Multiple robot programming using a concurrent logic language

Advances in robotics has led to the cooperation of multiple robots among themselves and with their industrial automation environment. Efficient interaction with industrial robots thus becomes one of the key factors in the successful utilization of this modern equipment. When multiple manipulators have to be coordinated, there is a need for a new programming approach that facilitates and encompasses the needs of concurrency, synchronization, timing, and communication. Most robot languages have been developed with little attention being given to the integration of the robot with its environment. Currently, there is a gap between the robot capabilities, the task definition environment, and language facilities supplied to use robots.This paper analyzes the needs and then establishes that a concurrent logic programming approach is a step towards achieving a multi-robot knowledgeable task programming. In particular, the FCP dialect of concurrent Prolog is demonstrated, and analyzed.This research is partially supported by the Paul Ivanier Center for research in robots and production management.     

Profiling prolog programs

Profilers play an important role in the development of efficient programs. Profiling techniques developed for traditional languages are inadequate for logic programming languages, for a number of reasons: first, the flow of control in logic programming languages, involving back-tracking and failure, is significantly more complex than in traditional languages; secondly, the time taken by a unification operation, the principal primitive operation of such languages, cannot be predicted statically because it depends on the size of the input; and finally, programs may change at run-time because clauses may be added or deleted using primitives such as assert and retract. This paper describes a simple profiler for Prolog. The ideas outlined here may be used either to implement a simple interactive profiler, or integrated into Prolog compilers.     

Translating a Linear Logic Programming Language into Java

There have been several proposals for logic programming language based on linear logic: Lolli [8], Lygon [7], LO [3], LinLog [2], Forum [11], HACL [10]. In these languages, it is possible to create and consume resources dynamically as logical formulas. The efficient handling of resource formulas is, therefore, an important issue in the implementation of these languages. Lolli, Lygon, and Forum are implemented as interpreter systems; Lolli is on SML and λProlog, Lygon is on Prolog, Forum is on SML, λProlog and Prolog. However, none of them have been implemented in Java.In this paper, we describe the Prolog Café ¹ system which translates a linear logic programming language called LLP to Java via the LLPAM [12] [5], an extension of the standard WAM [16] [1] for LLP. LLP is a superset of Prolog and a subset of Lolli. The main difference from the first implementation [4] is resource compilation. That is to say, resource formulas are compiled into closures which consist of a reference of compiled code and a set of bindings for free variables. Calling these resources is integrated with the ordinary predicate invocation.Prolog Café is portable to any platform supporting Java and easily expandable with increasing Java's class libraries. In performance, on average, Prolog Café generate 2.2 times faster code for a set of classical Prolog benchmarks compared with jProlog.     

Logicon: An integration of prolog into icon

This paper describes the coupling of logic programming with Icon, which is a programming language aimed at string processing. Icon and Prolog have many similarities and their integration is feasible and desirable because the weaknesses of one can be compensated for by the strengths of the other. In our case, a Prolog interpreter was written as an Icon procedure that can be linked and called by an Icon program. This interpreter deals with all Icon data types and can be called in the context of the goal-directed evaluation of Icon. We give an example showing the power of this symbiosis between these two languages where a Prolog call in Icon is a generator and an Icon call in a Prolog clause is a built-in predicate.     

High-level characteristics of or- and independent and-parallelism in prolog

Although studies of a number of parallel implementations of logic programming languages are now available, their results are difficult to interpret due to the multiplicity of factors involved, the effect of each of which is difficult to separate. In this paper we present the results of a high-level simulation study of or- and independent and-parallelism with a wide selection of Prolog programs that aims to determine the intrinsic amount of parallelism, independently of implementation factors, thus facilitating this separation. We expect this study will be instrumental in better understanding and comparing results from actual implementations, as shown by some examples provided in the paper. In addition, the paper examines some of the issues and tradeoffs associated with the combination of and- and or-parallelism and proposes reasonable solutions based on the simulation data obtained.     

Oracle Semantics for Prolog

This paper proposes to specify semantic definitions for logic programming languages such as Prolog in terms of an oracle which specifies the control strategy and identifies which clauses are to be applied to resolve a given goal. The approach is quite general. It can be applied to Prolog to specify both operational and declarative semantics as well as other logic programming languages. Previous semantic definitions for Prolog typically encode the sequential depth-first search of the language into various mathematical frameworks. Such semantics mimic a Prolog interpreter in the sense that following the "leftmost" infinite path in the computation tree excludes computation to the right of this path from being considered by the semantics. The basic idea in this paper is to abstract away from the sequential control of Prolog and to provide a declarative characterization of the clauses to apply to a given goal. The decision whether or not to apply a clause is viewed as a query to an oracle which is specified from within the semantics and reasoned about from outside. This approach results in simple and concise semantic definitions which are more useful for arguing the correctness of program transformations and providing the basis for abstract interpretations than previous proposals.     

