Design, Implementierung und Evaluierung einer virtuellen Maschine für Oz

Diese Arbeit beschreibt Design, Implementierung und Evaluierung einer virtuellen Maschine für die Kernsprache von Oz, die wir mit L bezeichnen. Wir stellen L aus didaktischen Gründen als Erweiterung einer Teilsprache von SML dar. Die wichtigsten Unterschiede von L zu SML sind: logische Variablen, Threads, Synchronisation und dynamische Typisierung. Ausgehend von einer informellen Beschreibung der dynamischen Semantik über ein Graphenmodell entwickeln wir daraus schrittweise auf unterschiedlichen Abstraktionsebenen eine virtuelle Maschine für L. Wir beschreiben zunächst ein einfaches Grundmodell. Darauf aufbauend diskutieren wir verschiedene Optimierungen. Schließlich verfeinern wir weiter, indem wir auf Aspekte der Implementierung des Modells eingehen. Abschließend evaluieren wir die Effektivität der vorgestellten Techniken an einer Reihe von größeren Anwendungen aus der Praxis. Weiter zeigen wir, daß die Implementierung der Sprache kompetitiv ist mit den schnellsten Emulatoren für statisch getypte funktionale Sprachen.This thesis presents the design, implementation and evaluation of a virtual machine for the core language of Oz, which we call L. We present L for didactic reasons as an extension of a sublanguage of SML. The most important differences between L and SML are: logic variables, threads, synchronization and dynamic typing. Starting from an informal description of the dynamic semantics in terms of a graph model, we develop step by step on various levels of abstraction a virtual machine for L. We begin with a simple basic model. We then propose several optimizations of this model. Afterwards we keep refining our approach by addressing specific aspects of the implementation of the model. Finally we evaluate the effectiveness of the techniques using a set of larger real world applications. Further we show, that the implementation of the language is competitive with the fastest emulators for statically typed functional languages

An abstract machine for Oz

Oz is a concurrent constraint language providing for first-class procedures, concurrent objects, and encapsulated search. DFKI Oz is an interactive implementation of Oz competitive in performance with commercial Prolog and Lisp systems. This paper describes AMOZ, the abstract machine underlying DFKI Oz. AMOZ implements rational tree constraints, first-class procedures, local computation spaces for deep guards, and preemptive and fair threads

Design, Implementierung und Evaluierung einer virtuellen Maschine für Oz

Diese Arbeit beschreibt Design, Implementierung und Evaluierung einer virtuellen Maschine für die Kernsprache von Oz, die wir mit L bezeichnen. Wir stellen L aus didaktischen Gründen als Erweiterung einer Teilsprache von SML dar. Die wichtigsten Unterschiede von L zu SML sind: logische Variablen, Threads, Synchronisation und dynamische Typisierung. Ausgehend von einer informellen Beschreibung der dynamischen Semantik über ein Graphenmodell entwickeln wir daraus schrittweise auf unterschiedlichen Abstraktionsebenen eine virtuelle Maschine für L. Wir beschreiben zunächst ein einfaches Grundmodell. Darauf aufbauend diskutieren wir verschiedene Optimierungen. Schließlich verfeinern wir weiter, indem wir auf Aspekte der Implementierung des Modells eingehen. Abschließend evaluieren wir die Effektivität der vorgestellten Techniken an einer Reihe von größeren Anwendungen aus der Praxis. Weiter zeigen wir, daß die Implementierung der Sprache kompetitiv ist mit den schnellsten Emulatoren für statisch getypte funktionale Sprachen.This thesis presents the design, implementation and evaluation of a virtual machine for the core language of Oz, which we call L. We present L for didactic reasons as an extension of a sublanguage of SML. The most important differences between L and SML are: logic variables, threads, synchronization and dynamic typing. Starting from an informal description of the dynamic semantics in terms of a graph model, we develop step by step on various levels of abstraction a virtual machine for L. We begin with a simple basic model. We then propose several optimizations of this model. Afterwards we keep refining our approach by addressing specific aspects of the implementation of the model. Finally we evaluate the effectiveness of the techniques using a set of larger real world applications. Further we show, that the implementation of the language is competitive with the fastest emulators for statically typed functional languages

On the representation of temporal knowledge

The growing interest in an adequate modelling of time in Artificial Intelligence has given rise to the research discipline of Temporal Reasoning (TR). Due to different views, different approaches towards TR such as PL1, modal logics or Allen\u27s intervall logic have been investigated. It was realized at an early stage that each of this approaches has some strong points whereas it suffers from certain drawbacks. Thus recently, a number of research activities have emerged aiming at a combination of the classical paradigms for representing time. In the first part of this paper, we present an overview of the most important approaches to the integration of temporal knowledge into logic programming. In the second part, we present the CRONOLOG temporal logic programming language which has been developed to cover the quintessence of the approaches presented before. The third part of the paper describes TRAM, which it is an extension of CRONOLOG to a temporal knowledge representation system. Using TRAM it is possible to represent knowledge depending on time and to reason about this knowledge. TRAM has been conceptually based on a combination of modal logics with Allen\u27s interval logic. We present the Extended Modal Logics (EML) which establishes the theoretical framework for TRAM. We define an operational semantics and a horizontal compilation scheme for TRAM

Integrating Efficient Records into Concurrent Constraint Programming

We show how to implement efficient records in constraint logic programming (CLP) and its generalization concurrent constraint programming (CCP). Records can be naturally integrated into CCP as a new constraint domain. The implementation provides the added expressive power of concurrency and fine-grained constraints over records, yet does not pay for this expressivity when it is not used. In addition to traditional record operations, our implementation allows to compute with partially-known records. This fine granularity is useful for natural-language and knowledge-representation applications. The paper describes the implementation of records in the DFKI Oz system. Oz is a higher-order CCP language with encapsulated search. We show that the efficiency of records in CCP is competitive with modern Prolog implementation technology and that our implementation provides improved performance for natural-language applications

DFKI Oz User's Manual

DFKI Oz is an interactive implementation of Oz, a high-level programming language designed for concurrent symbolic computation. DFKI Oz features a programming interface based on GNU Emacs, a concurrent browser, an object-oriented interface to Tcl/Tk, powerful interoperability features (sockets, C, C++), an incremental compiler, a garbage collector, and support for stand-alone applications. Performance is competitive with commercial Prolog and Lisp systems. DFKI Oz is available for many platforms running Unix/X, including Sun SPARC systems and 80x86 PCs running Linux. This document describes DFKI Oz from the user's perspective, giving information about the programming environment, efficiency issues, stand-alone applications, the interface to C and C++, and the installation of the system. This User's Manual corresponds to release 1.1 of the DFKI Oz system

Efficient logic variables for distributed computing

We define a practical algorithm for distributed rational tree unification and prove its correctness in both the off-line and on-line cases. We derive the distributed algorithm from a centralized one, showing clearly the trade-offs between local and distributed execution. The algorithm is used to implement logic variables in the Distributed Oz system. Oz has a very general execution model that consists of concurrent constraints extended with explicit state and higher-orderness. We show that logic variables can easily be added to the more restricted models of Java and ML. We present common distributed programming idioms in a network-transparent way using logic variables. We show that in common cases the algorithm maintains the same message latency as explicit message passing. In addition, it is able to handle uncommon cases that arise from the properties of latency tolerance and third-party independence. This is evidence that using logic variables in distributed computing is beneficial a..