%0 Journal Article
%T A Stochastic Hyperheuristic for Unsupervised Matching of Partial Information
%A Kieran Greer
%J Advances in Artificial Intelligence
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/790485
%X This paper (Revised version of a white paper ˇ°Unsupervised Problem-Solving by Optimising through Comparisons,ˇ± 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 ˇ°licasˇ± 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 ˇ°licasˇ± 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.
%U http://www.hindawi.com/journals/aai/2012/790485/