A parallel implementation of Flat Concurrent Prolog

Flat Concurrent Prolog is a simple, practical, concurrent programming language which has an efficient uniprocessor implementation. This paper describes an initial parallel implementation of the language; it consists of an interpreter implemented on an Intel iPSC Hypercube. The parallel execution of concurrent logic programming languages involves many nontrivial implementation problems. Some of these problems are well known and have been treated extensively in the literature. The most difficult task is to integrate problem solutions in a coherent and efficient manner. The algorithm presented has been useful in providing insights into the major problems and includes a number of novel ideas to simplify implementation. It does not attempt to solve all the problems involved but rather provides a workable basis for current and future research. The algorithm is under ongoing refinement, simplification and improvement.     

Meta-interpreters and partial evaluation in Parlog

Since logic programs are executable specifications, the main concern of logic programming, producing efficient programs, is tangential to the mainstream of formal methods. A fashionable automatic transformation technique for producing efficient programs, partial evaluation, applied to the concurrent logic programming language Parlog is discussed.     

A fact dependency system for the logic programmer

We describe experimental work in logic programming for architects, leading to the setting up of a fact dependency system. The system operates as an interpreter of the user's instructions, storing his decision and the conclusions inferred from those decisions. Consistency from a user's point of view is automatically maintained. A separate introduction to the Prolog logic programming language is appended to this paper.     

Parallel branch-and-bound search in Parlog

The concurrent logic languages, of which Parlog is one, have been promoted as a new generation of software languages specifically designed for parallel programming. This paper investigates their application to a search problem commonly used as an illustration of artificial intelligence techniques, the 8-puzzle. It notes that programs written in the concurrent logic languages which do not pay attention to the parallelism can fall into two possible traps: either there is little real parallelism in them due to data dependencies, or there is too much parallelism and any practical architecture will be overwhelmed. A solution which controls the parallelism using user-defined priorities is proposed. This solution has the advantage of being architecture-independent.     

IAda: A language for robot programming based on Ada

We present a programming language for robots which we have implemented based on the Ada language. It is an interpreted language which permits dynamic configuration of software. It manipulates Ada tasks and subroutines. One of the Ada tasks is an inference engine of a logic programming language adapted to real-time constraints. We show how the conjunction of Ada tasks, to perform perception and action functions on the robot, to logic programs, for the control of these tasks, both manipulated by the IAda language, gives a powerful environment for robot programming.     

Communicating sequential processes for distributed constraint satisfaction

CSPCONS is a programming language that supports program execution over multiple Prolog processes with constraints. The language is an extended version of Csp-ii, a version of Prolog that supports channel-based communicating processes and TCP/IP communication, that is based on the CSP model introduced by Hoare. Cspcons inherits all the advanced features of Csp-ii and extends it by introducing constraint solving capabilities to the processes. In Cspcons each Prolog process has one or more solvers attached and each solver is independent from the others, following the original Csp-ii model, thus resulting to a communicating sequential constraint logic programming system. Such a model can facilitate greatly the implementation of distributed CLP applications. This paper describes the original Csp-ii system along with details of the extensions that resulted to the Cspcons system and presents an example demonstrating the applicability of the system to distributed constraint satisfaction problems.     

A join algorithm for combining AND parallel solutions in AND/OR parallel systems

When two or more literals in the body of a Prolog clause are solved in (AND) parallel, their solutions need to bejoined to compute solutions for the clause. This is often a difficult problem in parallel Prolog systems that exploit OR and independent AND parallelism in Prolog programs. In several AND/OR parallel systems proposed recently, this problem is side-stepped at the cost of unexploited OR parallelism in the program, in part due to the complexity of the backtracking algorithm beneath AND parallel branches. In some cases, the data dependency graphs used by these systems cannot represent all the exploitable indenpendent AND parallelism known at compile time.In this paper, we describe the compile time analysis for an optimizedjoin algorithm for supporting independent AND parallelism in logic programs efficiently without leaving any OR parallelism unexploited. We then discuss how this analysis can be used to yield very efficient runtime behavior. We also discuss problems associated with a tree representation of the search space when arbitrarily complex data dependency graphs are permitted. We describe how these problems can be resolved by mapping the search space onto the data dependency graphs themselves. The algorithm has been implemented in a compiler for parallel Prolog based on the Reduce-OR process model. The algorithm is suitable for the implementation of AND/OR systems on both shared and nonshared memory machines. Performance on benchmark programs exhibiting AND and OR parallelism on one shared memory machine and one message passing machine is presented.This work was supported in part by NSF Grants CCR-87-00988 and CCR-89-02496.A shorter version of this paper appears in theProceedings of NACLP 1990.     

A first exploration of PrologIII's capabilities

This paper explores PrologIII's capabilities through the study of two short but non-trivial applications that are chosen to show the advantages of constraint logic programming languages over conventional Prologs, especially for tackling combinatorial problems. The first application (manipulation of causal graphs) highlights the expressive power of the constraints on booleans and shows that constraint logic programming techniques provide great flexibility for the exploration of the domain of an application, and therefore significantly improve Prolog's prototyping capabilities. The second application (map colouring) demonstrates that a combined use of numerical constraints and constraints on trees of PrologIII enables one to improve solutions based on standard backtracking, through the use of simple heuristics. Such heuristics allow drastic reduction of the search space and yield good performance even for quite complex problems. The adaptability of the approach to other combinatorial problems is shown on several examples.     

