Advanced KR&R

I. Introduction to:

Knowledge Representation and Reasoning

Last Update: 22 August 2008

Note: NEW or UPDATED material is highlighted

  1. What is AI?

  2. My definition of KRR:

  3. 4 quotes about KR:

    1. "The core of AI is ... knowledge representation"

    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."

    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.

  4. An interesting goal for KRR:

  5. What is "knowledge"?

  6. On representing non-existents and other "intensional" items:

  7. Knowledge-Representation Hypothesis

  8. Syntax vs. semantics:

  9. Some important, and often overlooked, papers:

  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.

Copyright © 2005-2008 by William J. Rapaport (