SMILE/ MARVEL: Two Approaches to Knowledge-Based Programming Environments

This technical report consists of three related papers in the area of intelligent assistance for software development and maintenance. Intelligent Assistance without Artificial Intelligence describes SMILE, a software engineering environment that assists teams of programmers without using AI technology. An Architecture for Intelligent Assistance in Software Development presents an AI approach to generalizing the capabilities of SMILE. Granularity Issues in a Knowledge-Based Programming Environment briefly describes MARVEL, an intelligent assistant based on this AI approach, and compares it to SMILE

An active learning and training environment for database programming

Active learning facilitated through interactive, self-controlled learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a tool for database programming that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. Therefore, we discuss analysis and evaluation of these Web-based environments focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the learning approach, learning organisation and actual tool usage are aspects of behaviour that require different techniques to be used

A Platform-independent Programming Environment for Robot Control

The development of robot control programs is a complex task. Many robots are different in their electrical and mechanical structure which is also reflected in the software. Specific robot software environments support the program development, but are mainly text-based and usually applied by experts in the field with profound knowledge of the target robot. This paper presents a graphical programming environment which aims to ease the development of robot control programs. In contrast to existing graphical robot programming environments, our approach focuses on the composition of parallel action sequences. The developed environment allows to schedule independent robot actions on parallel execution lines and provides mechanism to avoid side-effects of parallel actions. The developed environment is platform-independent and based on the model-driven paradigm. The feasibility of our approach is shown by the application of the sequencer to a simulated service robot and a robot for educational purpose

Reusable Knowledge-based Components for Building Software Applications: A Knowledge Modelling Approach

In computer science, different types of reusable components for building software applications were proposed as a direct consequence of the emergence of new software programming paradigms. The success of these components for building applications depends on factors such as the flexibility in their combination or the facility for their selection in centralised or distributed environments such as internet. In this article, we propose a general type of reusable component, called primitive of representation, inspired by a knowledge-based approach that can promote reusability. The proposal can be understood as a generalisation of existing partial solutions that is applicable to both software and knowledge engineering for the development of hybrid applications that integrate conventional and knowledge based techniques. The article presents the structure and use of the component and describes our recent experience in the development of real-world applications based on this approach

DNF Sampling for ProbLog Inference

Inference in probabilistic logic languages such as ProbLog, an extension of Prolog with probabilistic facts, is often based on a reduction to a propositional formula in DNF. Calculating the probability of such a formula involves the disjoint-sum-problem, which is computationally hard. In this work we introduce a new approximation method for ProbLog inference which exploits the DNF to focus sampling. While this DNF sampling technique has been applied to a variety of tasks before, to the best of our knowledge it has not been used for inference in probabilistic logic systems. The paper also presents an experimental comparison with another sampling based inference method previously introduced for ProbLog.Comment: Online proceedings of the Joint Workshop on Implementation of Constraint Logic Programming Systems and Logic-based Methods in Programming Environments (CICLOPS-WLPE 2010), Edinburgh, Scotland, U.K., July 15, 201

Visual creation of inhabited 3D environments: An ontology-based approach

The creation of virtual reality applications and 3D environments is a complex task that requires good programming skills and expertise in computer graphics and many other disciplines. The complexity increases when we want to include complex entities such as virtual characters and animate them. In this paper we present a system that assists in the tasks of setting up a 3D scene and configuring several parameters affecting the behavior of virtual entities like objects and autonomous virtual humans. Our application is based on a visual programming paradigm, supported by a semantic representation, an ontology for virtual environments. The ontology allows us to store and organize the components of a 3D scene, together with the knowledge associated with them. It is also used to expose functionalities in the given 3D engine. Based on a formal representation of its components, the proposed architecture provides a scalable VR system. Using this system, non-experts can set up interactive scenarios with minimum effort; no programming skills or advanced knowledge is require

Kate's Model Verification Tools

Kennedy Space Center's Knowledge-based Autonomous Test Engineer (KATE) is capable of monitoring electromechanical systems, diagnosing their errors, and even repairing them when they crash. A survey of KATE's developer/modelers revealed that they were already using a sophisticated set of productivity enhancing tools. They did request five more, however, and those make up the body of the information presented here: (1) a transfer function code fitter; (2) a FORTRAN-Lisp translator; (3) three existing structural consistency checkers to aid in syntax checking their modeled device frames; (4) an automated procedure for calibrating knowledge base admittances to protect KATE's hardware mockups from inadvertent hand valve twiddling; and (5) three alternatives for the 'pseudo object', a programming patch that currently apprises KATE's modeling devices of their operational environments

An investigation into student reactions towards rad versus traditional programming environments for novice developers

The traditional approach to programming using text editors is widely used in many institutions to teach introductory programming. These types of traditional programming environments provide fundamental programming concepts for learning, especially in the context of novice developers. In recent years, teaching institutions have seen a trend towards the introduction of visual drag-and-drop rapid application development (RAD) environments for teaching novice programmers. These \u27environments capture student interest in programming by allowing the construction of workable programs within a short time frame based on minimal pre-existing coding knowledge. However, some have argued that these visual RAD environments might not be suitable for providing fundamental programming concepts and syntax to novice developers. This research examines student perceptions towards visual RAD environments in comparison to traditional environments for learning programming for novice developers, mainly focusing on the novice developer\u27s first programming environment. To gather student reactions towards these programming environments, surveys, interviews and workshops were conducted with novice, intermediate and expert level student programmers. The results indicate that while visual RAD environments managed to capture the majority of the participants\u27 interest, the traditional approach was largely accepted as the most appropriate first environment for novice developers. Another finding from this research is the participants\u27 perceptions of the key aspects of learning programming, which also formed part of the deciding factors for the first environment. Understanding the underlying concepts, syntax and logic of the program seem to be the most important aspects followed by interest level and the ability to build workable programs quickly. The majority of participants perceived that traditional programming environments could help novice developers with understanding underlying concepts and syntax better than visual RAD environments. Although visual RAD environments do not require a traditional programming environment at the early stage of programming, the latter would become necessary as the program grows and more complex functions are required. Overall, the visual RAD environment was still the preferred environment for development despite the lack of pedagogical benefits compared with traditional environments