LMNtal as a hierarchical logic programming language

LMNtal (pronounced “elemental”) is a simple language model based on hierarchical graph rewriting that uses logical variables to represent connectivity and membranes to represent hierarchy. LMNtal is an outcome of the attempt to unify constraint-based concurrency and Constraint Handling Rules (CHR), the two notable extensions to concurrent logic programming. LMNtal is intended to be a substrate language of various computational models, especially those addressing concurrency, mobility and multiset rewriting. Although the principal objective of LMNtal was to provide a unifying computational model, it is of interest to equip the formalism with a precise logical interpretation. In this paper, we show that it is possible to give LMNtal a simple logical interpretation based on intuitionistic linear logic and a flattening technique. This enables us to call LMNtal a hierarchical, concurrent linear logic language.     

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.     

A multicomputer garbage collector for a single-assignment language

An asynchronous garbage collector for a message-passing multiprocessor (multicomputer) is described. This combines Weighted Reference Counting (WRC) interprocessor collection and tracing intraprocessor collection to permit individual processors to reclaim local storage independently. A novel feature is the integration of Weighted Reference Counting collection and the communication algorithms required to support a global address space in a single assignment language. This significantly reduces communication overhead and space requirements attributable to garbage collection. In addition, techniques are described that avoid the creation of cyclic structures that cannot be reclaimed using WRC. Experimental studies performed in a concurrent logic programming system that incorporates the collector confirm its efficiency and the benefits of integrating garbage collector and language implementation.     

Near-Horn Prolog and beyond

Near-Horn Prolog is an extension of Prolog designed to handle disjunction and classical negation. The emphasis here is on minimal change from standard Prolog in regard to notation, derivation form, and speed of inner-loop computation. The procedure is optimized for the input program that is near-Horn; i.e., a program where almost all clauses are definite clauses. This paper goes beyond the near-Horn focus to report on the completeness of one version of nH-Prolog, along with soundness of the procedure. Completeness is important here not only for the usual reasons of guaranteed success on small problems and insight into the behavior of the procedure but also because we anticipate the introduction of negation-as-failure which requires conviction that a proof will be found if a proof exists.This research was supported in part by the U.S. Army Research Office under grants DAAG29-84-K-0072 and DAAL03-88-K-0082 and by NSF Grant IRI-8805696.     

约束逻辑编程中宽数据算术运算算法

提出了一种在利用约束逻辑编程生成RTL数据通路模拟矢量的方法中处理宽数据的新方法。该方法解决了现有CLP求解器所能处理的最大整数限制问题。该文设计并实现了宽数据加法、减法和乘法运算的分解算法,运算分解扩展了现有CLP的功能,使其能适应实际的数据通路设计。实验结果表明方法是有效的。     

A controlling scheme for Prolog through count terms

We propose an extension to Prolog called the count term for controlling Prolog execution. The purpose is to allow the programmers as well as the users to have greater flexibility in controlling the execution behavior of Prolog programs and for limiting the number of answers or proofs retrieved when Prolog is used as a database query language. Both syntax and operational semantics of the count term are defined. An implementation strategy based on WAM (Warren Abstract Machine) is provided. We analyze the possible meanings one might associate with the count term. The possible replacement of cut and fail by the count term is presented. The ease of analysis of programs with count terms is discussed.     

Extension table built-ins for prolog

The ET* algorithm is a complete evaluation strategy for Datalog programs, which are logic programs without function symbols. The ET* algorithm uses extension tables and depth-first iterative deepening to provide the evaluation of pure function-free logic programs as declarative specifications. Extension tables are a memo facility that the algorithm uses both to cut infinite derivation paths for complete evaluation and to optimise the evaluation of logic programs. The original implementation of the ET* algorithm incorporated extension tables as part of the Prolog database using the built-in predicates assert and retract. The advantage of implementing the extension table using the Prolog database is the portability of the ET* algorithm. There are several disadvantages, however, with this approach. One disadvantage is the cost associated with the built-in predicates assert and retract, which are known to be expensive operations in most current Prolog systems. Another disadvantage is the differences across implementations in the semantics that these built-ins provide for dynamic predicates. This paper presents an efficient implementation of extension tables as a global data structure in Prolog, which includes a set of built-in primitives for manipulating the extension table. The ET* algorithm is updated to reflect the utilisation of the global extension table data structure. The implementations of the ET* algorithm are compared using time and space performance on a variety of benchmark programs.     

Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization

Certain tasks, such as formal program development and theorem proving, fundamentally rely upon the manipulation of higher-order objects such as functions and predicates. Computing tools intended to assist in performing these tasks are at present inadequate in both the amount of knowledge they contain (i.e., the level of support they provide) and in their ability to learn (i.e., their capacity to enhance that support over time). The application of a relevant machine learning technique—explanation-based generalization (EBG)—has thus far been limited to first-order problem representations. We extend EBG to generalize higher-order values, thereby enabling its application to higher-order problem encodings.Logic programming provides a uniform framework in which all aspects of explanation-based generalization and learning may be defined and carried out. First-order Horn logics (e.g., Prolog) are not, however, well suited to higher-order applications. Instead, we employ Prolog, a higher-order logic programming language, as our basic framework for realizing higher-order EBG. In order to capture the distinction between domain theory and training instance upon which EBG relies, we extend Prolog with the necessity operator of modal logic. We develop a meta-interpreter realizing EBG for the extended language, Prolog, and provide examples of higher-order EBG.     

Implementation techniques for prolog databases

All clauses comprising a Prolog program are stored in a database from which they can be removed later. Other long-term data structures are represented as clauses and are also stored and removed from the same database. Implementation techniques for the manipulation of clauses are not well known, and a lack of information has led to incorrect and incomplete implementations. Further previously unresolved issues are apparent when considering the storage of compiled clauses. We describe the way database manipulations are performed in Prolog-X, a new compiler-based Prolog system. We also introduce a new technique for storing the source form of compiled clauses.