This paper (Revised version of a white paper “Unsupervised Problem-Solving by Optimising through Comparisons,” originally published on DCS and Scribd, October 2011.) describes the implementation and functionality of a centralised problem solving system that is included as part of the distributed “licas” system. This is an open source framework for building service-based networks, similar to what you would do on a Cloud or SOA platform. While the framework can include autonomous and distributed behaviour, the problem-solving part can perform more complex centralised optimisation operations and then feed the results back into the network. The problem-solving system is based on a novel type of evaluation mechanism that prefers comparisons between solution results, over maximisation. This paper describes the advantages of that and gives some examples of where it might perform better, including possibilities related to a more cognitive system. 1. Introduction This paper describes the implementation and functionality of a centralised problem-solving system that is included as part of the distributed “licas” service-based framework [1]. The licas (lightweight (internet-based) communication for autonomic services) system is an open source framework for building service-based networks, similar to what you would do on a Cloud or SOA platform. The framework comes with a server for running the services on, mechanisms for adding services to the server, mechanisms for linking services with each other, and mechanisms for allowing the services to communicate with each other. The default communication protocol inside of licas itself is an XML-RPC mechanism, but dynamic invocation of external Web Services is also possible. The main server package is now completely J2ME compatible, meaning that porting to a mobile device should be possible. The architecture and adaptive capabilities through dynamic linking add something new that is not available in other similar systems. While the framework is built around distributed and autonomous objectives, the system is also useful as a test platform for more general AI problems. As such, a centralised component has been added, allowing for heuristic searches to evaluate the situation and feedback the results. The centralised problem solver uses a hyperheuristic with a matching process at its core. The algorithm and novel nature of the process can be briefly described as follows. The solutions and the problem datasets are randomly placed into a grid and then a game is played to try and optimise the total cost over the whole grid.
References
[1]
Licas, http://licas.sourceforge.net/.
[2]
K. Greer, “A stochastic hyper-heuristic for optimising through comparisons,” in Proceedings of the 3rd International Symposium on Knowledge Acquisition and Modeling (KAM'10), pp. 325–328, IEEE, Wuhan, China, October 2010.
[3]
K. Greer, “Literature review for the multi-source intelligence project called “a stochastic hyper-heuristic for optimising through comparisons”,” Distributed Computing Systems Research Report, 2011, http://www.scribd.com/doc/58227009/Hyper-Heuristic-Literature-Review.
[4]
E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, and R. Qu, “A survey of hyper-heuristics,” Computer Science Technical Report NOTTCS-TR-SUB-0906241418-2747, University of Nottingham, 2009, (hhSurvey09).
[5]
M. Bader-El-Den and R. Poli, “Generating SAT Local-Search Heuristics using a GP Hyper-Heuristic Framework,” in Proceedings of the 8th International Conference on Artificial Evolution (EA'07), pp. 37–49, 2007.
[6]
E. Ozcan, M. Misir, and E. K. Burke, “A self-organising hyper-heuristic framework,” in Proceedings of the 4th Multidisciplinary International Scheduling Conference: Theory & Applications (MISTA'09), pp. 784–787, Dublin, Ireland, August 2009.
[7]
J. G. Marín-Blázquez and S. Schulenburg, “Multi-step environment learning classifier systems applied to hyper-heuristics,” in Proceedings of the 8th Annual Genetic and Evolutionary Computation Conference, pp. 1521–1528, Washington, DC, USA, July 2006.
[8]
R. Bai, J. Blazewicz, E. K. Burke, G. Kendall, and B. McCollum, “A simulated annealing hyper-heuristic methodology for flexible decision support,” Tech. Rep. NOTTCS-TR-2007-8, School of CSiT, University of Nottingham, 2007.
[9]
T. P. Runarsson and X. Yao, “Stochastic ranking for constrained evolutionary optimization,” IEEE Transactions on Evolutionary Computation, vol. 4, no. 3, pp. 284–294, 2000.
[10]
N. Sahoo, J. Callan, R. Krishnan, G. Duncan, and R. Padman, “Incremental hierarchical clustering of text documents,” in Proceedings of the 15th ACM Conference on Information and Knowledge Management (CIKM'06), pp. 357–366, New York, NY, USA, November 2006.
[11]
I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 1993.
[12]
A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence, vol. 97, no. 1-2, pp. 245–271, 1997.
[13]
R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997.
[14]
S. McClean, B. Scotney, K. Greer, and R. Pairceir, “Conceptual clustering of heterogeneous distributed databases,” in Proceedings of the 12th Joint European Conference on Machine Learning (ECML'01) and 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'01), Workshop on Ubiquitous Data Mining for Mobile and Distributed Environments, pp. 46–55, September 2001.
[15]
J. J. Rocchio, “Relevance feedback in information retrieval,” in The SMART Retrieval System: Experiments in Automatic Document Processing, G. Salton, Ed., pp. 313–323, Prentice Hall, Englewood Cliffs, NJ, USA, 1971.
[16]
G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “An kNN model-based approach and its application in text categorization,” in Computational Linguistics and Intelligent Text Processing, 5th International Conference, Cicling 2004, Seoul, Korea, A. Gelbukh, Ed., pp. 559–570, Springer, New York, NY, USA, 2004.
[17]
D. Mladeni？, J. Brank, M. Grobelnik, and N. Milic-Frayling, “Feature selection using linear classifier weights: Interaction with classification models,” in Proceedings of Sheffield SIGIR—27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 234–241, Sheffield, UK, July 2004.
[18]
K. Greer, “Symbolic neural networks for clustering higher-level concepts,” NAUN International Journal of Computers, vol. 5, no. 3, pp. 378–386, 2011.
