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ATAL
2010
Springer
14 years 11 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
ATAL
2010
Springer
14 years 11 months ago
Hybrid transitive trust mechanisms
Establishing trust amongst agents is of central importance to the development of well-functioning multi-agent systems. For example, the anonymity of transactions on the Internet c...
Jie Tang, Sven Seuken, David C. Parkes
ATAL
2010
Springer
14 years 11 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh
ATAL
2010
Springer
14 years 11 months ago
Learning multi-agent state space representations
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
Yann-Michaël De Hauwere, Peter Vrancx, Ann No...
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CCGRID
2010
IEEE
14 years 11 months ago
Availability Prediction Based Replication Strategies for Grid Environments
Volunteer-based grid computing resources are characteristically volatile and frequently become unavailable due to the autonomy that owners maintain over them. This resource volati...
Brent Rood, Michael J. Lewis