DSHOP: Distributed simple hierarchical ordered planner.

Planning has been an important subject in the area of Artificial Intelligence (AI) for over three decades. Planning is the problem of seeking a series of actions (that is, a plan) that will accomplish a desired goal. Most planning approaches rely on a single processor or a single-agent paradigm. Unfortunately, in a complex world, a single agent may not be sufficient to optimally solve the problem. Distributed Planning is a sub-field of Distributed AI that involves multi-agents working together to solve large planning problems. Distribution may speed up the traditional planning system through parallelism. Hierarchical Task Network (HTN) planning is an AI planning methodology that creates plans by task decomposition. SHOP (Simple Hierarchical Ordered Planner) is a domain-independent HTN planning system designed by Dana Nau et al. that plans for tasks in the same order that they will later be executed. This thesis aims at designing and implementing a distributed version of SHOP (that is, DSHOP) and running it on a high performance distributed system called SHARCNET. The implementation is based upon Message Passing Interface (MPI), that is, a library of functions used to achieve parallelism via message-passing. We investigate two approaches to share work between processors: state-copying and state-recomputation. We implemented a state-copying based DSHOP system (DSHOPC), and a state-recomputation based DSHOP system (DSHOPR). We compared these two implementations of DSHOP with the Java version of SHOP on a set of randomly generated artificial domains. A set of experimental results has been used to evaluate the performance of the DSHOP algorithm.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L83. Source: Masters Abstracts International, Volume: 43-01, page: 0240. Advisers: Scott Goodwin; Froduald Kabanza. Thesis (M.Sc.)--University of Windsor (Canada), 2004

High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Programming constraint services

This thesis presents design, application, implementation, and evaluation of computation spaces as abstractions for programming constraint services at a high level. Spaces are seamlessly integrated into a concurrent programming language and make constraintbased computations compatible with concurrency through encapsulation. Spaces are applied to search and combinators as essential constraint services. State-of-the-art and new search engines such as visual interactive search and parallel search are covered. Search is expressive and concurrency-compatible by using copying rather than trailing. Search is space and time efficient by using recomputation. Composable combinators, also known as deep-guard combinators, stress the control facilities and concurrency integration of spaces. The implementation of spaces comes as an orthogonal extension to the implementation of the underlying programming language. The resulting implementation is shown to be competitive with existing constraint programming systems.Diese Dissertation beschreibt Entwurf, Verwendung, Implementierung und Evaluierung von Computation Spaces für die Programmierung von Constraintdiensten. Spaces werden in eine nebenläufige Programmiersprache integriert. Sie fungieren als Kapseln für Berechnungen mit Constraints. Dadurch wird die Kompatibilität zu nebenläufigen Berechnungen gewährleistet. Suche und Kombinatoren sind zentrale Constraintdienste, die mit Spaces programmiert werden. Es werden sowohl übliche, als auch vollkommen neue Suchmaschinen, wie zum Beispiel interaktive Suche und parallele Suche, vorgestellt. Durch Kopieren wird Suche ausdrucksstark und kompatibel mit Nebenläufigkeit. Durch Wiederberechnung wird Suche effizient hinsichtlich Speicherbedarf und Laufzeit. Kombinatoren, die ineinander geschachtelt werden können (so genannte deep-guard Kombinatoren), verdeutlichen die Kontrollmöglichkeiten von Spaces. Die Implementierung von Spaces erfolgt als orthogonale Erweiterung einer Implementierung für die zugrundeliegende Programmiersprache. Das Ergebnis ist konkurrenzfähig zu existierenden Constraintprogrammiersystemen

Parallel CLP on Heterogeneous Networks

The combination of Or-Parallelism and Constraint Logic Programming (CLP) has proven to be very effective in tackling large combinatorial problems in real-life applications. However, existing implementations have focused on shared-memory multiprocessors. In this paper, we investigate how we can efficiently implement Or-Parallel CLP languages on heterogeneous networks, where communication bandwidth is much lower and heterogeneity requires all communication to be in a machine-independent format. Since a recomputation-based system has the potential to solve these problems, we analyse the performance of a prototype using this approach. On a representative set of CLP programs we show that close to optimal speedups can be obtained on networks for programs generating large search spaces and that the overhead of recomputation is surprisingly low. We compare this approach with that of stack-copying and also discuss how side-effects can be dealt with during recomputation. The main conclusion of the paper is that incremental recomputation is a clean and efficient execution model for Or-Parallel CLP systems on heterogeneous networks