Hierarchical Common-Sense Interaction Learning

M. Rovatsos, J. Lind

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We describe a hierarchical learning approach for effective coordination in repeated games based on a common-sense decomposition of the “coordination problem”. In contrast to most other research on mechanism design and game-learning, we concentrate on breaking down the top-level problem into simpler learning tasks concerned with learning utility functions, best-response strategies and cooperation potentials. We also report on empirical results with the layered learning architecture LAYLA that is constructed using these sub-components in a resource-load balancing scenario. The positive results show that the approach deserves further investigation, although a number of (possibly problem-inherent) difficulties illustrate the limitations of learning approaches in real-world applications
Original languageEnglish
Title of host publicationMultiAgent Systems, 2000. Proceedings. Fourth International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Print)0-7695-0625-9
Publication statusPublished - 2000

Keywords / Materials (for Non-textual outputs)

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