Nondominated equilibrium solutions of multiobjective two-person nonzero-sum games in normal and extensive forms

In this paper, we review the development of studies on multiobjective noncooperative games, and particularly we focus on nondominated equilibrium solutions in multiobjective two-person nonzero-sum games in normal and extensive forms. After outlining studies related to multiobjective noncooperative games, we treat multiobjective two-person nonzero-sum games in normal form, and a mathematical programming problem yielding nondominated equilibrium solutions is shown. As for extensive form games, we first provide a game representation of the sequence form, and then formulate a mathematical programming problem for obtaining nondominated equilibrium solutions

A general framework of agent-based simulation for analyzing behavior of players in games, Journal of Telecommunications and Information Technology, 2007, nr 4

In this paper, we give a general framework of agent-based simulation for analyzing behavior of players in various types of games. In our simulation model, artificial adaptive agents have a mechanism of decision making and learning based on neural networks and genetic algorithms. The synaptic weights and thresholds characterizing the neural network of an artificial agent are revised in order that the artificial agent obtains larger payoffs through a genetic algorithm. The proposed framework is illustrated with two examples, and, by giving some simulation result, we demonstrate availability of the simulation analysis by the proposed framework of agent-based simulation, from which a wide variety of simulation settings can be easily implemented and detailed data and statistics are obtained

Agent-based Simulation Analysis for Network Formation

In this paper, we conduct agent-based simulation experiments for network formation analysis. In the published papers, Bala and Goyal (2000) have constructed a mathematical model leading a star network to be strict Nash equilibrium. However, Berninghaus et al. (2007) have conducted the laboratory experiments using human subjects basing on the mathematical model, and the result of the experiments indicates that human subjects do not always make decision just as the mathematical model predicted. In this paper, we propose a simulation model using the adaptive artificial agents to clarify the reason of the deviation from the mathematical predictions

A Semantic Similarity Measurement Method Based on Information Quality in the Structure of the Gene Ontology

Gene ontology (GO) which described a biological concept of gene has attracted attention as an index for measuring semantic similarity of gene. This paper considers a new method for measuring the semantic similarity of GO through an extension and combination of two existing methods by Resnik and Wang et al. in order to improve their drawbacks of effects of shallow annotation. It is shown that the proposed method is superior to existing methods through experiments with pathway data

An Evolutionary Multi-Objective Optimization-Based Constructive Method for Learning Classifier Systems Adjusting to Non-Markov Environments

Learning Classifier Systems (LCSs) are rule-based systems that automatically build their rule set so as to get optimal policies through evolutionary processes. This paper considers an evolutionary multi-objective optimization-based constructive method for LCSs that adjust to non-Markov environments. Our goal is to construct a XCSMH (eXtended Classifier System - Memory Hierarchic) that can obtain not only optimal policies but also highly generalized rule sets. Results of numerical experiments show that the proposed method is superior to an existing method with respect to the generality of the obtained rule sets

An approximate solution method based on tabu search for k-minimum spanning tree problems

This paper considers k-minimum spanning tree problems. An existing solution algorithm based on tabu search, which was proposed by Katagiri et al., includes an iterative solving procedure of minimum spanning tree (MST) problems for subgraphs to obtain a local optimal solution of k-minimum spanning tree problems. This article provides a new tabu-searchbased approximate solution method that does not iteratively solve minimum spanning tree problems. Results of numerical experiments show that the proposed method provides a good performance in terms of accuracy over those of existing methods for relatively high cardinality k

Resistive Oxygen Sensor Using Ceria-Zirconia Sensor Material and Ceria-Yttria Temperature Compensating Material for Lean-Burn Engine

Temperature compensating materials were investigated for a resistive oxygen sensor using Ce0.9Zr0.1O2 as a sensor material for lean-burn engines. The temperature dependence of a temperature compensating material should be the same as the sensor material; therefore, the Y concentration in CeO2-Y2O3 was optimized. The resistance of Ce0.5Y0.5O2-δ was independent of the air-to-fuel ratio (oxygen partial pressure), so that it was confirmed to function as a temperature compensating material. Sensor elements comprised of Ce0.9Zr0.1O2 and Ce0.5Y0.5O2-δ were fabricated and the output was determined to be approximately independent of the temperature in the wide range from 773 to 1,073 K

Artificial adaptive agent model characterized by learning and fairness in the ultimatum games, Journal of Telecommunications and Information Technology, 2007, nr 4

This paper examines the result of the experimental research on the ultimatum games through simulation analysis. To do so, we develop agent-based simulation system imitating the behavior of human subjects in the laboratory experiment by implementing a learning mechanism involving a concept of fairness. In our agent-based simulation system, mechanisms of decision making and learning are constructed on the basis of neural networks and genetic algorithms

Coordination Gameswith Communication Costs in NetworkEnvironments, Journal of Telecommunications and Information Technology, 2010, nr 2

In this paper, we deal with a coordination game in a network where a player can choose both an action of the game and partners for playing the game. In particular, a player interacts with players connecting through a path consisting of multiple links as well as with players directly connecting by a single link. We represent decay or friction of payoffs with distance as communication costs, and examine the effect of the communication cost on behavior of players in the game and network formation. We investigate properties of equilibrium networks by classifying the link cost and the communication cost, and show diversity of the equilibrium networks