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Adversarial Problem Solving: Modeling an Opponent Using Explanatory Coherence
Author(s) -
Thagard Paul
Publication year - 1992
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/s15516709cog1601_4
Subject(s) - coherence (philosophical gambling strategy) , analogy , adversarial system , construct (python library) , adversary , generalization , explanatory model , fictitious play , computer science , artificial intelligence , cognitive science , deception , game theory , mathematical economics , epistemology , psychology , mathematics , social psychology , computer security , philosophy , statistics , programming language
In adversarial problem solving (APS), one must anticipate, understand and counteract the actions of an opponent. Military strategy, business, and game playing all require an agent to construct a model of an opponent that includes the opponent's model of the agent. The cognitive mechanisms required for such modeling include deduction, analogy, inductive generalization, and the formation and evaluation of explanatory hypotheses. Explanatory coherence theory captures part of what is involved in APS, particularly in cases involving deception.