On the use of evidence theory in belief base revision
This work addresses the problem of enhancing reliability and rationality in belief change for agents in AI applications, though it appears incremental as it builds on existing methods.
The paper tackles belief base revision by introducing credible belief base revision using evidence theory, resulting in two new formula-based revision operators and a compromise operator that minimizes loss of initial beliefs.
This paper deals with belief base revision that is a form of belief change consisting of the incorporation of new facts into an agent's beliefs represented by a finite set of propositional formulas. In the aim to guarantee more reliability and rationality for real applications while performing revision, we propose the idea of credible belief base revision yielding to define two new formula-based revision operators using the suitable tools offered by evidence theory. These operators, uniformly presented in the same spirit of others in [9], stem from consistent subbases maximal with respect to credibility instead of set inclusion and cardinality. Moreover, in between these two extremes operators, evidence theory let us shed some light on a compromise operator avoiding losing initial beliefs to the maximum extent possible. Its idea captures maximal consistent sets stemming from all possible intersections of maximal consistent subbases. An illustration of all these operators and a comparison with others are inverstigated by examples.