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Distributed guided local search for solving binary DisCSPs. (2005)
Conference Proceeding
BASHARU, M., ARANA, I. and AHRIZ, H. 2005. Distributed guided local search for solving binary DisCSPs. In Russell, I. and Markov, Z. (eds.) Proceedings of the 18th International conference of the Florida Artificial Intelligence Research Society (FLAIRS 2005), 16-18 May 2005, Clearwater, USA. Palo Alto: AAAI Press [online], pages 660-665. Available from: http://www.aaai.org/Papers/FLAIRS/2005/Flairs05-108.pdf

We introduce the Distributed Guided Local Search (Dist- GLS) algorithm for solving Distributed Constraint Satisfaction Problems. Our algorithm is based on the centralised Guided Local Search algorithm, which is extended with additional heuristics in... Read More about Distributed guided local search for solving binary DisCSPs..

Solving DisCSPs with penalty-driven search. (2005)
Conference Proceeding
BASHARU,M., ARANA, I. and AHRIZ, H. 2005. Solving DisCSPs with penalty-driven search. In Proceedings of the 20th American Association for Artificial Intelligence national conference on artificial intelligence (AAAI-05), co-located with the 17th Annual conference on innovative applications of artificial intelligence (IAAI-05), 9-13 July 2005, Pittsburgh, USA. Palo Alto: AAAI Press, pages 47-52.

We introduce the Distributed, Penalty-driven Local search algorithm (DisPeL) for solving Distributed Constraint Satisfaction Problems. DisPeL is a novel distributed iterative improvement algorithm which escapes local optima by the use of both tempora... Read More about Solving DisCSPs with penalty-driven search..

Escaping local optima with penalties in distributed iterative improvement search. (2005)
Presentation / Conference
BASHARU, M., ARANA, I. and AHRIZ, H. 2005. Escaping local optima with penalties in distributed iterative improvement search. Presented at the 6th International workshop on distributed constraint reasoning (DCR 2005), part of the 19th International joint conference on artificial intelligence (IJCAI-05), 30 July - 5 August 2005, Edinburgh, UK.

The advantages offered by iterative improvement search make it a popular technique for solving problems in centralised settings. However, the key challenge with this approach is finding effective strategies for dealing with local optima. Such strateg... Read More about Escaping local optima with penalties in distributed iterative improvement search..