Towards Solving Decision Making Problems Using Probabilistic Model Checking

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Author(s)
Shi, Ling
Liu, Shuang
Hao, Jianye
Koh, Jun Yang
Hou, Zhe
Dong, Jin Song
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Juan E. Guerrero

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2017
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Kyushu Univ, Fukuoka, JAPAN

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Abstract

Decision making seeks the optimal choice for maximum rewards or minimal costs under certain conditions, requirements and constraints. Decision making problems in practice are usually complicated as they may be partially observable, stochastic, and dynamic. Such complexities make the traditional decision making methods like mathematical programming difficult to find the optimal choices effectively and efficiently. In this work, we conduct a case study with the 4-player Kuhn Poker game by combining machine learning with probabilistic model checking to generate optimal decisions. Experimental results show that the agent employing our method outperforms the conservative and bluffing players regardless of the positions of players.

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2017 22ND INTERNATIONAL CONFERENCE ON ENGINEERING OF COMPLEX COMPUTER SYSTEMS (ICECCS)

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2017-November

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© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Applied mathematics not elsewhere classified

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