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Real-time opponent learning in automated negotiation using recursive Bayesian filtering
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DOI：10.1016/j.eswa.2019.03.025.png)
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Abstract
En 中文
Automated negotiation is a toolset to model human interactions during a negotiation process with the aim of improving the efficiency and quality of decision-making using advanced information analytics. During the negotiation, the participants share their viewpoints and concerns about the negotiation issues. However, in reality, they usually do not reveal the details of their preferences to one another. Therefore, modeling and learning opponents' behavior is a crucial component of automated negotiation. In this paper, we propose an estimation technique based on recursive Bayesian filtering to facilitate opponent modeling and-learning in the context of multi-participant, multi-issue negotiations. In the proposed technique, opponents' preference profiles are modeled using fuzzy functions, which are very close to the way humans evaluate alternatives. As the negotiation progresses, the agents can recursively learn the parameters of these models in real time. The only required information for this learning process includes the feedback and the arguments the participants may provide in support of their decisions. At each round, a probabilistic graphical model is also implemented that utilizes the learned preference limits of the participants to offer a new proposal with a high probability of satisfying the participants and reaching an agreement. The proposed methodology is examined in two different negotiation contexts: energy-system development and real estate service. The experiments show that the proposed opponent modeling/learning approach increases the efficiency of the negotiation up to 85% and facilitates reaching an agreement in fewer rounds of negotiation without requiring any prior understanding of the negotiation participants. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Automated negotiation
Opponent modeling
Recursive Bayesian filtering
Unscented particle filtering
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