Automated revision of CLIPS rule-bases

This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases. CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases

REVISION PROGRAMMING: A KNOWLEDGE REPRESENTATION FORMALISM

The topic of the dissertation is revision programming. It is a knowledge representation formalismfor describing constraints on databases, knowledge bases, and belief sets, and providing acomputational mechanism to enforce them. Constraints are represented by sets of revision rules.Revision rules could be quite complex and are usually in a form of conditions (for instance, ifthese elements are present and those elements are absent, then this element must be absent). Inaddition to being a logical constraint, a revision rule specify a preferred way to satisfy the constraint.Justified revisions semantics assigns to any database a set (possibly empty) of revisions.Each revision satisfies the constraints, and all deletions and additions of elements in a transitionfrom initial database to the revision are derived from revision rules.Revision programming and logic programming are closely related. We established an elegantembedding of revision programs into logic programs, which does not increase the size of a program.Initial database is used in transformation of a revision program into the corresponding logicprogram, but it is not represented in the logic program.The connection naturally led to extensions of revision programming formalism which correspondto existing extensions of logic programming. More specific, a disjunctive and a nestedversions of revision programming were introduced.Also, we studied annotated revision programs, which allow annotations like confidence factors,multiple experts, etc. Annotations were assumed to be elements of a complete infinitely distributivelattice. We proposed a justified revisions semantics for annotated revision programs which agreedwith intuitions.Next, we introduced definitions of well-founded semantics for revision programming. It assignsto a revision problem a single intended model which is computable in polynomial time.Finally, we extended syntax of revision problems by allowing variables and implemented translatorsof revision programs into logic programs and a grounder for revision programs. The implementationallows us to compute justified revisions using existing implementations of the stablemodel semantics for logic programs

Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation

Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.Comment: Knowledge Revision; Problem Solving; Informed Tree Search Strategy; Cartographic Generalisation., Paris : France (2008

Belief Revision with Uncertain Inputs in the Possibilistic Setting

This paper discusses belief revision under uncertain inputs in the framework of possibility theory. Revision can be based on two possible definitions of the conditioning operation, one based on min operator which requires a purely ordinal scale only, and another based on product, for which a richer structure is needed, and which is a particular case of Dempster's rule of conditioning. Besides, revision under uncertain inputs can be understood in two different ways depending on whether the input is viewed, or not, as a constraint to enforce. Moreover, it is shown that M.A. Williams' transmutations, originally defined in the setting of Spohn's functions, can be captured in this framework, as well as Boutilier's natural revision.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996