Applying Prolog to Develop Distributed Systems

Development of distributed systems is a difficult task. Declarative programming techniques hold a promising potential for effectively supporting programmer in this challenge. While Datalog-based languages have been actively explored for programming distributed systems, Prolog received relatively little attention in this application area so far. In this paper we present a Prolog-based programming system, called DAHL, for the declarative development of distributed systems. DAHL extends Prolog with an event-driven control mechanism and built-in networking procedures. Our experimental evaluation using a distributed hash-table data structure, a protocol for achieving Byzantine fault tolerance, and a distributed software model checker - all implemented in DAHL - indicates the viability of the approach

SAGA: A project to automate the management of software production systems

The project to automate the management of software production systems is described. The SAGA system is a software environment that is designed to support most of the software development activities that occur in a software lifecycle. The system can be configured to support specific software development applications using given programming languages, tools, and methodologies. Meta-tools are provided to ease configuration. Several major components of the SAGA system are completed to prototype form. The construction methods are described

On the Efficiency of Optimising Shallow Backtracking in Prolog

The cost of backtracking has been identified as one of the bottlenecks in achieving peak performance in compiled Prolog programs. Much of the backtracking in Prolog programs is shallow, i.e. is caused by unification failures in the head of a clause when there are more alternatives for the same procedure, and so special treatment of this form of backtracking has been proposed as a significant optimisation. This paper describes a modified WAM which optimises shallow backtracking. Four different implementation approaches are compared. A number of benchmark results are presented, measuring the relative tradeoffs between compilation time, code size, and run time. The results show that the speedup gained by this optimisation can be significant

Optimizing the SICStus Prolog virtual machine instruction set

The Swedish Institute of Computer Science (SICS) is the vendor of SICStus Prolog. To decrease execution time and reduce space requirements, variants of SICStus Prolog's virtual instruction set were investigated. Semi-automatic ways of finding candidate sets of instructions to combine or specialize were developed and used. Several virtual machines were implemented and the relationship between improvements by combinations and by specializations were investigated. The benefits of specializations and combinations of instructions to the performance of the emulator is on the average of the order of 10%. The code size reduction is 15%

Acquiring Word-Meaning Mappings for Natural Language Interfaces

This paper focuses on a system, WOLFIE (WOrd Learning From Interpreted Examples), that acquires a semantic lexicon from a corpus of sentences paired with semantic representations. The lexicon learned consists of phrases paired with meaning representations. WOLFIE is part of an integrated system that learns to transform sentences into representations such as logical database queries. Experimental results are presented demonstrating WOLFIE's ability to learn useful lexicons for a database interface in four different natural languages. The usefulness of the lexicons learned by WOLFIE are compared to those acquired by a similar system, with results favorable to WOLFIE. A second set of experiments demonstrates WOLFIE's ability to scale to larger and more difficult, albeit artificially generated, corpora. In natural language acquisition, it is difficult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, most results to date for active learning have only considered standard classification tasks. To reduce annotation effort while maintaining accuracy, we apply active learning to semantic lexicons. We show that active learning can significantly reduce the number of annotated examples required to achieve a given level of performance

A partial breadth-first execution model for prolog

MEM (Multipath Execution Model) is a novel model for the execution of Prolog programs which combines a depth-first and breadth-first exploration of the search tree. The breadth-first search allows more than one path of the SLD-tree to be explored at the same time. In this way, the computational cost of traversing the whole search tree associated to a program can be decreased because the MEM model reduces the overhead due to the execution of control instructions and also diminishes the number of unifications to be performed. This paper focuses on the description of the MEM model and its sequential implementation. Moreover, the MEM execution model can be implemented in order to exploit a new kind of parallelism, called path parallelism, which allows the parallel execution of unify operations related to simultaneously traversed pathsPeer ReviewedPostprint (published version

AiGERM: A logic programming front end for GERM

AiGerm (Artificially Intelligent Graphical Entity Relation Modeler) is a relational data base query and programming language front end for MCC (Mission Control Center)/STP's (Space Test Program) Germ (Graphical Entity Relational Modeling) system. It is intended as an add-on component of the Germ system to be used for navigating very large networks of information. It can also function as an expert system shell for prototyping knowledge-based systems. AiGerm provides an interface between the programming language and Germ