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Title: Weaver - a hybrid artificial intelligence laboratory for modelling complex, knowledge- and data-poor domains
Author: Hare, Matthew Peter
ISNI:       0000 0001 3531 7102
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1999
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Weaver is a hybrid knowledge discovery environment which fills a current gap in Artificial Intelligence (AI) applications, namely tools designed for the development and exploration of existing knowledge in complex, knowledge and data-poor domains. Such domains are typified by incomplete and conflicting knowledge, and data which are very hard to collect. Without the support of robust domain theory, many experimental and modelling assumptions have to be made whose impact on field work and model design are uncertain or simply unknown. Compositional modelling, experimental simulation, inductive learning, and experimental reformulation tools are integrated within a methodology analogous to Popper's scientific method of critical discussion. The purpose of Weaver is to provide a 'laboratory' environment in which a scientist can develop domain theory through an iterative process of in silico experimentation, theory proposal, criticism, and theory refinement. After refinement within Weaver, this domain theory may be used to guide field work and model design. Weaver is a pragmatic response to tool development in complex, knowledge- and data- poor domains. In the compositional modelling tool, a domain-independent algorithm for dynamic multiple scale bridging has been developed. The multiple perspective simulation tool provides an object class library for the construction of multiple simulations that can be flexibly and easily altered. The experimental reformulator uses a simple domain-independent heuristic search to help guide the scientist in selecting the experimental simulations that need to be carried out in order to critically test and refine the domain theory. An example of Weaver's use in an ecological domain is provided in the exploration of the possible causes of population cycles in red grouse (Lagopus, lagopus scoticus). The problem of AI tool validation in complex, knowledge- and data-poor domains is also discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Artificial intelligence