From knowledge bases to decision models

TLDR

Recent advances in network modelling have spurred AI researchers’ interest in probabilistic and decision modelling, yet fixed network models lack expressiveness for diverse situations. The study aims to merge flexible knowledge representation languages with the normative and computational strengths of decision‑modelling formalisms and algorithms. This is achieved by encoding general knowledge in an expressive language and dynamically generating a decision model for each specific problem instance. Several systems built on this approach demonstrate a range of innovative techniques and highlight key design challenges.

Abstract

Abstract In recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.