Now showing items 3-8 of 8

  • Escaping local optima in multi-agent oriented constraint satisfaction. 

    Basharu, Muhammed; Ahriz, Hatem; Arana, Ines (Springer-Verlag., 2004)
    BASHARU, M., AHRIZ, H. and ARANA, I., 2003. Escaping local optima in multi-agent oriented constraint satisfaction. In: F. COENEN, A. PREECE and A. MACINTOSH, eds. Research and development in intelligent systems Xx. Proceedings of Ai2003, the twenty-third SGAI international conference on innovative techniques and applications of artificial intelligence. 15-17 December 2003. Cambridge, UK. Pp. 97-110.
    We present a multi-agent approach to constraint satisfaction where feedback and reinforcement are used in order to avoid local optima and, consequently, to improve the overall solution. Our approach, FeReRA, is based on ...
  • Escaping local optima with penalties in distributed iterative improvement search. 

    Basharu, Muhammed; Arana, Ines; Ahriz, Hatem (IJCAI, 2005-07)
    BASHARU, M., ARANA, I. and AHRIZ, H., 2005. Escaping local optima with penalties in distributed iterative improvement search. In: Proceedings of the 6th International Workshop on Distributed Constraint Reasoning, DCR2005. 30 July 2005. pp. 192-206.
    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 ...
  • Escaping local optima: constraint weights vs. value penalties. 

    Basharu, Muhammed; Arana, Ines; Ahriz, Hatem (Springer Verlag http://dx.doi.org/10.1007/978-1-84800-094-0_5, 2007)
    BASHARU, M., ARANA, I. and AHRIZ, H. 2007. Escaping local optima: constraint weights vs. value penalties. In: M. BRAMER, F. COENEN and PETRIDIS, M. (eds.) Research and development in intelligent systems XXIV. Proceedings of the 27th SGAI International Conference on Artificial Intelligence, AI-07, 10-12 December 2007. Cambridge. pp. 51-64
    Constraint Satisfaction Problems can be solved using either iterative improvement or constructive search approaches. Iterative improvement techniques converge quicker than the constructive search techniques on large ...
  • Solving coarse-grained DisCSPs with Multi-DisPeL and DisBO-wd. 

    Basharu, Muhammed; Arana, Ines; Ahriz, Hatem (IEEE Computer Society http://dx.doi.org/10.1109/IAT.2007.68, 2007)
    BASHARU, M., ARANA, I and AHRIZ, A., 2007. Solving coarse-grained DisCSPs with Multi-DisPeL and DisBO-wd. In: TSAU YOUNG LIN, JEFFREY M. BRADSHAW, MATTHIAS KLUSCH, CHENGQUI ZHANG, ANDREI BRODER and HOWARD HO, eds. 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2007 Main Conference Proceedings). 2-5 November 2007. California, USA. Pp. 335-341.
    We present Multi-DisPel, a penalty-based local search distributed algorithm which is able to solve coarse-grained Distributed Constraint Satisfaction Problems (DisCSPs) efficiently. Multi-DisPeL uses penalties on values ...
  • Solving DisCSPs with penalty-driven search. 

    Basharu, Muhammed; Arana, Ines; Ahriz, Hatem (AAAI Press, 2005-07)
    BASHARU,M., ARANA, I. and AHRIZ, H. 2005. Solving DisCSPs with penalty-driven search. In: Proceedings of the Twentieth National Conference of Artificial Intelligence. 9-11 July 2005. Pittsburgh, Pennsylvania. pp. 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 ...
  • Stoch-DisPeL: exploiting randomisation in DisPeL. 

    Basharu, Muhammed; Arana, Ines; Ahriz, Hatem (2006)
    BASHARU, M., ARANA, I. and AHRIZ, H., 2006. Stoch-DisPeL: exploiting randomisation in DisPeL. In: Proceedings of 7th International Workshop on Distributed Constraint Reasoning, DCR2006. 8 May 2006. Hakodate, Japan. pp. 117-131.
    We present Stoch-DisPeL, an extension of the distributed constraint programming algorithm DisPeL which incorporates randomisation into the algorithm. We justify the introduction of stochastic moves and analyse its ...