AN INTELLIGENT DECISION SUPPORT BASED ON A SUBTRACTIVE CLUSTERING AND FUZZY INFERENCE SYSTEM FOR MULTIOBJECTIVE OPTIMIZATION PROBLEM IN SERIOUS GAME

I. G. P. Asto Buditjahjanto () and Hajime Miyauchi ()
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I. G. P. Asto Buditjahjanto: Department of Electrical Engineering, Universitas Negeri Surabaya, Kampus Ketintang, Surabaya, Indonesia
Hajime Miyauchi: Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan

International Journal of Information Technology & Decision Making (IJITDM), 2011, vol. 10, issue 05, 793-810

Abstract: Learning decision making through playing a game is an interesting activity for the decision maker or player. In this paper, a multiobjective optimization problem for economic and emission dispatch in which the player can learn about the tradeoff between fuel cost (economic) and emission problems to achieve optimal decisions is considered. A nonplayer character (NPC) is an entity that is built to provide intelligent decision support for the player. The proposed approach is carried out in two stages for the NPC module: the first stage uses the nondominated sorting genetic algorithm II method to solve the multiobjective optimization problem; this stage produces some optimal solutions. The next stage uses subtractive clustering to cluster optimal solutions; furthermore, these clusters are used to build a fuzzy inference system based on the Mamdani type. In this stage, players can select the best decision offered by the NPC.

Keywords: Multiobjective; optimization; nonplayer character; subtractive clustering; fuzzy inference system (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1142/S0219622011004579

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