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Künstliche Intelligenz 28(1) - März 2014

https://dl.gi.de/handle/20.500.12116/11088
Auflistung nach:

Auflistung Künstliche Intelligenz 28(1) - März 2014 nach Autor:in "Chen, Siqi"

1 - 2 von 2
  • Zeitschriftenartikel
    Automated Transfer for Reinforcement Learning Tasks
    (KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Bou Ammar, Haitham; Chen, Siqi; Tuyls, Karl; Weiss, Gerhard
    Reinforcement learning applications are hampered by the tabula rasa approach taken by existing techniques. Transfer for reinforcement learning tackles this problem by enabling the reuse of previously learned behaviours. To be fully autonomous a transfer agent has to: (1) automatically choose a relevant source task(s) for a given target, (2) learn about the relation between the tasks, and (3) effectively and efficiently transfer between tasks. Currently, most transfer frameworks require substantial human intervention in at least one of the previous three steps. This discussion paper aims at: (1) positioning various knowledge re-use algorithms as forms of transfer, and (2) arguing the validity and possibility of autonomous transfer by detailing potential solutions to the above three steps.
  • Zeitschriftenartikel
    Transfer for Automated Negotiation
    (KI - Künstliche Intelligenz: Vol. 28, No. 1, 2014) Chen, Siqi; Ammar, Haitham Bou; Tuyls, Karl; Weiss, Gerhard
    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.