NDLI: A novel approach to multiagent reinforcement learning: utilizing OLAP mining in the learning process

Content Provider	IEEE Xplore Digital Library
Author	Kaya, M. Alhajj, R.
Copyright Year	1998
Abstract	Reinforcement learning is considered as a strong method for learning in multiagent systems environments. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are much less experienced than others, or some state-action pairs are never visited during the learning phase. Further, before the learning process is completed, an agent cannot exhibit a certain behavior in some states that may be sufficiently experienced. This shows that learning in a partially observable and dynamic multiagent systems environment still constitutes a difficult and major research problem that is worth further investigation. Motivated by this, in this paper, a novel learning approach that integrates online analytical processing (OLAP)-based data mining into the process is proposed. First, a data cube OLAP architecture that facilitates effective storage and processing of the state information reported by agents is described. This way, the action of the other agent, even one not in the visual environment of the agent under consideration, can simply be estimated by extracting online association rules, a well-known data mining technique, from the constructed data cube. Then, a new action selection model that is also based on association rules mining is presented. Finally, states that are not sufficiently experienced by mining multiple-level association rules from the proposed data cube are generalized. Experiments conducted on a well-known pursuit domain show the robustness and effectiveness of the proposed learning approach.
Starting Page	582
Ending Page	590
Page Count	9
File Size	646750
File Format	PDF
ISSN	10946977
Volume Number	35
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Data mining Multiagent systems Association rules Robustness Artificial intelligence Computer science Intelligent robots Data analysis Cognitive science reinforcement learning data cube data mining multiagent online analytical processing (OLAP)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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