from syntactic structure and a simple dynamic model of attentional state. Like the parser, the algorithm is implemented in Prolog. The authors have tested it extensively on computer manual texts, and conducted a blind test on manual text containing 360 pronoun occurrences. The algorithm successfully

SLD resolution with negation as finite failure (SLDNF) reflects the procedural interpretation of predicate calculus as a programming language and forms the computational basis for Prolog systems. Despite its advantages for stack-based memory management, SLDNF is often not appropriate for query

One critical issue in the design of declarative languages is their memory performance, both in terms of total memory usage and locality in memory accesses. Early studies of Prolog programs have shown the language to have excellent characteristics in this regard. In this work we study the memory

part gives an overview of the important technical developments starting with the Warren Abstract Machine (WAM). The second part presents the history and the contributions of the major software and hardware systems. The third part charts the evolution of Prolog performance since Warren's DEC-10

The efficiency of Prolog compilers is increasing rapidly but the Prolog programs still cannot compete with traditional languages when executing simple conditionals. In this paper we present a possibility to increase Prolog performance by exploiting the shallow backtracking. Shallow backtracking

Most Prolog machines have been based on specialized architectures. Our goal is to start with a general purpose architecture and determine a minimal set of extensions for high performance Prolog execution. We have developed both the architecture and optimizing compiler simultaneously, drawing

procedure with depth-first iterative-deepening search and unification with the occurs check. Its high performance exceeds that of Prolog-based PTTP interpreters, and it is more concise and readable than the earlier Lisp-based compiler, which makes it superior for expository purposes. Examples of inputs

This paper proposes a novel analysis, called cardinality analysis, to approximate the number of solutions to a goal. The analysis is an instantiation of a new abstract interpretation framework for full Prolog (without dynamic predicates) based on sequences of substitutions. The abstract domain

Machine or WAM. Section 5 describes some optimizations and shows how Mercury handles I/O. Section 6 gives the current state of the Mercury system while section 7 presents performance results. 2. The Mercury language Syntactically, Mercury is similar to Prolog with additional declarations, partly

, and procedural constructs, such as iterative loops. Second, we exercise and test our implementations on various graph problems. We test several different Transaction Logic prototypes implemented on top of several different Prologs. The results effectively compare the query and update performance of different