Cognitive Science · 1977 · 117 citations · 29 references
BusinessComputer ScienceDecision SciencesArtificial IntelligenceDecision MakingDecision TheoryDecision-making ModelsHeuristic SearchMathematical ReasoningProblem SolvingProblem-solving StrategiesUtility FunctionsDecision ProblemsDecision-making HeuristicsDecision-making TheoryHeuristics In Decision MakingMathematical Problem SolvingOptimal Decision MakingOptimal Decision TheoryStrategic Reasoning
The paper proposes a problem‑solving framework that integrates mathematical decision theory into symbolic AI techniques. It uses a utility function to evaluate trade‑offs among strategies, guide strategy search, and address world‑model acquisition, planning effort, and verification decisions. The framework is demonstrated on the classic monkey‑and‑bananas problem.
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.
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Judgment under Uncertainty: Heuristics and Biases
Amos Tversky, Daniel Kahneman · Science · 1974 · 28K citations
Judgment under Uncertainty: Heuristics and Biases
Amos Tversky, Daniel Kahneman · 1975 · 23.1K citations