Improved EGT-Based Robustness Analysis of Negotiation Strategies in Multiagent Systems via Model Checking
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AbstractAutomated negotiations play an important role in various domains modeled as multiagent systems, where agents represent human users and adopt different negotiation strategies. Generally, given a multiagent system, a negotiation strategy should be robust in the sense that most agents in the system have the incentive to choose it rather than other strategies. Empirical game-theoretic (EGT) analysis is a game-theoretic analysis approach to investigate the robustness of different strategies based on a set of empirical results. In this study, we propose that model-checking techniques can be adopted to improve EGT analysis for negotiation strategies. The dynamics of strategy profiles can bemodeled as a labeled transition system using the counter abstraction technique. We define single-agent best deviation to represent the strategy deviations during negotiation, which focuses on each agent's best deviation benefit and is different from best single-agent deviation used in previous work. Two interesting properties in EGT analysis, i. e., empirical pure strategy Nash equilibrium and best reply cycle, are automatically verified to investigate the robustness of different strategies. For demonstration, the top-six strategies from the automated negotiating agents competition 2010-2012 are studied in terms of their robustness performance. In addition to identifying the most robust strategies, we supply complete rankings among them in different settings. We show that model checking is applicable and efficient to perform robustness analysis of negotiation strategies.
All Author(s) ListSong SZ, Hao JY, Liu Y, Sun J, Leung HF, Zhang J
Journal nameIEEE Transactions on Human-Machine Systems
Volume Number46
Issue Number2
Pages197 - 208
LanguagesEnglish-United Kingdom
KeywordsAutomated negotiation; empirical game theory; model checking; robustness analysis
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

Last updated on 2020-11-08 at 03:45