QD-Janus: A sequential implementation of Janus in Prolog

Janus is a language designed for distributed constraint programming. This paper describes QD-Janus, a sequential implementation of Janus in Prolog. The compiler uses a number of novel analyses and optimizations to improve the performance of the system. The choice of Prolog as the target language for a compiler, although unusual, is motivated by the following: (i) the semantic gap between Janus and Prolog is much smaller than that between Janus and, say, C or machine language—this simplifies the compilation process significantly, and makes it possible to develop a system with reasonable performance fairly quickly; (ii) recent progress in Prolog implementation techniques, and the development of Prolog systems whose speeds are comparable to those of imperative languages, indicates that the translation to Prolog need not entail a significant performance loss compared to native code compilers; and (iii) compilation to Prolog can benefit immediately from a significant body of work on, and implementations of, parallel Prolog systems. Our experience indicates that translation of logic programming languages to Prolog, accompanied by the development of good program analysis and optimization tools, is an effective way to quickly develop flexible and portable implementations with good performance and low cost.     

Meta-Level interpretation of constraint languages a case study: Logical primitives

This paper provides a detailed presentation of a Prolog-written meta-level interpreter for a constraint logic programming (CLP) language for expressing equalities and disequalities of finite trees, as well as non-negative integers (arities). The logical interpretation of the Prolog primitive functor (whose arguments are trees and arity) is used to illustrate the interactions among constraints pertaining to multiple domains. The paper’s objective is to provide insights about CLP language design and to present a modularized, incrementally-expandable meta-processor for this class of languages.     

Regulation management and logic programming

Regulations are pervasive in information systems. They manifest themselves as design rules, integrity constraints, deadlines, conventions, information disclosure requirements, policies, procedures, contracts, taxes, quotas and other statutes. Managing regulations is difficult. Regulations are complex, change frequently and rest on models of the real world that involve unusual vocabulary if not unusual concepts. Consequently, checking compliance with regulations is tedious and error-prone. Logic programming appears to provide a good framework for developing regulation management systems. Besides permitting arbitrary regulations to be modelled, it offers rapidity and ease of development, readability, incremental modifiability, extensibility and portability. These features are not provided by existing DP programming tools, database managers or conventional expert-system shells. This paper investigates the application of logic programming in a significant regulation management application: Workers' Compensation Insurance premium auditing. The insurance premium computation rules for the State of California were encoded as a large Prolog program. This application illustrates specific strengths and weaknesses of logic programming and Prolog in dealing with large-scale real-world regulations.     

If Prolog is the Answer, What is the Question? or What it Takes to Support AI Programming Paradigms

Knowledge programming, which makes use of the explicit representation and interpretation of knowledge to create intelligent programs, requires specialized languages and tools to help programmers. Prolog, an implementation of a logic programing language, provides some of these tools; it and other languages have been argued to be the "best" way to do such knowledge programming. This paper raises questions which suggest that any single paradigm of programming (e.g., logic programming or object-oriented programming) benefits by being integrated in a single environment with other paradigms of programming. Integration of these paradigms with each other, and within a flexible, user-friendly computing environment is also necessary. Such an environment must provide source level debugging and monitoring facilities, analysis and performance tuning tools, and an extended set of user communication programs.     

Multi-engine Horn Clause Prolog

We extend Horn Clause Prolog with two new primitives, new_engine (+Goal, +Answer, -Engine) and new_answer(+Engine, -Answer) for creating and exploring answer spaces of multiple interpreters (engines). We show that despite its ontological parsimony, the resulting language is comparable in practical expressiveness with conventional full Prolog, by allowing compact definitions for negation, if-then-else, all solution predicates, I/O and reflective meta-interpreters. While not really needed anymore as a workaround for the lack of expressiveness of pure Prolog, a form of dynamic database operations can be emulated as well, using the state of multiple engines. With multiple engines laying the foundation for multi-threaded execution models of logic programming languages, the surprising result that a minimal extension of Horn Clauses (with LD resolution) in fact bridges the gap to full Prolog, is significant as a basis for lightweight implementations of embedabble logic programming components. Our constructs have been used in the design of small footprint mobile code interpreters for a commercial multi-threaded agent programming language - Jinni - available from http://www.binnetcorp.com/Jinni”.     

Evaluation of the Effectiveness of Prolog for a CAD Application

Fifth-generation computers will probably be based on logic programming languages like Prolog rather than on Fortran-type languages. Are such languages suitable to 3-D CAD applications?     

