Prolog Programs Research Articles

Observational studies about novices interacting in a Prolog environment based on tools

This paper examines the general issues related to the interaction of novices in a Prolog environment based on tools to support both the operational and the declarative aspects of the language. The tools are based on graphical representations for the Prolog program and for Prolog execution of programs. This research aims to investigate how students come to understand and integrate these different views of Prolog and how the tools affect their understanding of the language. Observations conducted during this study show that the novices had an initial conceptual model of the virtual machine. Such models guided their interpretation of the Prolog program and of the behavior of the machine. Their misunderstandings may be interpreted as having resulted from their current models. These models evolved during their interaction within the environment. The type of feedback created by the tools provided changes in the students perspective related to the knowledge being represented (program) and the interlocutor in the process (virtual machine), which led them to the construction of the conceptual model of the language.

Automata-driven indexing of prolog clauses

Indexing Prolog clauses is an important optimization step that reduces the number of clauses on which unification will be performed and can avoid the pushing of a choice point. It is quite desirable to increase the number of functors used in indexing as this can considerably reduce the size of the filtered set. However, doing so can cause an enormous increase in code size and running time if indexing is done naively. This paper describes new and efficient indexing techniques that utilize all the functors in the clause heads and the goal. The salient feature of these techniques is that the selected clause head unifies (modulo nonlinearity) with the goal. In unification (modulo nonlinearity) all the variables in the terms being unified are assumed to be unique, so the only operation performed is of matching their constant portions. So use of our indexing techniques can result in sharper discrimination, fewer choice points, and reduced backtracking. These techniques have been incorporated into a Prolog compiler and using this compiler the run-time performance of a broad spectrum of Prolog programs has been improved.

First-order theories for pure Prolog programs with negation

First-order theories for pure Prolog programs with negation

A visual debugger for pure Prolog

This work involves the design and coding of an interpreter for pure Prolog and building a visual debugger for it. Most of the available Prolog interpreters contain some tracing facilities. They do not incorporate, however, a comprehensive visual debugger. The interpreter performs the operations of parsing, unification, resolution, and search in a state-space representation of the Prolog program. The visual debugger incorporates the graphical visualization and the manipulation of the SLD resolution tree. The user visualizes the execution of a pure Prolog program and interacts with the program inside a windowing environment. The program execution may be viewed without interruption or the execution can be stopped at any moment in time. At this point the “snapshot” can be scrutinized with the help of break-points and data displays. This software aims itself to those who wish to observe the actual process of predicate unification, substitution, resolution and goal matching in a Prolog program and to visually interact with the interpreter using a highly friendly and pleasing user interface. An advanced feature, referred to as debugging on the tree, provides the user with the ability to insert break-points directly on the SLD tree, to choose the path of execution, and change the search mode. The resultant search algorithm can be a mixture of depth-first and breadth-first search, avoiding infinite search paths.

First-order theories for pure Prolog programs with negation

The standard theory of logic programming is not applicable to Prolog programs even not to pure code. Modifying the theory to take account of reality more is the motivation of this article. For this purpose we introduce the l-completion and the inductive extension of a logic program. Both are first-order theories in a language with operators for success, failure and termination of goals. The l-completion of a logic program is a sound and complete axiomatization of the Prolog depth-first search under certain natural conditions; the inductive extension of the l-completion is a suitable theory for proving termination and equivalence of pure Prolog programs with negation.

Selecting among rules induced from a hurricane database

Rule induction can achieve orders of magnitude reduction in the volume of data descriptions. For example, we applied a commercial tool (IXLtm) to a 1,819 record tropical storm database, yielding 161 rules. However, the human comprehension goals of Knowledge Discovery in Databases may require still more orders, of magnitude. We present a rule refinement strategy, partly implemented in a Prolog program, that operationalizes interestingness into performance, simplicity, novelty, and significance. Applying the strategy to the induced rulebase yielded 10 genuinely interesting rules.

