Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Nonserial Dynamic Programming
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations
Statistics and Computing
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Using the Simulated Annealing Algorithm for Multiagent Decision Making
RoboCup 2006: Robot Soccer World Cup X
VWM: An Improvement to Multiagent Coordination in Highly Dynamic Environments
MATES '07 Proceedings of the 5th German conference on Multiagent System Technologies
A new minirobotics system for teaching and researching agent-based programming
CATE '07 Proceedings of the 10th IASTED International Conference on Computers and Advanced Technology in Education
Local coordination in online distributed constraint optimization problems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Agent-based decentralised coordination for sensor networks using the max-sum algorithm
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.00 |
Coordination graphs offer a tractable framework for cooperative multiagent decision making by decomposing the global payoff function into a sum of local terms. Each agent can in principle select an optimal individual action based on a variable elimination algorithm performed on this graph. This results in optimal behavior for the group, but its worst-case time complexity is exponential in the number of agents, and it can be slow in densely connected graphs. Moreover, variable elimination is not appropriate for real-time systems as it requires that the complete algorithm terminates before a solution can be reported. In this paper, we investigate the max-plus algorithm, an instance of the belief propagation algorithm in Bayesian networks, as an approximate alternative to variable elimination. In this method the agents exchange appropriate payoff messages over the coordination graph, and based on these messages compute their individual actions. We provide empirical evidence that this method converges to the optimal solution for tree-structured graphs (as shown by theory), and that it finds near optimal solutions in graphs with cycles, while being much faster than variable elimination.