The complexity of Markov decision processes
Mathematics of Operations Research
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Task Modelling in Collective Robotics
Autonomous Robots
The complexity of multiagent systems: the price of silence
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Planning Algorithms
Q-value functions for decentralized POMDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Value-based observation compression for DEC-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Solving Large-Scale and Sparse-Reward DEC-POMDPs with Correlation-MDPs
RoboCup 2007: Robot Soccer World Cup XI
Constraint-based dynamic programming for decentralized POMDPs with structured interactions
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Lossless clustering of histories in decentralized POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Point-based dynamic programming for DEC-POMDPs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Using evolution strategies to solve DEC-POMDP problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Optimal and approximate Q-value functions for decentralized POMDPs
Journal of Artificial Intelligence Research
Policy iteration for decentralized control of Markov decision processes
Journal of Artificial Intelligence Research
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Point-based policy generation for decentralized POMDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Point-based backup for decentralized POMDPs: complexity and new algorithms
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Quasi deterministic POMDPs and DecPOMDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs
Autonomous Agents and Multi-Agent Systems
An investigation into mathematical programming for finite horizon decentralized POMDPs
Journal of Artificial Intelligence Research
Online planning for multi-agent systems with bounded communication
Artificial Intelligence
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
An optimal best-first search algorithm for solving infinite horizon DEC-POMDPs
ECML'05 Proceedings of the 16th European conference on Machine Learning
IEEE Transactions on Information Technology in Biomedicine
Scaling up optimal heuristic search in Dec-POMDPs via incremental expansion
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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The Decentralized Partially Observable Markov Decision Process (DEC-POMDP) model addresses the multiagent planning problem in partially observable environments. Due to its high computational complexity, in general only very small size problems can be solved exactly and most researchers concentrate on approximate solution algorithms to handle more complex cases. However, many approximate solution techniques can handle large size problems only for small horizons due to their exponential memory requirements for representing the policies and searching the policy space. In this study, we offer an approximate solution algorithm called GA-FSC that uses finite state controllers (FSC) to represent a finite-horizon DEC-POMDP policy and searches the policy space using genetic algorithms. We encode FSCs into chromosomes and we use one exact and one approximate technique to calculate the fitness of the chromosomes. The exact calculation technique helps us to obtain better quality solutions with the cost of more processing time compared to the approximate fitness calculation. Our method is able to replicate the best results reported so far in the literature in most cases and it is also able to extend the reported horizons further in almost all cases when compared to optimal approaches.