A BOA-based adaptive strategy with multi-party perspective for automated multilateral negotiations

Determining an effective strategy for intelligent agents in multilateral negotiations is a more complicated problem than in bilateral negotiations. In order to achieve an optimal and beneficial agreement the agent needs to consider the behavior and desired utility of more than one opponent, determine a concession tactic based on a smaller agreement space, and use a computationally efficient mechanism for generating optimal offers. However, a mere extension of bilateral negotiation strategies cannot be effective in multilateral negotiations because the nature of most bilateral negotiation strategies is based on interaction with only one opponent and tracking a single behavior during the negotiation process. In this paper, we propose an adaptive approach based on a multi-party perspective to determine multilateral negotiation strategy. The proposed approach applies the BOA framework (Bidding, Opponent model, and Acceptance) and dynamically models the opponents’ preference profiles. In order to estimate the obtainable utility from opponents and help find a good offer, the agent uses an ensemble model made by individual frequency-based opponent models and a different level of attention to each party’s behavior. The proposed approach also implements a bidding strategy which applies the opponents’ desirable utility to adapt the agent’s concession tactic and produce appropriate offers. The results of experimental evaluations on various negotiation scenarios against the state of the art multilateral negotiation strategies show that our proposed strategy can provide superior performance in both individual utility and social welfare and lead to more optimal and fairer agreements.