Automatic Transfer Between Negotiation Tasks

Learning in automated negotiations, while useful, is hard because of the indirect way the target function can be observed and the limited amount of experience available to learn from. Transfer learning is a branch of machine learning research concerned with the reuse of previously acquired knowledge in new learning tasks to, for example, reduce the amount of learning experience required to attain a certain level of performance. This paper proposes two new variations of TrAdaBoost - a well known instance transfer technique - that can be used in a multi-issue negotiation setting. Theoretical bounds on the performance increase caused by this transfer scheme are derived and the suggested approach is evaluated in a couple of negotiation scenarios.