence W. (1999), "Perceptual Symbol Systems" Behavioral and Brain Sciences 22: 577-609.

       • Barsalou, Lawrence W. (1999), "Perceptions of Perceptual Symbols", author's response to commentary on "Perceptual Symbol Systems", Behavioral and Brain Sciences 22: 633-660.

     • On diagrammatic representation:
       • Allwein, Gerard, & Barwise, Jon (eds.) (1996), Logical Reasoning with Diagrams (New York: Oxford University Press).

       • Horn, Robert (forthcoming), "Infrastructure for Navigation of Interdisciplinary Debates--Critical Decisions for Representing Argumentation", in Visualizing Argumentation: Tools for Collaborative and Educational Sense-Making

     • Some References on the Procedural-Declarative Controversy

6. On representing non-existents and other "intensional" items:
   • Hirst, Graeme (1991), "Existence Assumptions in Knowledge Representation", Artificial Intelligence 49: 199-242.

   • SNePS and Knowledge, Belief, & Intensionality

7. Knowledge-Representation Hypothesis

8. Syntax vs. semantics:
   • McDermott, Drew (1981), "Artificial Intelligence Meets Natural Stupidity", in John Haugeland (ed.), Mind Design: Philosophy, Psychology, Artificial Intelligence (Cambridge, MA: MIT Press, 1981): 143-160.
     • Cited in a letter to the editor of The New Yorker by Ernest Davis, 6 January 2003, p. 5.
     • Cartoon on "McDermott's Fallacy"

9. Some important, and often overlooked, papers:
   • Minsky, Marvin L. (1965), "Matter, Mind, and Models", in Marvin Minsky (ed.), Semantic Information Processing (Cambridge, MA: MIT Press, 1968): 425-432.

   • Smith, Brian Cantwell (1987), "The Correspondence Continuum", Report CSLI-87-71 (Stanford, CA: Center for the Study of Language and Information).

   • Smith, Brian Cantwell (1985), "Limits of Correctness in Computers", Technical Report CSLI-85-36 (Stanford, CA: Center for the Study of Language and Information).
     • First published in Charles Dunlop & Rob Kling (eds.), Computerization and Controversy (San Diego: Academic Press, 1991): 632-646;
     • Reprinted in Timothy R. Colburn, James H. Fetzer, & Terry L. Rankin (eds.), Program Verification: Fundamental Issues in Computer Science (Dordrecht, Holland: Kluwer Academic Publishers, 1993): 275-293.

10. Tenenbaum & Augenstein on Data, Information, & Semantics"

11. Parisien, Christopher; & Thagard, Paul (2008), "Robosemantics: How Stanley the Volkswagen Represents the World", Minds and Machines 18(2) (Summer): 169-178.