An environment for logic programming

The article is devoted to the analysis of the analysis of the experience of teaching the most common modern programming languages in the leading institutions of higher education in Ukraine. The purpose of the article is to analyze the experience of teaching the most common modern programming languages in leading Ukrainian higher education institutions in order to determine the relevance of knowledge provided by the leading institutions of higher education of Ukraine regarding the modern popularity of programming languages. Based on recent research and publications and work plans of leading institutions of higher education, the necessary information was gathered and an analysis of the experience of training the most common modern programming languages was conducted.

In this article, we consider Answer Set Programming (ASP). It is a declarative problem solving paradigm that can be used to encode a problem as a logic program whose answer sets correspond to the solutions of the problem. It has been widely applied in various domains in AI and beyond. Given that answer sets are supposed to yield solutions to the original problem, the question of “why a set of atoms is an answer set” becomes important for both semantics understanding and program debugging. It has been well investigated for normal logic programs. However, for the class of disjunctive logic programs, which is a substantial extension of that of normal logic programs, this question has not been addressed much. In this article, we propose a notion of reduct for disjunctive logic programs and show how it can provide answers to the aforementioned question. First, we show that for each answer set, its reduct provides a resolution proof for each atom in it. We then further consider minimal sets of rules that will be sufficient to provide resolution proofs for sets of atoms. Such sets of rules will be called witnesses and are the focus of this article. We study complexity issues of computing various witnesses and provide algorithms for computing them. In particular, we show that the problem is tractable for normal and headcycle-free disjunctive logic programs, but intractable for general disjunctive logic programs. We also conducted some experiments and found that for many well-known ASP and SAT benchmarks, computing a minimal witness for an atom of an answer set is often feasible.

It has been claimed that logic programming offers outstanding possibilities for new concepts in programming environments. But with the exceptions of the pioneering work of Shapiro on algorithmic debugging, Pereira's rational debugging and early work on expert systems from Imperial College, there has been little progress reported in the field of logic programming environments. This summary describes our current work on a generic software engineering shell for logic programming. We use reflection and the amalgamation of meta-level language with the object level to express and support the incremental character of specifying/programming. An important facet of the shell is that we formalize some aspects of programming methodology and provide heuristics for avoiding errors. These heuristics formalize what experienced programmers may already know.

Though inductive logic programming (ILP for short) should mean the “induction of logic programs”, most research and applications of this area are only loosely related to logic programming. In fact, the automatic synthesis of “true” logic programs is a difficult task, since it cannot be done without a lot of information on the sought programs, and without the ability to describe in a simple way well-restricted searching spaces. In this chapter, we argue that, if such knowledge is available, inductive logic programming can be used as a valid tool for software engineering, and we propose an integrated framework for the development, maintenance, reuse, testing, and debugging of logic programs.

We present a type system for linear constraints over the reals intended for reasoning about the input-output directionality of variables. Types model the properties of definiteness, range width or approximation, lower and upper bounds of variables in a linear constraint. Several proof procedures are presented for inferring the type of a variable and for checking validity of type assertions. We rely on theory and tools for linear programming problems, linear algebra, parameterized polyhedra and negative constraints. An application of the type system is proposed in the context of the static analysis of constraint logic programs. Type assertions are at the basis of the extension of well-moding from pure logic programming. The proof procedures (both for type assertion validity and for well-moding) are implemented and their computational complexity is discussed. We report experimental results demonstrating the efficiency in practice of the proposed approach.

We present a logic programming language where both problem domain and computational knowledge are expressed in logic. A logic program in this language consists of an object-program and a number of meta-programs. The object program, a collection of formulae, is a description of the problem domain of interest. The meta-programs, also just collections of formulae, specify desired computational behaviours. The object-program and meta-programs are compiled together to produce a single, efficient procedural logic (Prolog) program.

The paper reports about a case study in algebraic specification. It describes TransLog, a transformation tool for pure Prolog programs. TransLog supports the interactive transformation of (a part of) a program. Six transformation steps are supported: unfolding, folding, goal definition, argument permutation (an instance of goal replacement), goal switching and definition elimination. As much as possible, pure Prolog semantics are preserved. The tool is equipped with navigation options, which allow the user to switch from the current program to another program in a transformation sequence. TransLog has been implemented with the ASF+SDFMeta-environment. The paper focuses on the algebraic specification of the tool, not on the underlying theory of (logic) program transformation. The specification is presented at a global level, only the specification of the unfold and fold step is discussed more in depth.

The relationship between TMS and general logic programs is an important issue in non-monotonic logic programming. In this paper, we prove that, after we translate the TMS theory into a general logic program, the TMS's well-founded assignment (or extension) is equivalent to the corresponding general logic program's stable model. It means that TMS can be completely integrated into a non-monotonic logic programming environment.

