The comparative linguistics of knowledge representation

  • Authors:

  • Goran Gogic;Henry Kautz;Christos Papadimitriou;Bart Selman

  • Affiliations:

  • University of California San Diego, CSE Department, La Jolla, California;AI Principles Research Department, AT&T Bell Laboratories, Murray Hill, NJ;University of California San Diego, CSE Department, La Jolla, California;AI Principles Research Department, AT&T Bell Laboratories, Murray Hill, NJ

  • Venue:
  • IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
  • Year:
  • 1995

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Abstract

We develop a methodology for comparing knowledge representation formalisms in terms of their "representational succinctness," that is, their ability to express knowledge situations relatively efficiently. We use this framework for comparing many important formalisms for knowledge base representation: propositional logic, default logic, circumscription, and model preference defaults; and, at a lower level, Horn formulas, characteristic models, decision trees, disjunctive normal form, and conjunctive normal form. We also show that adding new variables improves the effective expressibility of certain knowledge representation formalisms.