Reasoning and Facts Explanation in Valuation Based Systems
This work addresses computational challenges in probabilistic reasoning for AI and decision-making systems, though it appears incremental as it builds on existing genetic algorithm methods.
The paper tackles the NP-hard problem of finding the k most plausible explanations in Bayesian belief networks by proposing a genetic algorithm to overcome restrictions like limited k or network structure, and extends this approach to the general Valuation Based System framework using Dempster-Shafer belief calculus.
In the literature, the optimization problem to identify a set of composite hypotheses H, which will yield the $k$ largest $P(H|S_e)$ where a composite hypothesis is an instantiation of all the nodes in the network except the evidence nodes \cite{KSy:93} is of significant interest. This problem is called "finding the $k$ Most Plausible Explanation (MPE) of a given evidence $S_e$ in a Bayesian belief network". The problem of finding $k$ most probable hypotheses is generally NP-hard \cite{Cooper:90}. Therefore in the past various simplifications of the task by restricting $k$ (to 1 or 2), restricting the structure (e.g. to singly connected networks), or shifting the complexity to spatial domain have been investigated. A genetic algorithm is proposed in this paper to overcome some of these restrictions while stepping out from probabilistic domain onto the general Valuation based System (VBS) framework is also proposed by generalizing the genetic algorithm approach to the realm of Dempster-Shafer belief calculus.