Object-oriented programming has often been advocated as a means of improving and enhancing the facilities provided by a given programming environment. This thesis is concerned with an examination of the benefits of providing object-oriented facilities in the Logic programming language - Prolog. We consider these benefits from two different perspectives, specifically examining what benefits Prolog can gain from objects, and conversely, what benefits object-oriented programming can gain from Prolog. A previously proposed model of object execution in Prolog was used as the basis of this research. In implementing this proposal we have critically examined how well the model supports the principles of object-oriented programming, and in those areas which we consider deficient, identified alternatives for improving the model which have subsequently been implemented for the purposes of assessment. The name we have selected for our augmented system is ObLog, drawn from Ob(jects) in (Pro)log. We critically examine the suitability of ObLog in terms of object-oriented programming by implementing a series of example applications based on a Block World specification. The thesis concludes by proposing some areas in which further research might usefully be conducted.

Given a logic program P, the operator TP associated with P is closely related to the intended meaning of P. Given a first order language L that is generated by finitely many non-logical symbols, our aim is to study the algebraic properties of the set {TP|P is a general logic program in language L} with certain operators on it. For the operators defined in this paper the resulting algebraic structure is a bounded distributive lattice. Our study extends (to the case of general logic programs), the work of Mancarella and Pedreschi who initiated a study of the algebraic properties of the space of pure logic programs. We study the algebraic properties of this set and identify the ideals and zero divisors. In addition, we prove that our algebra satisfies various non-extensibility conditions.

Hybrid genetic algorithms are presented that use optimization heuristics and genetic techniques to outperform all existing programs for the timetabling problem. The timetabling problem is very hard (NP-complete) and a general polynomial time deterministic algorithm is not known. An artificial intelligence approach, in a logic programming environment, may be useful for such a problem. The decomposition and classification of constraints and the constraint ordering to obtain the minimization of the backtracking and the maximization of the parallelism are illustrated. The school timetabling problem is discussed in detail as a case study. The genetic algorithm approach is particularly well suited for this kind of problem, since there exists an easy way to assess a good timetable but not a well-structured automatic technique for constructing it. So, a population of timetables is created that evolves toward the best solutions. The evaluation function and the genetic operators are well separated from the domain-specific parts, such as the problem knowledge and the heuristics, i.e.. from the timetable builder. A fundamental issue and a general problem in the decision process and automated reasoning is how to efficiently obtain logic decisions under disjunctive constraints. Logic constraint satisfaction problems are in general NP-hard and a general deterministic polynomial time algorithm is not known. The present article illustrates an approach based on the hybridization of constrained heuristic search with novel genetic algorithm techniques. It compares favorably with the best-known programs to solve decisions problems under logic constraints. Complexity of the new algorithms and results of significant experiments are reported.

This paper proposes agent-based formulation of a Supply Chain Management (SCM) system for manufacturing firms. We model each firm as an intelligent agent, which communicates each other through the blackboard architecture in distributed artificial intelligence. To cope with the issues of conventional SCM systems, we employ the concept of information entropy, which represents the complexity of the purchase, sales, and inventory activities of each firm. Based on the idea, we implement an agent-based simulator to learn ‘good’ decisions via genetic programming in a logic programming environment. From intensive experiments, our simulator have shown good performance against the dynamic environmental changes.KeywordsSupply Chain ManagementInventory ActivityIntelligent AgentInformation EntropyManufacturing FirmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Based on a variable-free combinatory form of definite clause logic programs we outline a methodology and supporting program environment CombInduce for inducing well-moded logic programs from examples. The combinators comprise fold combinators for recursion on lists. The combinator form is distinguished by enabling piecewise composition of semantically meaningful program elements according to the compositional semantics principle. The principle of combining programs from combinators admits induction of programs without appealing to most-specific-generalization and predicate invention in contrast to prevailing ILP approaches. Moreover, the combinator form avoids confusing object and metavariables in the applied metalogic program environment. In addition useful algebraic rewriting rules can be formulated conveniently with the combinators.

Weighted graphs: A tool for studying the halting problem and time complexity in term rewriting systems and logic programming

Debugging on computer program is a complex cognitive activity. Although it is complex, it’s still one of the popular issues in computer programming task. It is a difficult task, which is to understand what the error is and how to solve such error? In computer programming the difficulty is to understand the Object-Oriented programming concept together with the programming logic. If the programming logic is incorrect, the program codes will have such error named as logic error and can caused highly maintenance cost. Logic error is a bug in a program that causes it to operate incorrectly, without terminating or crashing the program. It will produce unintended output or other behavior than what we are expecting. Method that use to develop a propose model Object Oriented Debugger is Unified Modeling Language (UML). It is the best choice model and suitable to design the Object Oriented Debugger which will be developed in an object oriented programming environment. The model will provide an ability to capture the characteristics of a system by using notations in the process of designing and implementing the system. The model of Object Oriented Debugger has been successfully implemented. This model has been developed using Unified Approach methodology, which consists of two methods such as Object-Oriented Analysis (OOA) and Object-Oriented Design (OOD). The model developed is to capture the structure and behavior of the Object Oriented Debugger by using the UML diagram. The model also can ease the readability of the documentation for the maintenance purposes. The design of the Object Oriented Debugger Model has been developed using the UML notation. It’s consisting of two parts that are object-oriented analysis and object-oriented design. All the developing and designing are based on the model in UML.