Evolving ontologies with online learning and forgetting algorithms
Evolving ontologies with online learning and forgetting algorithms
Agents that require vocabularies to complete tasks can be limited by static vocabularies which cannot evolve to meet unforeseen domain tasks, or reflect its changing needs or environment. However, agents can benefit from using evolution algorithms to evolve their vocabularies, namely the ability to support new domain tasks. While an agent can capitalise on being able support more domain tasks, using existing techniques can hinder them because they do not consider the associated costs involved with evolving an agent's ontology. With this motivation, we explore the area of ontology evolution in agent systems, and focus on the reduction of the costs associated with an evolving ontology.
In more detail, we consider how an agent can reduce the costs of evolving an ontology, these include costs associated with: the acquisition of new concepts; processing new concepts; the increased memory usage from storing new concepts; and the removal of unnecessary concepts. Previous work reported in the literature has largely failed to analyse these costs in the context of evolving an agent's ontology. Against this background, we investigate and develop algorithms to enable agents to evolve their ontologies.
More specifically, we present three online evolution algorithms that enable agents to: i) augment domain related concepts, ii) use prediction to select concepts to learn, and iii) prune unnecessary concepts from their ontology, with the aim to reduce the costs associated with the acquisition, processing and storage of acquired concepts. In order to evaluate our evolution algorithms, we developed an agent framework which enables agents to use these algorithms and measure an agent's performance. Finally, our empirical evaluation shows that our algorithms are successful in reducing the costs associated with evolving an agent's ontology.
Packer, Heather S.
0e86c31f-6460-4bbd-b6ac-c717ee2cbd96
May 2011
Packer, Heather S.
0e86c31f-6460-4bbd-b6ac-c717ee2cbd96
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac
Jennings, Nicholas R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Packer, Heather S.
(2011)
Evolving ontologies with online learning and forgetting algorithms.
University of Southampton, Electronics and Computer Science: Agents, Interactions & Complexity, Doctoral Thesis, 348pp.
Record type:
Thesis
(Doctoral)
Abstract
Agents that require vocabularies to complete tasks can be limited by static vocabularies which cannot evolve to meet unforeseen domain tasks, or reflect its changing needs or environment. However, agents can benefit from using evolution algorithms to evolve their vocabularies, namely the ability to support new domain tasks. While an agent can capitalise on being able support more domain tasks, using existing techniques can hinder them because they do not consider the associated costs involved with evolving an agent's ontology. With this motivation, we explore the area of ontology evolution in agent systems, and focus on the reduction of the costs associated with an evolving ontology.
In more detail, we consider how an agent can reduce the costs of evolving an ontology, these include costs associated with: the acquisition of new concepts; processing new concepts; the increased memory usage from storing new concepts; and the removal of unnecessary concepts. Previous work reported in the literature has largely failed to analyse these costs in the context of evolving an agent's ontology. Against this background, we investigate and develop algorithms to enable agents to evolve their ontologies.
More specifically, we present three online evolution algorithms that enable agents to: i) augment domain related concepts, ii) use prediction to select concepts to learn, and iii) prune unnecessary concepts from their ontology, with the aim to reduce the costs associated with the acquisition, processing and storage of acquired concepts. In order to evaluate our evolution algorithms, we developed an agent framework which enables agents to use these algorithms and measure an agent's performance. Finally, our empirical evaluation shows that our algorithms are successful in reducing the costs associated with evolving an agent's ontology.
More information
Published date: May 2011
Organisations:
University of Southampton, Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 194923
URI: http://eprints.soton.ac.uk/id/eprint/194923
PURE UUID: 9880699a-cf1d-4f3d-8833-ea33df9e7066
Catalogue record
Date deposited: 17 Aug 2011 07:48
Last modified: 15 Mar 2024 02:59
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Contributors
Author:
Heather S. Packer
Thesis advisor:
Nicholas Gibbins
Thesis advisor:
Nicholas R. Jennings
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