Knowledge-Based Systems. Overview and Selected Examples

The Advanced Computer Applications (ACA) project builds on IIASA's traditional strength in the methodological foundations of operations research and applied systems analysis, and its rich experience in numerous application areas including the environment, technology and risk. The ACA group draws on this infrastructure and combines it with elements of AI and advanced information and computer technology to create expert systems that have practical applications. By emphasizing a directly understandable problem representation, based on symbolic simulation and dynamic color graphics, and the user interface as a key element of interactive decision support systems, models of complex processes are made understandable and available to non-technical users. Several completely externally-funded research and development projects in the field of model-based decision support and applied Artificial Intelligence (AI) are currently under way, e.g., "Expert Systems for Integrated Development: A Case Study of Shanxi Province, The People's Republic of China." This paper gives an overview of some of the expert systems that have been considered, compared or assessed during the course of our research, and a brief introduction to some of our related in-house research topics

Robotics handbook. Version 1: For the interested party and professional

This publication covers several categories of information about robotics. The first section provides a brief overview of the field of Robotics. The next section provides a reasonably detailed look at the NASA Robotics program. The third section features a listing of companies and organization engaging in robotics or robotic-related activities; followed by a listing of associations involved in the field; followed by a listing of publications and periodicals which cover elements of robotics or related fields. The final section is an abbreviated abstract of referred journal material and other reference material relevant to the technology and science of robotics, including such allied fields as vision perception; three-space axis orientation and measurement systems and associated inertial reference technology and algorithms; and physical and mechanical science and technology related to robotics

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

A classification of techniques for the compensation of time delayed processes. Part 2: Structurally optimised controllers

Following on from Part 1, Part 2 of the paper considers the use of structurally optimised controllers to compensate time delayed processes

Programming Languages for Distributed Computing Systems

When distributed systems first appeared, they were programmed in traditional sequential languages, usually with the addition of a few library procedures for sending and receiving messages. As distributed applications became more commonplace and more sophisticated, this ad hoc approach became less satisfactory. Researchers all over the world began designing new programming languages specifically for implementing distributed applications. These languages and their history, their underlying principles, their design, and their use are the subject of this paper. We begin by giving our view of what a distributed system is, illustrating with examples to avoid confusion on this important and controversial point. We then describe the three main characteristics that distinguish distributed programming languages from traditional sequential languages, namely, how they deal with parallelism, communication, and partial failures. Finally, we discuss 15 representative distributed languages to give the flavor of each. These examples include languages based on message passing, rendezvous, remote procedure call, objects, and atomic transactions, as well as functional languages, logic languages, and distributed data structure languages. The paper concludes with a comprehensive bibliography listing over 200 papers on nearly 100 distributed programming languages

Harmonizing CMMI-DEV 1.2 and XP Method to Improve The Software Development Processes in Small Software Development Firms

Most software development organizations are small firms, and they have realized the need to manage and improve their software development and management activities. Traditional Software Process Improvement (SPI) models and standards are not realistic for these firms because of high cost, limited resources and strict project deadlines. Therefore, these firms need a lightweight software development method and an appropriate SPI model to manage and improve their software development and management processes. This study aims to construct a suitable software development process improvement framework for Small Software Development Firms (SSDFs) based on eXtreme Programming (XP) method and Capability Maturity Model Integration for Development Version 1.2 (CMMI-Dev1.2) model. Four stages are involved in developing the framework: (1) aligning XP practices to the specific goals of CMMI-Dev1.2 Key Process Areas (KPAs); (2) developing the proposed software development process improvement framework based on extending XP method by adapting the Extension-Based Approach (EBA), CMMI-Dev1.2, and generic elements of the SPI framework; (3) verifying the compatibility of the proposed framework to the KPAs of CMMI-Dev1.2 by using focus group method coupled with Delphi technique; and (4) validating the modified framework by using CMMI-Dev1.2 questionnaire as a main item to validate the suitability of the modified framework for SSDFs, and conducting two case studies to validate the applicability and effectiveness of this framework for these firms. The result of aligning XP practices to the KPAs of CMMI-Dev1.2 shows that twelve KPAs are largely supported by XP practices, eight KPAs are partially supported by XP practices, and two KPAs are not-supported by XP practices. The main contributions of this study are: software development process improvement framework for SSDFs, elicit better understanding of how to construct the framework, and quality improvement of the software development processes. There are possible avenues for extending this research to fulfil the missing specific practices of several KPAs, examining other agile practices and using CMMI-Dev1.3 to improve the framework, and conducting more case studie