A Class of Algorithms for Distributed Constraint Optimization
Addresses three major issues that arise in Distributed Constraint Optimization Problems (DCOP): efficient optimization algorithms, dynamic and open environments, and manipulations from self-interested users. This book introduces a series of DCOP algorithms, which are based on dynamic programming.
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I Preliminaries and Background
II The DPOP Algorithm
agent Xi allocation Artificial Intelligence assignment Asynchronous backtrack BB-M-DPOP branch and bound bytes cache CDD message centralized cluster root combinatorial auctions complexity computation constraint satisfaction problems cost DCOP algorithm DCOP model Dechter DFS arrangement DFS construction DFS tree DFS(A DFS−i dimensions DisCSP distributed algorithm distributed constraint optimization domain DPOP dynamic programming edge efficient social choice example explored exponential Faltings Figure H-DPOP hard constraints heuristic high width hypercubes induced width inference influence instantiation introduce linear number local search LS-DPOP marginal problems maximal memory requirements NCBB neighbors node Xi number of messages OptAPO optimal solution optimal value optimization problem overhead parent PC-DPOP performed Petcu phase possible problem graph protocol pruning pseudotree R-M-DPOP received redistribution relations reuse search algorithms search space Section self-stabilizing sensor Sepi server social choice problem solving subproblems token tuple UTIL messages UTIL propagation VALUE messages VALUE propagation VCG mechanism VCG payments