Abstract
This paper describes a multi-agent learning approach to adaptation to users' preferences realized by an interface agency. Using a contract-net-based negotiation technique, agents as contractors as well as managers negotiate with each other to pursue the overall goal of dynamic user adaptation. By learning from indirect user feedback, the adjustment of internal credit vectors and the assignment of contractors that gained maximal credit with respect to the user's current preferences, the preceding session, and current situational circumstances can be realized. In this way, user adaptation is achieved without accumulating explicit user models but by the use of implicit, distributed user models.
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Lenzmann, B., Wachsmuth, I. (1997). Contract-net-based learning in a user-adaptive interface agency. In: Weiß, G. (eds) Distributed Artificial Intelligence Meets Machine Learning Learning in Multi-Agent Environments. LDAIS LIOME 1996 1996. Lecture Notes in Computer Science, vol 1221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62934-3_50
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DOI: https://doi.org/10.1007/3-540-62934-3_50
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