Transfer for Automated Negotiation

Abstract

Learning in automated negotiation is a difficult problem because the target function is hidden and the available experience for learning is rather limited. Transfer learning is a branch of machine learning research concerned with the reuse of previously acquired knowledge in new learning tasks, for example, in order to reduce the amount of learning experience required to attain a certain level of performance. This paper proposes a novel strategy based on a variation of TrAdaBoost—a classic instance transfer technique—that can be used in a multi-issue negotiation setting. The experimental results show that the proposed method is effective in a variety of application domains against the state-of-the-art negotiating agents.