COLAB : a hybrid knowledge representation and compilation laboratory

Knowledge bases for real-world domains such as mechanical engineering require expressive and efficient representation and processing tools. We pursue a declarative-compilative approach to knowledge engineering. While Horn logic (as implemented in PROLOG) is well-suited for representing relational clauses, other kinds of declarative knowledge call for hybrid extensions: functional dependencies and higher-order knowledge should be modeled directly. Forward (bottom-up) reasoning should be integrated with backward (top-down) reasoning. Constraint propagation should be used wherever possible instead of search-intensive resolution. Taxonomic knowledge should be classified into an intuitive subsumption hierarchy. Our LISP-based tools provide direct translators of these declarative representations into abstract machines such as an extended Warren Abstract Machine (WAM) and specialized inference engines that are interfaced to each other. More importantly, we provide source-to-source transformers between various knowledge types, both for user convenience and machine efficiency. These formalisms with their translators and transformers have been developed as part of COLAB, a compilation laboratory for studying what we call, respectively, "vertical\u27; and "horizontal\u27; compilation of knowledge, as well as for exploring the synergetic collaboration of the knowledge representation formalisms. A case study in the realm of mechanical engineering has been an important driving force behind the development of COLAB. It will be used as the source of examples throughout the paper when discussing the enhanced formalisms, the hybrid representation architecture, and the compilers

Nondeterminism and Guarded Commands

The purpose of this paper is to discuss the relevance of nondeterminism in computer science, with a special emphasis on Dijkstra's guarded commands language.Comment: 34 pages. This is authors' version of Chapter 8 of the book K.R. Apt and C.A.R. Hoare (editors), Edsger Wybe Dijkstra: His Life, Work, and Legacy, volume 45 of ACM Books. ACM/Morgan & Claypool, 202

Lazy Evaluation and Delimited Control

The call-by-need lambda calculus provides an equational framework for reasoning syntactically about lazy evaluation. This paper examines its operational characteristics. By a series of reasoning steps, we systematically unpack the standard-order reduction relation of the calculus and discover a novel abstract machine definition which, like the calculus, goes "under lambdas." We prove that machine evaluation is equivalent to standard-order evaluation. Unlike traditional abstract machines, delimited control plays a significant role in the machine's behavior. In particular, the machine replaces the manipulation of a heap using store-based effects with disciplined management of the evaluation stack using control-based effects. In short, state is replaced with control. To further articulate this observation, we present a simulation of call-by-need in a call-by-value language using delimited control operations

Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

The development of an expert system shell with a mixed knowledge representation, explicit control of reasoning and a truth maintenance system

Bibliography: pages 227-236.This thesis concentrates on several important issues in expert system research, namely - representation of knowledge - control of reasoning - implementation of non-monotonic logics via truth maintenance systems. There are three parts to this thesis. PART1 covers the background research in the above mentioned topics. PART2 discusses the WISE system and the way in which research from PART1 was applied to the development of the WISE shell. PART3 considers the features of other expert system shells

A Framework for Efficient Execution of Logic Programs.

The focus of this dissertation is to develop an efficient framework for sequential execution of logic programs. Within this framework the logic programs are executed by pruning the goal-search tree whenever applicable. Three new concepts for pruning of computation during execution of logic programs are introduced. (1) Failure-binding. A Failure-binding for a literal is a binding which when applied to the literal fails the goal obtained from the literal. Failure-bindings for a literal are identified by analyzing the goal-tree of a goal which is obtained from the literal. The failure-bindings for a literal are used for intelligent backtracking based on the generator-consumer approach. Intelligent backtracking based on failure-bindings prune the computation of search space which lead to late detection of failure. (2) Failure-solution. A Failure-solution of a goal is unacceptable to some other subgoal in the forward execution. Failure-solutions of a goal are identified by analyzing the history of computation, during execution. Failure-solutions of the goals are used for intelligent forward execution. Intelligent forward execution prunes the computation of search space which leads to repeated failure resulting from repeated successes of a goal. (3) Forward jumping. Forward jumping is a method to avoid reexecution of some subgoals after backtracking (instead of naive forward execution after backtracking). Forward jumping is based on the dynamic subgoal dependencies in a rule. Such jumping prunes the computation of the search spaces which leads to the same sequences of successes of subgoals after backtracking. To facilitate the implementation of these concepts a new data structure, called segmented-stack, is defined. The space complexity of a segmented stack is linear in the number of nodes in the stack. Depth-first search as well as breadth-first search are very easily implemented on a segmented-stack during execution of logic programs. Execution of logic programs on a segmented-stack allows association of the search space, as well as the solutions, of a goal with the frame of the goal. This enables implementation of intelligent backtracking, intelligent forward execution and forward jumping. The search based on each of these paradigms is proved to be sound and complete. It is also shown that the implementation of these paradigms preserves the order of results obtained by Prolog. The effects of the non-logical operators, in Prolog, on the paradigms are studied. The search based on the these paradigms is compared individually, and collectively, with the standard search by Prolog

Topics in Programming Languages, a Philosophical Analysis through the case of Prolog

[EN]Programming languages seldom find proper anchorage in philosophy of logic, language and science. is more, philosophy of language seems to be restricted to natural languages and linguistics, and even philosophy of logic is rarely framed into programming languages topics. The logic programming paradigm and Prolog are, thus, the most adequate paradigm and programming language to work on this subject, combining natural language processing and linguistics, logic programming and constriction methodology on both algorithms and procedures, on an overall philosophizing declarative status. Not only this, but the dimension of the Fifth Generation Computer system related to strong Al wherein Prolog took a major role. and its historical frame in the very crucial dialectic between procedural and declarative paradigms, structuralist and empiricist biases, serves, in exemplar form, to treat straight ahead philosophy of logic, language and science in the contemporaneous age as well. In recounting Prolog's philosophical, mechanical and algorithmic harbingers, the opportunity is open to various routes. We herein shall exemplify some: - the mechanical-computational background explored by Pascal, Leibniz, Boole, Jacquard, Babbage, Konrad Zuse, until reaching to the ACE (Alan Turing) and EDVAC (von Neumann), offering the backbone in computer architecture, and the work of Turing, Church, Gödel, Kleene, von Neumann, Shannon, and others on computability, in parallel lines, throughly studied in detail, permit us to interpret ahead the evolving realm of programming languages. The proper line from lambda-calculus, to the Algol-family, the declarative and procedural split with the C language and Prolog, and the ensuing branching and programming languages explosion and further delimitation, are thereupon inspected as to relate them with the proper syntax, semantics and philosophical élan of logic programming and Prolog