MIT 16 412J - Cognitive Game Theory (85 pages)

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Cognitive Game Theory



Previewing pages 1, 2, 3, 4, 5, 6, 39, 40, 41, 42, 43, 80, 81, 82, 83, 84, 85 of actual document.

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Cognitive Game Theory

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Pages:
85
School:
Massachusetts Institute of Technology
Course:
16 412j - Cognitive Robotics
Cognitive Robotics Documents

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Cognitive Game Theory Alpha Beta minimax search Inductive Adversary Modeling Evolutionary Chess Jennifer Novosad Justin Fox and Jeremie Pouly Our lecture topic is cognitive game We are interested in this subject because games are a simple representation of reality on which we can test any concept developed in artificial intelligence For this reason games have always been considered as an attractive framework for new developments Our talk in divided in three parts Jeremie will first give a quick review of the minimax search and present a few improvements including alpha beta cutoffs transposition table and move ordering He will also introduce the two demonstrations of the lecture Jennifer Justin 1 Motivation Good benchmark Similar to military or financial domains Computer can beat humans Fun 2 Reasoning Techniques for Games Games Statistical Inference Search Minimax Alpha Beta Adversary model Evolutionary Algorithms Bayesian Nets Hidden Markov Models 3 Cognitive Game Theory Alpha Beta Search Jeremie Adversary Modeling Jennifer Evolutionary Algorithms Justin We return to the outline to note that the next section of this talk will now focus on a still small but more detailed and less abstract example of how evolutionary algorithms may be applied to create chess players This example can be found in the paper Kendall and Whitwell An Evolutionary Approach for the Tuning of a Chess Evaluation Function using Population Dynamics Proc 2001 IEEE Congress on Evolutionary Computation 4 Cognitive Game Theory Alpha Beta Search Minimax search Evaluation function Alpha Beta cutoffs Other improvements Demo Adversary Modeling Evolutionary Algorithms We return to the outline to note that the next section of this talk will now focus on a still small but more detailed and less abstract example of how evolutionary algorithms may be applied to create chess players This example can be found in the paper Kendall and Whitwell An Evolutionary Approach for the Tuning of a Chess Evaluation



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