Belief Maintenance in Bayesian Networks
This work addresses the problem of reasoning under uncertainty for AI systems by proposing a hybrid approach that combines BBNs and TMSs, though it appears incremental in nature.
The paper tackles the limitations of Bayesian Belief Networks (BBNs) in handling partially specified knowledge, contradictions, and explanations by integrating Belief Maintenance Systems (BMSs) based on probabilistic logic, resulting in a new class called Ignorant Belief Networks that can incrementally manage these issues.
Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning under uncertainty but bear some severe limitations: they require a large amount of information before any reasoning process can start, they have limited contradiction handling capabilities, and their ability to provide explanations for their conclusion is still controversial. There exists a class of reasoning systems, called Truth Maintenance Systems (TMSs), which are able to deal with partially specified knowledge, to provide well-founded explanation for their conclusions, and to detect and handle contradictions. TMSs incorporating measure of uncertainty are called Belief Maintenance Systems (BMSs). This paper describes how a BMS based on probabilistic logic can be applied to BBNs, thus introducing a new class of BBNs, called Ignorant Belief Networks, able to incrementally deal with partially specified conditional dependencies, to provide explanations, and to detect and handle contradictions.