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Information discovery and integration using a multi-agent system with ant colony optimization

Grasdijk, P.S. (2017) Information discovery and integration using a multi-agent system with ant colony optimization. Master's Thesis / Essay, Artificial Intelligence.

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With the abundance of unstructured data on the internet, retrieving and extracting relevant high-quality data has become a major challenge. Relevant data is diffuse with different unknown windows of availability, meaning that data can already be removed before crawlers are able to find it. In this thesis we describe the design of a multi-agent system that discovers, extracts and integrates information about books from vendors. Part of the pre-processing is done outside of the model in order to reduce complexity. The quality of the books is used as a basis for ant colony optimization, in order to assess the quality of the vendors and to decide which vendors will be used for further information retrieval. A variety of parameters and algorithms have been tested and compared, using three types of algorithm, namely ant system (AS), rank-based ant system (RAS), and random walk. Secondly, an experiment has been conducted in which the agents were allowed to request feedback on old books. The experiments show that a number of parameters heavily influence the effectiveness of an algorithm, as expected. Especially the number of crawlers relative to the size of the database is of large influence. Agent feedback also has a large impact on the results, creating a smaller, but more up-to-date knowledge base. Further research could be done with other swarm intelligence algorithms; we explain a few that might be effective.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:31
Last Modified: 15 Feb 2018 08:31

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