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Title: A process-oriented approach to representing and reasoning about naive physiology
Author: Arana Landín, Ines
ISNI:       0000 0001 3604 6287
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1995
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This thesis presents the RAP system: a Reasoner About Physiology. RAP consists of two modules: knowledge representation and reasoning. The knowledge representation module describes commonsense anatomy and physiology at various levels of abstraction and detail. This representation is broad (covers several physiological systems), dense (the number of relationships between anatomical and physiological elements is high) and uniform (the same kind of formalism is used to represent anatomy, physiology and their interrelationships). These features lead to a 'natural' representation of naive physiology which is, therefore, easy to understand and use. The reasoning module performs two tasks: 1) it infers the behaviour of a complex physiological process using the behaviours of its subprocesses and the relationships between them; 2) it reasons about the effect of introducing a fault in the model. In order to reason about the behaviour of a complex process, RAP uses a mechanism which consists of the following tasks: (i) understanding how subprocesses behave; (ii) comprehending how these subprocesses affect each others behaviours; (iii) "aggregating" these behaviours together to obtain the behaviour of the top level process; (iv) giving that process a temporal context in which to act. RAP uses limited commonsense knowledge about faults to reason about the effect of a fault in the model. It discovers new processes which originate as a consequence of a fault and detects processes which misbehave due to a fault. The effects of both newly generated and misbehaving processes are then propagated throughout the model to obtain the overall effect of the fault. RAP represents and reasons about naive physiology and is a step forward in the development of systems which use commonsense knowledge.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Artificial intelligence; Reasoning