Simply logical: intelligent reasoning by example

From the Publisher: An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material. A unique advantage to this work is the combination of AI, Prolog and Logic. Each technique is accompanied by a program implementing it. Seeks to simplify the basic concepts of logic programming. Contains exercises and authentic examples to help facilitate the understanding of difficult concepts.

Computer Aided Educational Expert Technique.(Dept.E)

This paper describes a fully operational intelligent tutoring system which incorporates artificial intelligence techniques to produce a computer aided instruction system for computer configuration and troubleshooting in the domain of education, Much of its logical or infer encing capabilities are derived from human expert in conjunction with uses of simulation models. The system also includes a high tuned structure for allowing students to communicate through a natural language dialogue and colour graphics. Although the system is extremely large, it is sufficiently fast to be exercised in a training or classroom environment. The system goes through three phases: teaching phase, diagnostic phase, and evaluation phase. It is devolped to be a step for improving the quality of education at low cost and to overcome the problems in existing approaches. It is divided into modules, each one contains a collection of PROLOG programs and produces several windows and about 150 colour screens. Students were considered as a model to be a major thrust in the system. Experiments had been carried out to study the effectiveness of the system and the iprovements in the aca- demic achievement of students. Results concluded the positive effect of the system where the experimental groups, which used the computer aided educational expert technique, had significantly greater gains than the control groups, which used traditional techniques, and had poor progress.

Reasoning about Prolog programs: From modes through types to assertions

Abstract We provide here a systematic comparative study of the relative strength and expressive power of a number of methods for program analysis of Prolog. Among others we show that these methods can be arranged in the following hierarchy: mode analysis ⇒ type analysis ⇒ monotonic properties ⇒ nonmonotonic run-time properties. We also discuss a method allowing us to prove global run-time properties.

Applying Prolog programming techniques

Much of the skill of Prolog programming comes from the ability to harness its comparatively simple syntax in sophisticated ways. It is possible to provide an account of part of the activity of Prolog programming in terms of the application of techniques—standard patterns of program development which may be applied to a variety of different programming problems. Numerous researchers have attempted to provide formal definitions of Prolog techniques but there has been little standardization of the approach and the computational use of techniques has been limited to small portions of the programming task. We demonstrate that techniques knowledge can be used to support programming in a wide variety of areas: editing, analysis, tracing, transformation and techniques acquisition. We summarize the main features of systems implemented by the authors for each of these types of activity and set these in the context of previous work, using a standard style of presentation. We claim that a techniques-based system which integrates these features would be worth more than the sum of its parts, since the same techniques knowledge can be shared by the different subsystems.

A matrix model for the flow of control in prolog programs with applications to profiling

AbstractIn Prolog the flow of control is relatively complex; four counts (which we call currents)—call, fail, succeed and redo—and two intrinsic properties—the failure and the ‘alternative’ probabilities—can be associated with every literal in a clause body. In this work we describe a new matrix model where those currents and properties are related at the literal, clause and predicate definition levels. This model is useful for predicate classification, execution profiling and program debugging. The application to profilers is discussed in detail.

A Hybrid Expert System for VOLT/VAR Control in Power Systems

The paper presents a hybrid expert system, based on the joint use of a best-first search strategy and a neural network model of the power system voltage/reactive power relationship, which assists the system operator in the voltage profile control task. A Prolog program, based on the proposed strategy, has been developed. Many tests carried out on standard systems have shown promising results.

A Formal Model of Legal Argumentation

Abstract. The paper gives a formal reconstruction of some fundamental patterns of legal reasoning, intended to reconcile symbolic logic and argumentation theory. Legal norms are represented as unidirectional inference rules which can be combined into arguments. The value of each argument (its qualification as justified, defensible, or defeated) is determined by the importance of the rules it contains. Applicability arguments, intended to contest or support the applicability of norms, preference arguments, purporting to establish preference relations among norms, and interpretative arguments are also formalised. All those argument types are connected in a unitary model, which relates legal reasoning to the indeterminacy of legal systems, intended as the possibility to develop incompatible defensible arguments. The model is applied to permissive norms and normative hierarchies, and is implemented in a Prolog program.

