Bounded-wait merge in Shapiro’s Concurrent Prolog

InA Subset of Concurrent Prolog and Its Interpreter (1983), E. Y. Shapiro introduces the language Concurrent Prolog. In his presentation, the problem of guaranteeing bounded-waiting during a merge operation is used as a programming example. Solutions are proposed for binary and n-ary merges. The solutions are, however, completely dependent on specific operational characteristics of a Concurrent Prolog machine or interpreter. This paper presents an alternate approach in which the property of bounded-waiting is incorporated into the semantics of the programs, demonstrable given only the computational model of the language. The solution strategy is to utilize the familiar systems programming techniques of block-on-input and busy-wait. This approach requires that the language be augmented with a metalogical predicate analogous to thevar(_) predicate of Sequential Prolog. The resultant programs are interesting and illustrative examples of Concurrent Prolog as a programming language.     

Translating production rules into a forward reasoning Prolog program

Several attempts have been made to design a production system using Prolog. To construct a forward reasoning system, the rule interpreter is often written in Prolog, but its execution is slow. To develop an efficient production system, we propose a rule translation method where production rules are translated into a Prolog program and forward reasoning is done by the translated program. To translate the rules, we adopted the technique developed in BUP, the bottom-up parsing system in Prolog. Man-machine dialogue functions were added to the production system and showed the potential of our method to be applied to expert systems.     

Bounded buffer communication in Concurrent Prolog

Concurrent Prolog is a logic-based parallel programming language which was designed and implemented experimentally in Prolog by E. Shapiro. In this paper, we examine the expressive power of communication mechanism based on shared logical variables and show that the language can express both unbounded buffer and bounded buffer stream communication only by shared logical variables and read-only annotation. We will also present the abstraction technique which hides buffer control such as unbounded and bounded inside stream operations and makes it invisible from user programs.     

Specialisation of Prolog and FCP programs using abstract interpretation

This paper presents an approach to specialising logic programs which is based on abstract interpretation. Program specialisation involves two stages, the construction of an abstract computation tree and a program construction stage. For the tree construction stage, abstract OLDT resolution is defined and used to construct a complete and finite tree corresponding to a given logic program and a goal. In the program construction stage, a specialised program is extracted from this tree. We focus on two logic programming languages: sequential Prolog and Flat Concurrent Prolog. Although the procedural reading of concurrent logic programs is very different from that of sequential programs, the techniques presented provide a uniform approach to the specialisation of both languages. We present the results for Prolog rigorously, and extend them less formally to Flat Concurrent Prolog. There are two main advantages of basing program specialisation on abstract interpretation. Firstly, termination can be ensured by using abstract interpretations over finite domains, while performing a complete flow analysis of the program. Secondly, correctness of the specialised program is closely related to well-defined consistency conditions on the concrete and abstract interpretations.     

Set abstraction—An extension of all solutions predicate in logic programming language

The concept of set abstraction is introduced as a simple analogy of that of lambda abstraction in the theory of lambda calculus. The set abstraction is concerned with two extensions concerning Prolog language features: “set expression” and “predicate variable.” It has been argued in the literature that the set expression extension to Prolog does really contribute to the power of the language, while the extension of predicate variables does not add anything to Prolog. Combining these two concepts of extensions to Prolog, we define “set abstraction” as the set expression in which predicate variables are allowed as data objects. In other words, the set abstraction gets involved in the higher order predicate logic. By showing some application examples, it is demonstrated that with the help of predicate variables set abstractions can nicely handle the issues of the second order predicate logic. Further, the implementation programs written in Prolog and Concurrent Prolog are presented.     

P-Prolog: A parallel logic language based on exclusive relation

This paper presents a parallel logic programming language named P-Prolog which is being developed as a logic programming language featuring both and- and or-parallelism. Compared with the other parallel logic programming languages, syntactic constructs such as read-only annotation,⁶⁾ mode declaration²⁾ and communication constraints⁷⁾ are not used in P-Prolog. A new concept introduced in P-Prolog is the exclusive relation of guarded Horn clauses. Advances included in P-prolog. are:

1. The synchronization mechanism can determine the direction of data flow dynamically.
2. Guarded Horn clauses can be interpreted as eitherdon’t care nondeterminism ordon’t know non-determinism.

A prototype interpreter of P-Prolog has been implemented in C-Prolog. We are now implementing a P-Prolog interpreter in the C language.     

Prolog-ELF incorporating fuzzy logic

Prolog-ELF incorporating fuzzy logic and several useful functions into Prolog has been implemented as a basic language for building knowledge systems with uncertainty or fuzziness. Prolog-ELF inherits all the desirable basic features of Prolog. In addition to assertions with truth-values between 1.0 and 0.5 (0 for exceptional cases), fuzzy sets can be very easily manipulated. An application of fuzzy logical database is illustrated.     

Architecture of a Reduction-Based Parallel Inference Machine: PIM-R

This paper presents a highly parallel machine architecture for logic programs. We propose a Reduction-Based Parallel Inference Machine: PIM-R and describe the parallel execution mechanisms for PIM-R to run Prolog and Concurrent Prolog programs and sofware simulation results. PIM-R uses the structure-copy method. It also uses the only reducible goal copy method, a unique process-structuring method, and the reverse compaction method to decrease the amount, of copying and various copyingrelated operations and the number of packets passing through the network. PIM-R architecture features include the distributed shared memory for Concurrent Prolog, network nodes for efficient packet distribution, and the structure memory to store a part of structured data for reducing the copying overhead.     

