Propositional Argumentation Systems and Symbolic Evidence Theory
Propositional Argumentation Systems and Symbolic Evidence Theory
Propositional Argumentation Systems and Symbolic Evidence Theory
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iv<br />
An efficient implementation of assumption–based systems mainly depends<br />
on an efficient <strong>and</strong> compact representation of logical formulae. For that<br />
purpose, a general programming toolbox for logical formulae has been developed.<br />
The toolbox is the basis of the entire modeling system. It includes<br />
algorithms to compute prime implicates <strong>and</strong> pseudo–support, as well as<br />
methods to decompose knowledgde bases. Furthermore, a generic module<br />
enabling local computation in valuation networks has been implemented.<br />
Based on this module, an inference mechanism for assumption–based systems<br />
has been realized. Along with it, a student project has elaborated a<br />
modeling language <strong>and</strong> an appropriate interpreter. All these modules can<br />
be combined into a user–friendly modeling system that can be used to build<br />
<strong>and</strong> h<strong>and</strong>le assumption–based models. This thesis describes the architecture<br />
of the modeling system.<br />
Two fields are considered as possible applications of assumption–based systems.<br />
The first application is the field of causal or Bayesian networks. This<br />
thesis attempts to transform structures of causal relations into assumption–<br />
based systems. The second application is the field of model–based diagnostics<br />
in faulty technical systems.
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