Matrix analysis
Distributed Artificial Intelligence
Distributed Artificial Intelligence
Constraint-directed negotiation of resource reallocations
Distributed Artificial Intelligence (Vol. 2)
Negotiating task decomposition and allocation using partial global planning
Distributed Artificial Intelligence (Vol. 2)
What is coordination theory and how can it help design cooperative work systems?
CSCW '90 Proceedings of the 1990 ACM conference on Computer-supported cooperative work
Technical Note: \cal Q-Learning
Machine Learning
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Multi-agent reinforcement learning: independent vs. cooperative agents
Readings in agents
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Intelligent systems and interfaces
Stochastic dynamic programming with factored representations
Artificial Intelligence
Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers
Learning fuzzy classifier systems for multi-agent coordination
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Context-specific multiagent coordination and planning with factored MDPs
Eighteenth national conference on Artificial intelligence
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Coordination in multiagent reinforcement learning: a Bayesian approach
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Dynamic Programming
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Multi-Agent Reinforcement Learning for Planning and Scheduling Multiple Goals
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Environment centered analysis and design of coordination mechanisms
Environment centered analysis and design of coordination mechanisms
Layered learning in multiagent systems
Layered learning in multiagent systems
A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms
Neural Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Coalition formation mechanism in multi-agent systems based on genetic algorithms
Applied Soft Computing
Knowledge propagation in a distributed omnidirectional vision system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA
Fuzzy Sets and Systems
Coordination guided reinforcement learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ``coarse-grained'' and ``fine-grained'', are proposed to address agents coordination behavior at two different levels. The ``coarse-grained'' algorithm operates at one level and tackle hard system constraints, and the ``fine-grained'' at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.