Ewa Madalińska-Bugaj

A Generalized QSQR Evaluation Method for Horn Knowledge Bases

We generalize the QSQR evaluation method to give the first set-oriented depth-first evaluation method for Horn knowledge bases. The resulting procedure closely simulates SLD-resolution (to take advantages of the goal-directed approach) and highly exploits set-at-a-time tabling. Our generalized QSQR evaluation procedure is sound and complete. It does not use adornments and annotations. To deal with function symbols, our procedure uses iterative deepening search, which iteratively increases term-depth bound for atoms and substitutions occurring in the computation. When the term-depth bound is fixed, our evaluation procedure runs in polynomial time in the size of extensional relations.

Reasoning about Action and Change: Actions with Abnormal Effects

Most of the research devoted to reasoning about action and change has been based on the assumption that each action behaves in a fixed way. More specifically, to each action A there is assigned a unique specification S describing the effects of A in terms of a state in which A is performed.1 For instance, the well-known action shoot is usually defined as making a gun unloaded and a turkey dead, provided that a gun was loaded. Accordingly, each time the action is executed in a state in which the gun is loaded, it is taken for granted that the turkey is made dead.

Belief revision revisited

In this paper, we propose a new belief revision operator, together with a method of its calculation. Our formalization differs from most of the traditional approaches in two respects. Firstly, we formally distinguish between defeasible observations and indefeasible knowledge about the considered world. In particular, our operator is differently specified depending on whether an input formula is an observation or a piece of knowledge. Secondly, we assume that a new observation, but not a new piece of knowledge, describes exactly what a reasoning agent knows at the moment about the aspect of the world the observation concerns.

Program Verification Techniques as a Tool for Reasoning about Action and Change

We apply Dijkstras semantics for programming languages to formalize reasoning about action and change. The basic idea is to view an action A as a transformation which to each formula β assigns a formula α, with the intention that α represents the set of all initial states such that execution of A begun in any one of them is guaranteed to terminate in a state satisfying β.

Generalizing the QSQR Evaluation Method for Horn Knowledge Bases

We generalize the QSQR evaluation method to give a set-oriented depth-first evaluation method for Horn knowledge bases. The resulting procedure closely simulates SLD-resolution (to take advantages of the goal-directed approach) and highly exploits set-at-a-time tabling. Our generalized QSQR evaluation procedure is sound, complete, and tight. It does not use adornments and annotations. To deal with function symbols, our procedure uses iterative deepening search which iteratively increases term depth bound for atoms occurring in the computation. When the term depth bound is fixed, our evaluation procedure runs in polynomial time in the size of extensional relations.

First-order Generalization of the MPMA Belief Update Operator

In this paper we generalize the MPMA belief update operator by admitting first-order knowledge bases and restricted first-order update formulae. This allows us to consider scenarios which cannot be properly formalized either in the originalMPMA or in other existing approaches to belief update.

How to Solve Qualification and Ramification Using Dijkstra's Semantics for Programming Languages

In this paper we consider two aspects of reasoning about action and change: qualification and ramification problems in the context of domain constraint axioms. It was pointed out that the same axiom may cause qualification and ramification. The reason that we distinguish these two cases lies in the semantics of concerning fluents. To distinguish whether a given constrain axiom involves qualification or ramification additional information, namely influence relation, which define how fluents can possibly affect each other, must be provided.

Reasoning about Action and Change: Defeasible Observations and Actions with Abnormal Effects

We provide a very general framework to reason about action and change. Our approach generalizes existing formalisms aimed at this type of inference in three respects. Firstly, we admit actions with abnormal effects, i.e. actions that may behave abnormally with respect to their intended specifications. Secondly, we admit defeasible observations, i.e. observations that are subject to invalidation. Thirdly, we admit arbitrary priorities between abnormalities, what allows us to prefer some actions and/or observations while resolving conflicts.

Consistency-based Revision of Structured Belief Bases

In this paper we extend a consistency-based approach (originally introduced by Delgrande and Schaub) to belief revision for structured belief bases. We explicitly distinguish between observations, i.e., facts that an epistemic agent observes or is being told, and rules representing general knowledge about the considered world. When new information becomes available respective sets are being altered in a different way to preserve parts of knowledge during the revision process. Such an approach allows us to deal with difficult and complex scenarios, involving defeasible information and derivation filtering, with common-sense results.

Extending Knowledge Base Update into First-Order Knowledge Bases

In this paper, we introduce a new belief update operator. In contrast to all existing approaches to this problem, which are based on propositional or description logics, our proposal admits representing knowledge bases in full first-order logic. In consequence, our proposal is much more expressive than the existing ones.