thesis
Analysing the familiar : reasoning about space and time in the everyday world
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Abstract
The development of suitable explicit representations of knowledge that
can be manipulated by general purpose inference mechanisms has always
been central to Artificial Intelligence (AI). However, there has been a
distinct lack of rigorous formalisms in the literature that can be used
to model domain knowledge associated with the everyday physical world.
If AI is to succeed in building automata that can function reasonably
well in unstructured physical domains, the development and utility of such
formalisms must be secured.
This thesis describes a first order axiomatic theory that can be used
to encode much topological and metrical information that arises in our
everyday dealings with the physical world. The formalism is notable for
the minimal assumptions required in order to lift up a very general
framework that can cover the representation of much intuitive spatial and
temporal knowledge. The basic ontology assumes regions that can be
either spatial or temporal and over which a set of relations and
functions are defined. The resulting partitioning of these abstract
spaces, allow complex relationships between objects and the description of
processes to be formally represented. This also provides a useful
foundation to control the proliferation of inference commonly associated
with mechanised logics. Empirical information extracted from the domain
is added and mapped to these basic structures showing how further
control of inference can be secured.
The representational power of the formalism and computational
tractability of the general methodology proposed is substantiated using
two non-trivial domain problems - modelling phagocytosis and exocytosis
of uni-cellular organisms, and modelling processes arising during the
cycle of operations of a force pump