Credal Valuation Networks for Machine Reasoning Under Uncertainty
This work addresses uncertainty in machine reasoning for human operators, but it appears incremental as it builds on existing imprecise probability theory and valuation algebra frameworks.
The paper tackles the problem of machine reasoning under uncertainty by developing a credal valuation network, a graphical system for higher-level fusion and reasoning, and demonstrates its utility on a small-scale example.
Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human operators. Valuations, which are mathematical representation of (uncertain) knowledge and collected data, are expressed as credal sets, defined as coherent interval probabilities in the framework of imprecise probability theory. The basic operations with such credal sets, combination and marginalisation, are defined to satisfy the axioms of a valuation algebra. A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example.