Three systems for cryptographic protocol analysis

Three experimental methods have been developed to help apply formal methods to the security verification of cryptographic protocols of the sort used for key distribution and authentication. Two of these methods are based on Prolog programs, and one is based on a general-purpose specification and verification system. All three combine algebraic with state-transition approaches. For purposes of comparison, they were used to analyze the same example protocol with a known flaw.

Cut and side-effects in a data-driven implementation of Prolog

A number of data-driven execution models have been proposed for parallel execution of logic programs8, 12, 9, 3) LogDf is an abstract data-driven execution model for pure logic programs3) which has shown promising performance during simulations. However, the original model lacks support for extra logical features such as cut and side-effects, which are needed to execute Prolog programs. This paper describes a scheme that has been incorporated into the LogDf model to support cut and side-effects. The main component of the scheme is a data structure, called a flat, non-strict S-Stream, which maintains strict ordering of multiple solutions, and, at the same time, allows simultaneous modification by several actors. This ordering corresponds to the order in which solutions would be produced in a sequential system and is necessary to implement cut and side-effects. The correct synchronization and ordering of operations on the cells of an S-Stream is ensured by the use of I-structure memory.1) The descriptor based token coloring mechanism in the LogDf provides convenient support for maintaining the scope information associated with cuts. An efficient garbage collection strategy is also proposed.

On the occur-check-free PROLOG programs

In most PROLOG implementations, for efficiency occur-check is omitted from the unification algorithm. This paper provides natural syntactic conditions that allow the occur-check to be safely omitted. The established results apply to most well-known PROLOG programs, including those that use difference lists, and seem to explain why this omission does not lead in practice to any complications. When applying these results to general programs, we show their usefulness for proving absence of floundering. Finally, we propose a program transformation that transforms every program into a program for which only the calls to the built-in unification predicate need to be resolved by a unification algorithm with the occur-check.

A declarative approach for first-order built-in's of Prolog

We provide here a framework for studying Prolog programs with various built-in's that include arithmetic operations, and such metalogical relations likevar andground. To this end we propose a new, declarative semantics and prove completeness of the Prolog computation mechanism w.r.t. this semantics. We also show that this semantics is fully abstract in an appropriate sense. Finally, we provide a method for proving termination of Prolog programs with built-in's which uses this semantics. The method is shown to be modular and is illustrated by proving termination of a number of programs including the unify program of Sterling and Shapiro [17].

IDNAF prolog

The relationship between resolution theorem proving and graph search is complicated by control statements that affect the program's meaning. An interesting case arises when combining Prolog interpreters implementing the cut and iterative deepening search. Because these features are dependent, an interpreter that naively combines both features gives unintended answers to queries. In particular, negation as failure breaks down. One solution is to use sound negation, but many certain AI problems use both sound negation and negation as failure. As well, when debugging large Prolog programs, it is useful to be able to execute the same program with and without iterative deepening, even though the program contains cuts. Besides generating unintended answers, the Prolog interpreter naively combining cuts and iterative deepening also generates redundant answers. Here, we show how to avoid both.

Discrete sequence prediction and its applications

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We present a simple and practical algorithm (TDAG) for discrete sequence prediction, verify its performance on data compression tasks, and apply it to problem of dynamically optimizing Prolog programs for good average-case behavior.

Rich Classes Inferrable from Positive Data: Length-Bounded Elementary Formal Systems

Inductive inference from positive data is shown to be remarkably powerful using the framework of elementary formal systems. An elementary formal system, EFS for short, is a kind of logic program on Σ+ consisting of finitely many axioms. Any context-sensitive language is definable by a restricted EFS, called a length-bounded EFS. Length-bounded EFSs with at most n axioms are considered, and it is shown that inductive inference from positive data works successfully for their models as well as for their languages. From this it follows that any class of logic programs, such as Prolog programs, corresponding to length-bounded EFSs can be inferred from positive facts.