Multi-dimensional logic programming

2001

According to Dynamic Logic Programming (DLP), knowledge may be given by a sequence of theories (encoded as logic programs) representing different states of knowledge. These may represent time (eg in updates), specificity (eg in taxonomies), strength of updating instance (eg in the legislative domain), hierarchical position of knowledge source (eg in organizations), etc. The mutual relationships extant among states are used to determine the semantics of the combined theory composed of all the individual theories.

Logic Programming and Nonmonotonic Reasoning, 2005

Multidimensional dynamic logic programs are a paradigm which allows to express (partially) hierarchically ordered evolving knowledge bases through (partially) ordered multi sets of logic programs. They solve contradictions among rules in different programs by allowing rules in more important programs to reject rules in less important ones. This class of programs extends the class of dynamic logic program that provides meaning to sequences of logic programs. Recently the refined stable model semantics has fixed some counterintuitive behaviour of previously existing semantics for dynamic logic programs. However, it is not possible to directly extend the definitions and concepts of the refined semantics to the multidimensional case and hence more sophisticated principles and techniques are in order. In this paper we face the problem of defining a proper semantics for multidimensional dynamic logic programs by extending the idea of well supported model to this class of programs and by showing that this concept alone is enough for univocally characterizing a proper semantics. We then show how the newly defined semantics coincides with the refined one when applied to sequences of programs. This work was supported by project POSI/40958/SRI/01, FLUX, and by the European Commission within the 6th Framework P. project Rewerse, no. 506779.

Theoretical Computer Science, 1997

This paper introduces an extension of logic programming based on multi-dimensional logics, called MLP. In a multi-dimensional logic the values of elements vary depending on more than one dimension, such as time and space. The resulting logic programming language is suitable for modelling objects which involve implicit and/or explicit temporal and spatial dependencies. The execution of programs of the language is based on a resolution-type proof procedure called MSLD-resolution (for multi-dimensional SLD-resolution). The paper also establishes the declarative semantics of multi-dimensional logic programs, based on an extension of Herbrand models. In particular, it is shown that MLP programs satisfy the minimum model semantics. A novel multidimensional interface to MLP is also outlined; it can be used as a powerful development tool with the advantage of non-determinism inherent in logic programming.

Lecture Notes in Computer Science, 2001

This paper explores the applicability of the new paradigm of Multi-dimensional Dynamic Logic Programming to represent an agent's view of the combination of societal knowledge dynamics. The representation of a dynamic society of agents is the core of MIN ERVA [11], an agent architecture and system designed with the intention of providing a common agent framework based on the unique strengths of Logic Programming, hat allows the combination of several non-monotonic knowledge representation and reasoning mechanisms developed in recent years.

Science of Computer Programming, 2002

The main goal of this paper is to outline a methodology of programming in dynamic problem domains. The methodology is based on recent developments in theories of reasoning about action and change and in logic programming. The basic ideas of the approach are illustrated by discussion of the design of a program which verifies plans to control the reaction control

The Journal of Logic Programming, 1992

2000

At present, the search for specific information on the World Wide Web is faced with several problems, which arise on the one hand from the vast number of information sources available, and on the other hand from their intrinsic heterogeneity, since standards are missing. A promising approach for solving the complex problems emerging in this context is the use of multi-agent systems of information agents, which cooperatively solve advanced information-retrieval problems. This requires advanced capabilities to address complex tasks, such as search and assessment of information sources, query planning, information merging and fusion, dealing with incomplete information, and handling of inconsistency. In this paper, our interest lies in the role which some methods from the field of declarative logic programming can play in the realization of reasoning capabilities for information agents. In particular, we are interested to see in how they can be used, extended, and further developed for the specific needs of this application domain. We review some existing systems and current projects, which typically address information-integration problems. We then focus on declarative knowledge-representation methods, and review and evaluate approaches and methods from logic programming and nonmonotonic reasoning for information agents. We discuss advantages and drawbacks, and point out the possible extensions and open issues. Contents 1 Introduction 3 2 Intelligent Information Agents 4 3 Problems and Challenges 8 4 Systems and Frameworks 9 4.1 Cohen's Information System for Structured Collections of Text 10 4.2 Information Manifold 11 4.3 Carnot 11 4.4 InfoSleuth 12 4.5 Infomaster 12 4.6 COIN 13 7 Revision and Update 7.1 Revision Programs by Marek and Truszczyński 7.2 Update Rules as Logic Programs by Pereira et al. 7.3 Abductive Updates by Inoue and Sakama 7.4 Updates by Means of PLPs by Foo and Zhang 7.5 Dynamic Logic Programming by Alferes et al. 7.6 Updates and Preferences by Alferes and Pereira 7.7 Inheritance Programs and Updates 7.8 Revision of Preference Default Theories by Brewka 7.9 Arbitration 8 Quantitative Information 8.1 Disjunctive Programs with Weak Constraints by Buccafurri et al. 8.2 Weight Constraint Rules by Niemelä et al. 8.3 Weighted Logic Programming by Marek and Truszczyński 8.4 Probabilistic Programs by Subrahmanian et al. 9 Temporal Reasoning 9.1 Reasoning about Actions 9.2 Temporal Logics for BDI agents 9.3 LUPS, a Language for Specifying Updates 10 Evaluation 10.1 Preference Handling 10.2 Logic Programs with Quantitative Information 10.3 Revision and Update 10.4 Temporal Reasoning 11 Conclusion References

Lecture Notes in Computer Science, 2003

Over recent years, various semantics have been proposed for dealing with updates in the setting of logic programs. The availability of different semantics naturally raises the question of which are most adequate to model updates. A systematic approach to face this question is to identify general principles against which such semantics could be evaluated. In this paper we motivate and introduce a new such principle -the refined extension principle -which is complied with by the stable model semantics for (single) logic programs. It turns out that none of the existing semantics for logic program updates, even though based on stable models, complies with this principle. For this reason, we define a refinement of the dynamic stable model semantics for Dynamic Logic Programs that complies with the principle.

2004

Over recent years, various semantics have been proposed for dealing with updates in the setting of logic programs. The availability of different semantics naturally raises the question of which are most adequate to model updates. A systematic approach to face this question is to identify general principles against which such semantics could be evaluated. In this paper we motivate and introduce a new such principle-the refined extension principle-which is complied with by the stable model semantics for (single) logic programs. It turns out that none of the existing semantics for logic program updates, even though based on stable models, complies with this principle. For this reason, we define a refinement of the dynamic stable model semantics for Dynamic Logic Programs that complies with the principle. This work was partially supported by FEDER financed project FLUX (POSI/40958/SRI/2001) and by project SOCS (IST-2001-32530). Special thanks are due to Pascal Hitzler and Reinhard Kahle for helpful discussions.

1994

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