Abstract
A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent's preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other's wishes and future behavior into account when deciding on a proposal. Therefore, in order to reach better and earlier agreements, an agent can apply learning techniques to construct a model of the opponent. There is a mature body of research in negotiation that focuses on modeling the opponent, but there exists no recent survey of commonly used opponent modeling techniques. This work aims to advance and integrate knowledge of the field by providing a comprehensive survey of currently existing opponent models in a bilateral negotiation setting. We discuss all possible ways opponent modeling has been used to benefit agents so far, and we introduce a taxonomy of currently existing opponent models based on their underlying learning techniques. We also present techniques to measure the success of opponent models and provide guidelines for deciding on the appropriate performance measures for every opponent model type in our taxonomy.
Highlights
Negotiation is a process in which parties interact to settle a mutual concern to improve their status quo
Different opponent models are created to learn different negotiation aspects; we introduce our taxonomy of the various concepts that are learned, and how they are learned, in Sect
We survey opponent modeling techniques used in bilateral negotiations, and we discuss all possible ways in which opponent models are employed to benefit negotiation agents
Summary
Negotiation is a process in which parties interact to settle a mutual concern to improve their status quo. Negotiation is a core activity in human society, and is studied by various disciplines, including economics [147,158], artificial intelligence [69,91,106,107,118,182], game theory [19,69,91,118,120,147,172], and social psychology [170]. In the last two decades, there has been a growing interest in the automation of negotiation and e-negotiation systems [18,71,91,99,107], for example in the setting of e-commerce [20,79,105,126]. The interest is fueled by the promise of automated agents being able to negotiate on behalf of human negotiators and to find better outcomes than human negotiators [20, 55, 89, 121, 126, 149, 192]
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