Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84707
Title: PROMOCA: Probabilistic Modeling and Analysis of Agents in Commitment Protocols
Authors: Günay, Akın
Liu, Yang
Zhang, Jie
Keywords: Commitment protocols
Multi agent systems
Issue Date: 2016
Source: Günay, A., Liu, Y., & Zhang, J. (2016). PROMOCA: Probabilistic Modeling and Analysis of Agents in Commitment Protocols. Journal of Artificial Intelligence Research, 57, 465-508.
Series/Report no.: Journal of Artificial Intelligence Research
Abstract: Social commitment protocols regulate interactions of agents in multiagent systems. Several methods have been developed to analyze properties of commitment protocols. However, analysis of an agent's behavior in a commitment protocol, which should take into account the agent's goals and beliefs, has received less attention. In this paper we present ProMoca framework to address this issue. Firstly, we develop an expressive formal language to model agents with respect to their commitments. Our language provides dedicated elements to define commitment protocols, and model agents in terms of their goals, behaviors, and beliefs. Furthermore, our language provides probabilistic and non-deterministic elements to model uncertainty in agents' beliefs. Secondly, we identify two essential properties of an agent with respect to a commitment protocol, namely compliance and goal satisfaction. We formalize these properties using a probabilistic variant of linear temporal logic. Thirdly, we adapt a probabilistic model checking algorithm to automatically analyze compliance and goal satisfaction properties. Finally, we present empirical results about efficiency and scalability of ProMoca.
URI: https://hdl.handle.net/10356/84707
http://hdl.handle.net/10220/41952
ISSN: 1076-9757
DOI: http://dx.doi.org/10.1613/jair.5135
Rights: © 2016 AI Access Foundation. This paper was published in Journal of Artificial Intelligence Research and is made available as an electronic reprint (preprint) with permission of AI Access Foundation. The published version is available at: [http://dx.doi.org/10.1613/jair.5135]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
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