Publication in EndNote Format

%0 Report
%@ 319
%A Konolige, Kurt
%T Belief and Incompleteness
%C 333 Ravenswood Ave., Menlo Park, CA 94025
%I AI Center, SRI International
%D 1984
%X 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.
%U http://www.ai.sri.com/pubs/files/633.pdf