Compiling OR-parallelism into AND-parallelism

This paper suggests a general method for compiling OR-parallelism into AND-parallelism. An interpreter for an AND/OR-parallel language written in the AND-parallel subset of the language induces a source-to-source transformation from the full language into the AND-parallel subset. This transformation can be identified and implemented as a special purpose compiler or applied using a general purpose partial evaluator. The method is demonstrated to compile a variant of Concurrent Prolog into an AND-parallel subset of the language called Flat Concurrent Prolog (FCP). It is also shown applicable to the compilation of OR-parallel Prolog to FCP. The transformation identified is simple and efficient. The performance of the method is discussed in the context of programming examples. These compare well with conventionally compiled Prolog programs.     

Uses of Prolog in implementation of expert systems

Prolog is becoming a popular language in A. I. applications and particularly in the implementation of knowledge based expert systems. We have identified three different uses of Prolog: (1) building expert systems directly in ordinary Prolog, (2) using Prolog as the implementation language for an higher level of interpretation, and (3) extending Prolog with suitable features and directly using it. In this paper, we define the three uses in more details, compare them, and cite some concrete examples.     

Design and simulation of a sequential Prolog machine

Prolog-X is an implemented portable interactive sequential Prolog system in which clauses are incrementally compiled for a virtual machine called the ZIP Machine. At present, the ZIP Machine is emulated by software, but it has been designed to permit easy implementation in microcode or hardware. Prolog-X running on the software-based emulator provides performance comparable with existing Prolog interpreters. To demonstrate its efficiency, compatibility, and comprehensiveness of implementation, Prolog-X has been used to compile and run several large applications programs. Several novel techniques are used in the implementation, particularly in the areas of the representation of therecordx database, the selection of clauses, and the compilation of arithmetic expressions.     

A Prolog technology theorem prover

An extension of Prolog, based on the model elimination theorem-proving procedure, would permit production of a logically complete Prolog technology theorem prover capable of performing inference operations at a rate approaching that of Prolog itself.     

How to invent a Prolog machine

In this paper we study the compilation of Prolog by making visible hidden operations (especially unification), and then optimizing them using well-known partial evaluation techniques. Inspection of straightforward partially evaluated unification algorithms gives an idea how to design special abstract machine instructions which later form the target language of our compilation. We handle typical compiler problems like representation of terms explicitly. This work gives a logical reconstruction of abstract Prolog machine code, and represents an approach of constructing a correct compiler from Prolog to such a code. As an example, we are explaining the unification principles of Warren’s New Prolog Engine within our framework.     

The occur-check problem in Prolog

We present a method for preprocessing Prolog programs so that their operational semantics will be given by the first-order predicate calculus. Most Prolog implementations do not use a full unification algorithm, for efficiency reasons. The result is that it is possible to create terms having loops in them, whose semantics is not adequately described by first-order logic. Our method finds places where such loops may be created, and adds tests to detect them. This should not appreciably slow down the execution of most Prolog programs.     

Sequential Prolog machine PEK

The sequential Prolog machine PEK currently under development is described. PEK is an experimental machine designed for high speed execution of Prolog programs. The PEK machine is controlled by horizontal-type microinstructions. The machine includes bit slice microprocessor elements comprising a microprogram sequencer and ALU, and possesses hardware circuits for unification and backtracking. The PEK machine consists of a host processor (MC68000) and a backend processor (PEK engine). A Prolog interpreter has been developed on the machine and the machine performance evaluated. A single inference can be executed in 89 microinstructions, and execution speed is approximately 60–70 KLIPS.     

Temporal logic based hardware description and its verification with Prolog

Techniques of hierarchical specification and verification of hardware with temporal logic and Prolog are presented by example. Both hardware designs in gates and state-diagrams are translated into a relation between the present and the next state, which is represented in Prolog.¹⁾ Specifications are constructed by temporal logic that can express state sequences (e.g. timing diagrams) easily and also are translated into a relation between the present and the next state in Prolog. The verification method is based upon the temporal logic decision procedure in Ref. 2) and, referring to the relation tables between the present state and the next state, the verifier can reason in both directions—forward and backward in temporal sequences. Prolog has very powerful pattern matching, and its automatic backtracking capabilities facilitate easy-to-write verifier programs. It is concluded that a total verification system handling various design levels can be constructed with temporal logic and Prolog.     

Knowledge information processing language: ShapeUp

A new logic programming language, ShapeUp, is developed. ShapeUp is an expanded Prolog system with string matching facilities. The language has been developed to give programmers a new computer programming environment, especially for knowledge information processing. This area includes natural language comprehension and intelligent text processing systems with better man-machine interfaces. For this kind of application, character string data play a principal part rather than conventional numerical data. In ShapeUp, string patterns are introduced as Prolog ‘terms’. Their matching process is performed inside the unification. Thus, a program is far simpler and easier to write and read in ShapeUp, than in conventional Prolog systems, and program size is extremely reduced.     

Partial evaluation of metaprograms in a “multiple worlds” logic language

This paper describes a partial evaluation system specifically designed to be used as an automatic compilation tool for metaprograms in a KBMS (EPSILON) based on Prolog. EPSILON main underlying concepts are the extension of Prolog with theories (“multiple worlds”) and the use of metaprogramming as the basic technique to define new inference engines and tools. Our partial evaluator is oriented towards theories and metainterpreter specialization. Being designed to be used as an automatic compiler, it does not require declarations from the user to control the unfolding process. It handles full Prolog and provides also an elegant solution to the problem of the partial evaluation of incomplete and self-modifying programs, by exploiting the multiple worlds feature added to Prolog. EPSILON partial evaluation system turned out to be a very useful and powerful tool to combine the low cost and the flexibility of metaprogramming with the performance requirements of a practical knowledge based system.