Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques

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.

POPPONENT: Highly accurate, individually and socially efficient opponent preference model in bilateral multi issue negotiations (Extended Abstract)

“…Another difficulty with modeling an opponent's preferences in bilateral negotiations is related to the time factor. The post event analysis of ANAC tournaments also confirms that the computational complexity of the opponent models and the poor accuracy are the two main factors that degrade the performance of the agents applying these models [Baarslag, 2016]. In particular, the time factor is of paramount importance in online opponent models.…”

“…The negotiation setting here is in accordance with the setting employed by the state of the art models in the field of bilateral automated negotiations and the setting of the ANAC 2010-2013 [Baarslag et al, 2012;Fujita et al, 2013;Yaakov and Ilany, 2015;Zafari et al, 2015] 1 . Automated agents alternatively exchange offers and compete against each other to reach a joint agreement on a set of issues in bilateral negotiations.…”

“…In an automated bilateral multi-issue version of negotiations, intelligent computer agents engage in a cooperative process on behalf of their beneficiaries with different and sometimes contradicting interests, with the objective of achieving an agreement on one or more issues. Recently, with the emergence of ANAC (an annual international Automated Negotiating Agents Competition) [Baarslag et al, 2012;Fujita et al, 2013], many new negotiation strategies have been developed. Most of the existing sophisticated negotiation strategies typically consist of a set of fixed modules.…”

“…Most of the existing sophisticated negotiation strategies typically consist of a set of fixed modules. In general, three main components are distinguished in a negotiating agent which work together within a BOA framework to accomplish the whole negotiation task [Baarslag et al, 2014]: Bidding Strategy (BS), which decides which offer will be sent to the opponent as the next proposal, Opponent Model (OM), which constructs a model of the preference profile of the opponent through a learning technique, and Acceptance Strategy (AC), which receives the incoming offer from the opponent and the outgoing offer chosen by the bidding strategy component, and determines whether the incoming offer is acceptable for the agent or not.…”

“…Despite the variety in opponent modelling techniques, most of the current models rely on a small and common set of learning techniques [Baarslag et al, 2016]. Moreover, since in a single negotiation session all bids (i.e., training examples) are not available at the same time, traditional learning techniques are not easily applied.…”