The Journal of Logic Programming最新文献

In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads. We start by introducing the notion of an update $\text{P⊕U}$ of one logic program P by another logic program U. Subsequently, we provide a precise semantic characterization of $\text{P⊕U}$, and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates $\text{P}{}_1\text{⊕P}{}_2\text{⊕⋯,}$ defining a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, each describing a different state of our knowledge of the world. Different states may represent different time points or different sets of priorities or perhaps even different viewpoints. Consequently, program modules may contain mutually contradictory as well as overlapping information. The role of the dynamic program update is to employ the mutual relationships existing between different modules to precisely determine, at any given module composition stage, the declarative as well as the procedural semantics of the combined program resulting from the modules.

In this paper, we propose an argumentation-based semantic framework, called DAS, for disjunctive logic programming. The basic idea is to translate a disjunctive logic program into an argumentation-theoretic framework. One unique feature of our proposed framework is to consider the disjunctions of negative literals as possible assumptions so as to represent incomplete information. In our framework, three semantics preferred disjunctive hypothesis (PDH), complete disjunctive hypothesis (CDH) and well-founded disjunctive hypothesis (WFDH) are defined by three kinds of acceptable hypotheses to represent credulous, moderate and skeptical reasoning in artificial intelligence (AI), respectively. Furthermore, our semantic framework can be extended to a wider class than that of disjunctive programs (called bi-disjunctive logic programs). In addition to being a first serious attempt in establishing an argumentation-theoretic framework for disjunctive logic programming, DAS integrates and naturally extends many key semantics, such as the minimal models, extended generalized closed world assumption (EGCWA), the well-founded model, and the disjunctive stable models. In particular, novel and interesting argumentation-theoretic characterizations of the EGCWA and the disjunctive stable semantics are shown. Thus the framework presented in this paper does not only provide a new way of performing argumentation (abduction) in disjunctive deductive databases, but also is a simple, intuitive and unifying semantic framework for disjunctive logic programming.

The well-founded semantics has gained wide acceptance partly because it is a skeptical semantics. That is, the well-founded model posits as unknown atoms which are deemed true or false in other formalisms such as stable models. This skepticism makes the well-founded model not only useful in itself, but also suitable as a basis for other forms of non-monotonic reasoning. For instance, since algorithms to compute stable models are intractable, the atoms relevant to such algorithms can be limited to those undefined in the well-founded model. Thus, an engine that efficiently evaluates programs according to the well-founded semantics can be seen as a prerequisite to practical systems for non-monotonic reasoning. This paper describes the architecture of the Warren Abstract Machine (WAM)-based abstract machine underlying the XSB system. This abstract machine, called the SLG-WAM, uses tabling to efficiently compute the well-founded semantics of non-ground normal logic programs in a goal-directed way. To do so, the SLG-WAM requires sophisticated extensions to its core tabling engine for fixed-order stratified programs. A mechanism must be implemented to represent answers that are neither true nor false, and the delay and simplification operations – which serve to break and to resolve cycles through negation, must be implemented. We describe fully these extensions to our tabling engine, and demonstrate the efficiency of our implementation in two ways. First, we present a theorem that bounds the need for delay to those literals which are not dynamically stratified for a fixed-order computation. Second, we present performance results that indicate that the overhead of delay and simplification to Prolog – or tabled – evaluations is minimal.

We present a method to compute abduction in logic programming. We translate an abductive framework into a normal logic program with integrity constraints and show the correspondence between generalized stable models and stable models for the translation of the abductive framework. Abductive explanations for an observation can be found from the stable models for the translated program by adding a special kind of integrity constraint for the observation. Then, we show a bottom-up procedure to compute stable models for a normal logic program with integrity constraints. The proposed procedure excludes the unnecessary construction of stable models on early stages of the procedure by checking integrity constraints during the construction and by deriving some facts from integrity constraints. Although a bottom-up procedure has the disadvantage of constructing stable models not related to an observation for computing abductive explanations in general, our procedure avoids the disadvantage by expecting which rule should be used for satisfaction of integrity constraints and starting bottom-up computation based on the expectation. This expectation is not only a technique to scope rule selection but also an indispensable part of our stable model construction because the expectation is done for dynamically generated constraints as well as the constraint for the observation.

Abductive logic programming (ALP) and disjunctive logic programming (DLP) are two different extensions of logic programming. This paper investigates the relationship between ALP and DLP from the program transformation viewpoint. It is shown that the belief set semantics of an abductive program is expressed by the answer set semantics and the possible model semantics of a disjunctive program. In converse, the possible model semantics of a disjunctive program is equivalently expressed by the belief set semantics of an abductive program, while such a transformation is generally impossible for the answer set semantics. Moreover, it is shown that abductive disjunctive programs are always reducible to disjunctive programs both under the answer set semantics and the possible model semantics. These transformations are verified from the complexity viewpoint. The results of this paper turn out that ALP and DLP are just different ways of looking at the same problem if we choose an appropriate semantics.

This paper presents the framework of Abductive Constraint Logic Programming (ACLP), which integrates Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving. This integration of constraint solving into abductive reasoning facilitates a general form of constructive abduction and enables the application of abduction to computationally demanding problems. The paper studies the formal declarative and operational semantics of the ACLP framework together with its application to various problems. The general characteristics of the computation of ACLP and of its application to problems are also discussed. Empirical results based on an implementation of the ACLP framework on top of the CLP language of ECLiPSe show that ACLP is computationally viable, with performance comparable to the underlying CLP framework on which it is built. In addition, our experiments show the natural ability for ACLP to accommodate easily and in a robust way new or changing requirements of the original problem. ACLP thus combines the advantages of modularity and flexibility of the high-level representation afforded by abduction together with the computational effectiveness of low-level specialised constraint solving.

In 1969 Cordell presented his seminal description of planning as theorem proving with the situation calculus. The most pleasing feature of Green's account was the negligible gap between high-level logical specification and practical implementation. This paper attempts to reinstate the ideal of planning via theorem proving in a modern guise. In particular, the paper shows that if we adopt the event calculus as our logical formalism and employ abductive logic programming as our theorem proving technique, then the computation performed mirrors closely that of a hand-coded partial-order planning algorithm. Soundness and completeness results for this logic programming implementation are given. Finally the paper shows that, if we extend the event calculus in a natural way to accommodate compound actions, then using the same abductive theorem proving techniques we can obtain a hierarchical planner.

We introduce a logic programming language which supports hypothetical and counterfactual reasoning. The language is based on a conditional logic which enables to formalize conditional updates of the knowledge base. Due to the presence of integrity constraints, alternative revisions of the knowledge base may result from an update. We develop an abductive semantics which captures different evolutions of the knowledge base. Furthermore, we provide a goal-directed abductive proof procedure to compute the alternative solutions for a goal. We finally analyze our conditional programming language in the context of belief revision theory, and we establish a connection with Nebel's prioritized base revision.