205 research outputs found
A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems
Complete search algorithms are important methods for solving Distributed Constraint Optimization Problems (DCOPs), which generally utilize bounds to prune the search space. However, obtaining high-quality lower bounds is quite expensive since it requires each agent to collect more information aside from its local knowledge, which would cause tremendous traffic overheads. Instead of bothering for bounds, we propose a Bound-Independent Pruning (BIP) technique for existing tree-based complete search algorithms, which can independently reduce the search space only by exploiting local knowledge. Specifically, BIP enables each agent to determine a subspace containing the optimal solution only from its local constraints along with running contexts, which can be further exploited by any search strategies. Furthermore, we present an acceptability testing mechanism to tailor existing tree-based complete search algorithms to search the remaining space returned by BIP when they hold inconsistent contexts. Finally, we prove the correctness of our technique and the experimental results show that BIP can significantly speed up state-of-the-art tree-based complete search algorithms on various standard benchmarks
HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-based Inference
Search and inference are two main strategies for optimally solving
Distributed Constraint Optimization Problems (DCOPs). Recently, several
algorithms were proposed to combine their advantages. Unfortunately, such
algorithms only use an approximated inference as a one-shot preprocessing phase
to construct the initial lower bounds which lead to inefficient pruning under
the limited memory budget. On the other hand, iterative inference algorithms
(e.g., MB-DPOP) perform a context-based complete inference for all possible
contexts but suffer from tremendous traffic overheads. In this paper,
hybridizing search with context-based inference, we propose a complete
algorithm for DCOPs, named {HS-CAI} where the inference utilizes the contexts
derived from the search process to establish tight lower bounds while the
search uses such bounds for efficient pruning and thereby reduces contexts for
the inference. Furthermore, we introduce a context evaluation mechanism
to select the context patterns for the inference to further reduce the
overheads incurred by iterative inferences. Finally, we prove the
correctness of our algorithm and the experimental results demonstrate its
superiority over the state-of-the-art
- …