Multi-agent collaborative service and distributed problem solving

Authors:
Jiming Liu;Xiaolong Jin;Yi Tang
Affiliations:
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong;Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong;Department of Mathematics, Guangzhou University, Guangzhou, China
Venue:
Cognitive Systems Research
Year:
2004

Citing 12
Cited 6

Noise strategies for improving local search

AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the run-time behaviour of stochastic local search algorithms for SAT

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Using global constraints for local search

DIMACS workshop on on Constraint programming and large scale discrete optimization
Local search characteristics of incomplete SAT procedures

Artificial Intelligence
Multi-agent oriented constraint satisfaction

Artificial Intelligence
A Discrete Lagrangian-Based Global-SearchMethod for Solving Satisfiability Problems

Journal of Global Optimization
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms

IEEE Transactions on Knowledge and Data Engineering
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT

CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
The theory and applications of discrete constrained optimization using lagrange multipliers

The theory and applications of discrete constrained optimization using lagrange multipliers
The exponentiated subgradient algorithm for heuristic Boolean programming

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A new method for solving hard satisfiability problems

AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Evidence for invariants in local search

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

A Driving Force for e-Transformation - The Centre for e-Transformation Research / WIC Hong Kong Centre

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Web intelligence meets brain informatics

WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Towards human-level web intelligence

RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Building a brain-informatics portal on the wisdom web with a multi-layer grid: a new challenge for web intelligence research

MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
The wisdom web: a grand intellectual undertaking

AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Web intelligence meets brain informatics: an impending revolution in WI and brain sciences

AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we show that collaborative services can be formulated into distributed constraint satisfaction problems. We introduce the notion of multi-agent collaborative service (MACS), and employ a distributed discrete Lagrange multipliers (DDLM) method to automatically handle an MACS task. The DDLM method is based on a distributed multi-agent system. The behaviors of agents are guided by predefined DDLM rules. In order to make it more efficient in achieving a solution state, we incorporate strategies for tuning the Lagrange multipliers. We validate the effectiveness of the DDLM method with benchmark SAT problems. Furthermore, we provide the mathematical properties of DDLM and present the corresponding DDLM algorithms.