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.
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Notes
Adopting the same notation as in the original TrAdaBoost paper, the index s stands for "same distribution instance space" and the index D for "different distribution instance space".
Extending TrAdaBoost to multi-class classification problems is fairly straight forward.
Please note, that the formalization using the KL measure assesses that the two distributions should be having the same domain. This is reasonable in our framework as we operate within the same negotiation domain. If the two distributions are structurally different both could be approximated using one bigger distribution such as Gaussian mixture models.
In this work we split the negotiation session in intervals of 3 s.
Competitiveness refers to the minimum distance of possible outcomes in a domain to the point where both parties are both fully satisfied. To put it differently, agents tend to achieve better performance in a domain with lower competitiveness.
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Chen, S., Ammar, H.B., Tuyls, K. et al. Transfer for Automated Negotiation. Künstl Intell 28, 21–27 (2014). https://doi.org/10.1007/s13218-013-0284-x
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DOI: https://doi.org/10.1007/s13218-013-0284-x