Publication in BibTeX Format

@TECHREPORT{AICPub633:1984,
  AUTHOR={Konolige, Kurt},
  TITLE={Belief and Incompleteness},
  ADDRESS={333 Ravenswood Ave., Menlo Park, CA 94025},
  INSTITUTION={AI Center, SRI International},
  MONTH={Jan},
  NUMBER={319},
  YEAR={1984},
  ABSTRACT={Two artificially intelligent (AI) computer agents begin to play a game
     of chess, and the following conversation ensues: 
 
 S1: Do you know
     the rules of chess? 
 S2: Yes. 
 S1: Then you know whether White has
     a forced initial win or not. 
 S2: Upon reflection, I realize that I must.
     
 S1: Then there is no reason to play. 
 S2: No. 
 Both agents are
     state-of-the-art constructions, incorporating the latest AI research in chess
     playing, natural-language understanding, planning, etc. But because of the
     overwhelming combinatorics of chess, neither they nor the fastest foreseeable
     computers would be able to search the entire game tree to find out whether
     White has a forced win. Why then do they come to such an odd conclusion about
     their own knowledge of the game? The chess scenario is an anecdotal example
     of the way inaccurate cognitive models can lead to behavior that is less than
     intelligent in artificial agents. In this case, the agents’ model of belief
     is not correct. They make the assumption that an agent actually knows all the
     consequences of his beliefs. S1 knows that chess is a finite game, and thus
     reasons that, in principle, knowing the rules of chess is all that is required
     to figure out whether White has a forced initial win. After learning that S2
     does indeed know the rules of chess, he comes to the erroneous conclusion that
     S2 also knows this particular consequence of the rules. and S2 himself, reflecting
     on his own knowledge in the same manner, arrives at the same conclusion, even
     though in actual fact he could never carry out the computations necessary to
     demonstrate it.}
}