PT-ISABB: A Hybrid Tree-based Complete Algorithm to Solve Asymmetric Distributed Constraint Optimization Problems

TL;DR

PT-ISABB is a hybrid algorithm combining inference and search to efficiently solve large-scale asymmetric distributed constraint optimization problems while balancing privacy concerns.

Contribution

The paper introduces PT-ISABB, a novel hybrid complete algorithm that integrates inference and search for better performance on ADCOPs.

Findings

PT-ISABB outperforms existing algorithms in solving large-scale ADCOPs.

The algorithm achieves tighter lower bounds through tailored inference.

Experimental results show superior efficiency and effectiveness.

Abstract

Asymmetric Distributed Constraint Optimization Problems (ADCOPs) have emerged as an important formalism in multi-agent community due to their ability to capture personal preferences. However, the existing search-based complete algorithms for ADCOPs can only use local knowledge to compute lower bounds, which leads to inefficient pruning and prohibits them from solving large scale problems. On the other hand, inference-based complete algorithms (e.g., DPOP) for Distributed Constraint Optimization Problems (DCOPs) require only a linear number of messages, but they cannot be directly applied into ADCOPs due to a privacy concern. Therefore, in the paper, we consider the possibility of combining inference and search to effectively solve ADCOPs at an acceptable loss of privacy. Specifically, we propose a hybrid complete algorithm called PT-ISABB which uses a tailored inference algorithm to provide tight lower bounds and a tree-based complete search algorithm to exhaust the search space. We prove the correctness of our algorithm and the experimental results demonstrate its superiority over other state-of-the-art complete algorithms.