PARLOG and its applications

The key concepts of the parallel logic programming language PARLOG are introduced by comparing the language with Prolog. Some familiarity with Prolog and with the concepts of logic programming is assumed. Two major application areas of PARLOG, systems programming and object-oriented programming, are illustrated. Other applications are briefly surveyed     

High-performance logic programming with the Aquarius Prologcompiler

Aquarius Prolog, a high performance compiler designed and built to test the hypothesis that Prolog can be implemented as efficiently as an imperative language by compiling the more powerful features of logic programming only where they are needed, and then only in the simplest form, is described. The authors begin with some background on logic programming and then discuss the Prolog language in more detail. They present an overview of their compiler, giving its structure and the principles underlying its high performance. They compare their system with two popular high-performance commercial systems and with two implementations of C and conclude with an overview of ways to extend this work     

Focal structures and information types in Prolog

Several studies have suggested that the mental structures of programmers of procedural languages have a close relationship with a model of structural knowledge related to functional information known as programming Plans. It also has been claimed that experienced programmers organize this representation in a hierarchical structure where some elements of Plans are focal or central to them. However, it is not clear that this is the case for other types of programming languages, especially for those which are significantly different from the procedural paradigm.The study reported in this paper investigates whether these claims are true for Prolog, a language which has important differences to procedural languages. Prolog does not have obvious syntactic cues to mark blocks of code (begin/end, repeat/until, etc). Also, its powerful primitives (unification and backtracking) and the extensive use of recursion might influence how programmers comprehend Prolog code in a significant way.The findings of the study suggest that Plans and functional information are important for Prolog programmers, but that there is also at least another model of structural knowledge valid for this language. This model of structural knowledge, Prolog schemas, is related to data structure information and it seems that a hierarchical organisation that highlights the relevance of some of its elements as focal is valid for Prolog. These results support the view that comprehension involves the detection of varying aspects of the code and that each of the structures related to these aspects might have their own organization and hierarchical relations.     

Bounded quantifications for iteration and concurrency in logic programming

The only means for repetition in most logic programming languages, including Prolog, is recursion. Definite iteration is introduced in logic programming languages through the bounded quantification construct. Firstly, it is claimed that this construct is often, though not always, more natural than recursion for expressing relations that involve repetition. In particular, programs involving arrays and similar data structures are significantly simplified. Secondly, it is argued that bounded quantifications should be efficiently implementable on sequential computers and have a high potential for running in parallel, particularly on computers supporting the SPMD model of computation. Bounded quantifications are compared with related constructs from other languages, including the definite loops of imperative languages and the array comprehensions of recent functional languages.     

Relating the implementation techniques of functional and functional logic languages

Functional logic languages are declarative programming languages that integrate the programming paradigms of functional and logic languages within a single framework. They are extensions of functional languages with principles derived from logic programmingNarrowing, the evaluation mechanism of functional logic languages, can be defined as a generalization ofreduction, the evaluation mechanism of purely functional languages. The unidirectional pattern matching, which is used for parameter passing in functional languages, is simply replaced by the bidirectionalunification known from logic programming languages. We show in this paper, how to extend a reduction machine, that has been designed for the evaluation of purely functional programs to a machine that performs narrowing. The necessary extensions concern the realization of unification and backtracking, for which we fall back upon the methods of Warren’s Prolog engine.²¹⁾ The narrowing machine embodies an optimized treatment of deterministic computations. A complete specification of the reduction and the narrowing machine and of the translation of a sample language into abstract machine code is given. Comparative results of a C-implementation of the reduction and the narrowing machine show that the time overhead of the more complex narrowing evaluation is, in general, less than 10% of the reduction evaluation.     

Efficient Interval Linear Equality Solving in Constraint Logic Programming

Existing interval constraint logic programming languages, such as BNR Prolog, work under the framework of interval narrowing and are deficient in solving systems of linear constraints over real numbers, which constitute an important class of problems in engineering and other applications. In this paper, we suggest to separate linear equality constraint solving from inequality and non-linear constraint solving. The implementation of an efficient interval linear constraint solver, which is based on the preconditioned interval Gauss-Seidel method, is proposed. We show how the solver can be adapted to incremental execution and incorporated into a constraint logic programming language already equipped with a non-linear solver based on interval narrowing. The two solvers share common interval variables, interact and cooperate in a round-robin fashion during computation, resulting in an efficient interval constraint arithmetic language CIAL. The CIAL prototypes, based on CLP(R), are constructed and compared favorably against several major interval constraint logic programming languages.     

JavaLog: a framework-based integration of Java and Prolog for agent-oriented programming

Intelligent agent development has imposed new challenges on the necessary language support. Object-oriented languages have been proposed as an appropriate tool, although logic-oriented languages are more adequate for managing mental attitudes. Multi-paradigm languages supporting encapsulation of actions, hiding of private knowledge and flexible manipulation of knowledge are, certainly, a good alternative for programming agents. However, a unique language to support flexible and efficient development of multi-agent systems confronts with the tradeoffs imposed by expressive power, efficiency and support technology. An alternative to conciliate these tradeoffs is not to think about a single language but an incrementally compatible family of agent-oriented multi-paradigm languages. In this work we present an approach based on object-oriented framework technology for integrating object and logic paradigms in such a way that new language features can be incrementally added to the core language. This core language is based on logic modules integrated as object abstractions in the object paradigm. JavaLog is a materialization of this framework integrating Java and Prolog. This core was extended to provide multi-threading support, mobility and temporal-logic operators to Prolog. MoviLog, the mobile part of the family provides a novel mobility mechanism, reactive mobility by failure, which enables virtual Prolog databases distributed across Web sites.