Introduction to distributed algorithms
Introduction to distributed algorithms
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Distributed breakout revisited
Eighteenth national conference on Artificial intelligence
Comparing two approaches to dynamic, distributed constraint satisfaction
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Improving the efficiency of the distributed stochastic algorithm
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Overlay networks for task allocation and coordination in large-scale networks of cooperative agents
Autonomous Agents and Multi-Agent Systems
Adaptive and non-adaptive distribution functions for DSA
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
Improving the privacy of the asynchronous partial overlay protocol
Multiagent and Grid Systems - Principles and Practice of Multi-Agent Systems
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The Distributed Probabilistic Protocol (DPP) is a new, approximate algorithm for solving Distributed Constraint Satisfaction Problems (DCSPs) that exploits prior knowledge to improve the algorithm's convergence speed and efficiency. This protocol can most easily be thought of as a hybrid between the Distributed Breakout Algorithm (DBA) and the Distributed Stochastic Algorithm (DSA), because like DBA, agents exchange "improve" messages to control the search process, but like DSA, actually change their values based on a random probability. DPP improves upon these algorithms by having the agents exchange probability distributions that describe the likelihood of having particular "improve" values. These distributions can then be used by an agent to estimate the probability of having the best improve value among its neighbors or to compute the error caused by not informing other agents of changes to its improve value. This causes the protocol to use considerably fewer messages than both DBA and DSA, does not require a user to choose a randomness parameter like DSA, and allows DPP to more quickly converge onto good solutions. Overall, this protocol is empirically shown to be very competitive with both DSA and DBA.