Integrating Logic Programming Research Articles

The distribution semantics for normal programs with function symbols

The distribution semantics integrates logic programming and probability theory using a possible worlds approach. Its intuitiveness and simplicity have made it the most widely used semantics for probabilistic logic programming, with successful applications in many domains. When the program has function symbols, the semantics was defined for special cases: either the program has to be definite or the queries must have a finite number of finite explanations. In this paper we show that it is possible to define the semantics for all programs. We also show that this definition coincides with that of Sato and Kameya on positive programs. Moreover, we highlight possible approaches for inference, both exact and approximate.

An Abstract View on Modularity in Knowledge Representation

Modularity is an essential aspect of knowledge representation theory and practice. It has received substantial attention. We introduce model-based modular systems, an abstract framework for modular knowledge representation formalisms, similar in scope to multi-context systems but employing a simpler information-flow mechanism. We establish the precise relationship between the two frameworks, showing that they can simulate each other. We demonstrate that recently introduced modular knowledge representation formalisms integrating logic programming with satisfiability and, more generally, with constraint satisfaction can be cast as modular systems in our sense. These results show that our formalism offers a simple unifying framework for studies of modularity in knowledge representation.

Functions as passive constraints in LIFE

LIFE is a programming language proposing to integrate logic programming, functional programming, and object-oriented programming. It replaces first-order terms with ψ-terms, data structures that allow computing with partial information. These are approximation structures denoting sets of values. LIFE further enriches the expressiveness of ψ-terms with functional dependency constraints. We must explain the meaning and use of functions in LIFE declaratively, as solving partial information constraints. These constraints do not attempt to generate their solutions but behave as demons filtering out anything else. In this manner, LIFE functions act as declarative coroutines. We need to show that the ψ-term's approximation semantics is congruent with an operational semantics viewing functional reduction as an effective enforcing of passive constraints. In this article, we develop a general formal framework for entailment and disentailment of constraints based on a technique called relative simplification. We study its operational and semantical properties, and we use it to account for functional application over ψ-terms in LIFE.

A survey of the rule language RL/1

In this paper we introduced the RL project which aims to integrate logic programming, relational databases and constraint solving in a single relational framework. We gave a summary of the architecture of the RL/1 system and shortly discussed the characteristics of the RL/1 language. A target of the project has become to turn the RL/1 system into an industrial strength database interface.

Integrating logic programming into a data base course: views as rules in deductive relational data bases

article Free Access Share on Integrating logic programming into a data base course: views as rules in deductive relational data bases Author: Yuksel Uckan View Profile Authors Info & Claims ACM SIGCSE BulletinVolume 23Issue 1Mar. 1991 pp 184–191https://doi.org/10.1145/107005.107037Published:01 March 1991Publication History 0citation217DownloadsMetricsTotal Citations0Total Downloads217Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

LogScheme: Integrating logic programming into Scheme

This research aims to fully integrate logic programming into the programming language Scheme. We use a minimalist approach, based on the observation that the fundamental aspects of logic programming, nondeterminism and unification, are separable both in concept and in implementation. We have found that only two new primitive functions and one new special form need to be added to Scheme to achieve this integration. Using these primitives, we can write programs in the style of Scheme, Icon, Prolog, or any mixture thereof. We have found that a style of programming that uses both logical and functional techniques can be more powerful than the use of either technique alone. Because Scheme has side effects and continuations, this research addresses different problems and choices than previous research [2, 16, 19] on merging functional and logical languages.

Using logic programming to facilitate qualitative data analysis

Conventional word-processing programs and LOGLISP, a system which integrates logic programming with LISP, form the basis for QUALOG, a set of computer programs which facilitates the qualitative data analysis process. These programs aid the researcher's conceptual work in data analysis in four areas: 1) formatting the data as text files; 2) creating, organizing, and storing a knowledge base of information about the data; 3) testing knowledge-base queries relevant to working hypotheses for confirming and for disconfirming information; and 4) creating, organizing, and storing files for the researcher's memoranda.