Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to application. The paper attempts to expose some of the challenges and prospects for the further development of the field.

In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in artificial intelligence. We consider exten- sions of the language of definite logic programs by classical (strong) negation, disjunc- tion, and some modal operators and show how each of the added features extends the representational power of the language.

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

this paper is at analyzing from various points of view the relationships betwee

Abduction is an important form of nonmonotonic reasoning allowing one to find explanations for certain symptoms or manifestations. When the application domain is described by a logical theory, we speak about logic-based abduction. Candidates for abductive explanations are usually subjected to minimality criteria such as subsetminimality, minimal cardinality, minimal weight, or minimality under prioritization of individual hypotheses. This paper presents a comprehensive complexity analysis of relevant decision and search problems related to abduction on propositional theories. Our results indicate that abduction is harder than deduction. In particular, we show that with the most basic forms of abduction the relevant decision problems are complete for complexity classes at the second level of the polynomial hierarchy, while the use of prioritization raises the complexity to the third level in certain cases.

This paper presents an approach to temporal reasoning in which prediction is deduction but explanation is abduction. It is argued that all causal laws should be expressed in the natural form effect if cause. Any given set of laws expressed in this way can be used for both forwards projection (prediction) and backwards projection (explanation), but abduction must be used for explanation whilst deduction is used for prediction. The approach described uses a shortened form of Kowalski and Sergot's Event Calculus and incorporates the assumption that properties known to hold must have explanations in terms of events. Using abduction to implement this assumption results in a form of default persistence which correctly handles problems which have troubled other formulations. A straightforward extension to SLD resolution is described which implements the abductive approach to explanation, and which complements the well-understood deductive methods for prediction. Introduction Temporal reason...

Our focus in this paper is on natural exogenous actions (Pinto [23]), namely those which occur in response to known laws of physics, like a ball bouncing at times determined by Newtonian equations of motion. The property of such actions that we wish to capture is that they must occur at their predicted times, provided no earlier actions (natural or agent initiated) prevent them from occurring. Because several such actions may occur simultaneously, we need a theory of concurrency. Because such actions may be modeled by equations of motion, we need to represent continuous time. This paper shows how to gracefully accommodate all these features within the situation calculus, without sacrificing the simple solution to the frame problem of Reiter [25]. One nice consequence of this approach is a situation calculus specification of deductive planning, with continuous time and true concurrency, and where the agent can incorporate external natural event occurrences into her...

We define formal notions of temporal domain and temporal database, and use them to survey a wide spectrum of temporal query languages. We distinguish between an abstract temporal database and its concrete representations, and accordingly between abstract and concrete temporal query languages. We also address the issue of incomplete temporal information. 1 Introduction A temporal database is a repository of temporal information. A temporal query language is any query language for temporal databases. In this paper we propose a formal notion of temporal database and use this notion in surveying a wide spectrum of temporal query languages. The need to store temporal information arises in many computer applications. Consider, for example, records of various kinds: financial [37], personnel, medical [98], or judicial. Also, monitoring data, e.g., in telecommunications network management [4] or process control, has often a temporal dimension. There has been a lot of research in temporal dat...

A family of extensions of SLDNF-resolution for normal abductive programs is presented. The main difference between our approach and existing procedures is the treatment of non-ground abductive goals. A completion semantics is given and the soundness and completeness of the procedures has been proven. The research presented here, provides a simple framework of abductive procedures, in which a number of parameters can be set, in order to fit the abduction procedure to the application under consideration.

. We present the SLDNFA procedure, which integrates two important nonmonotonic paradigms, negation as failure and abduction. The main difference between SLDNFA and existing approaches is the improved treatment of non-ground abductive goals. We present an extension for temporal reasoning based on Abductive Event Calculus. We show the power of the approach by applying it to planning and solving wellknown temporal reasoning problems. Interestingly, the procedure generates partial plans; the order of events is left unspecified when they do not interfere. 1