The automation of reasoning as deduction in logical theories is well established. Such logical theories are usually inherited
from the literature or are built manually for a particular reasoning task. They are then regarded as fixed. We will argue
that they should be regarded as fluid.
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As Pólya and others have argued, appropriate representation is the key to successful problem solving [Pólya, 1945]. It follows
that a successful problem solver must be able to choose or construct the representation best suited to solving the current
problem. Some of the most seminal episodes in human problem solving required radical representational change.
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Automated agents use logical theories called ontologies. For different agents to communicate they must align their ontologies. When a large, diverse and evolving community of autonomous
agents are continually engaged in online negotiations, it is not practical to manually pre-align the ontologies of all agent
pairs - it must be done dynamically and automatically.
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Persistent agents must be able to cope with a changing world and changing goals. This requires evolving their ontologies as
their problem solving task evolves. The W3C call this ontology evolution.
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The research reported in this paper was supported by EPSRC grant EP/E005713/1. It will soon be supported by EPSRC grant EP/G000700/1
I would like to thank Michael Chan, Lucas Dixon and Fiona McNeill for their feedback on this paper and their contributions
to the research referred to in it.