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Decision Theory and Artificial Intelligence II: The Hungry Monkey *
Author(s) -
Feldman Jerome A.,
Sproull Robert F.
Publication year - 1977
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog0102_2
Subject(s) - computer science , decision theory , artificial intelligence , reliability (semiconductor) , function (biology) , decision problem , expected utility hypothesis , management science , mathematics , mathematical economics , algorithm , engineering , power (physics) , statistics , physics , quantum mechanics , evolutionary biology , biology
This paper describes a problem‐solving framework In which aspects of mathematical decision theory are incorporated into symbolic problem‐solving techniques currently predominant in artificial intelligence. The utility function of decision theory IS used to reveal tradeoffs among competing strategies for achieving various goals, taking into account such factors as reliability, the complexity of steps in the strategy, and the value of the goal. The utility function on strategies can therefore be used as a guide when searching for good strategies. It is also used to formulate solutions to the problems of how to acquire a world model, how much planning effort is worthwhile, and whether verification tests should be performed. These techniques are illustrated by application to the classic monkey and bananas problem.