Applications of artificial intelligence to mission planning

The scheduling problem facing NASA-Marshall mission planning is extremely difficult for several reasons. The most critical factor is the computational complexity involved in developing a schedule. The size of the search space is large along some dimensions and infinite along others. It is because of this and other difficulties that many of the conventional operation research techniques are not feasible or inadequate to solve the problems by themselves. Therefore, the purpose is to examine various artificial intelligence (AI) techniques to assist conventional techniques or to replace them. The specific tasks performed were as follows: (1) to identify mission planning applications for object oriented and rule based programming; (2) to investigate interfacing AI dedicated hardware (Lisp machines) to VAX hardware; (3) to demonstrate how Lisp may be called from within FORTRAN programs; (4) to investigate and report on programming techniques used in some commercial AI shells, such as Knowledge Engineering Environment (KEE); and (5) to study and report on algorithmic methods to reduce complexity as related to AI techniques

VORONOI COMPLEXES IN HIGHER DIMENSIONS, COHOMOLOGY OF GLN(Z)GL_N (Z) FOR N≥8N\ge 8 AND THE TRIVIALITY OF K8(Z)K_8 (Z)

We enumerate the low dimensional cells in the Voronoi cell complexes attached to the modular groups $SL_N (Z)$ and $GL_N (Z)$ for $N = 8, 9, 10, 11$, using quotient sublattices techniques for $N = 8, 9$ and linear programming methods for higher dimensions. These enumerations allow us to compute some cohomology of these groups and prove that $K_8 (Z) = 0$, providing new knowledge on the Kummer-Vandiver conjecture

A survey of representations employed in object-orientated programming

This document presents an overview of the program visualisations additional to the program code provided by some of the most popular object-oriented programming environments to support tasks involving program comprehension. These representations were compared in terms of the programming aspects they highlight and of their information modality. Those with common characteristics according to these criteria were identified. Finally, a brief analysis of these common representations in terms of Green's Cognitive Dimensions is presented. Two questions arising from this survey are (a) whether representations additional to the code should be redundant and highlight similar information to the main notation or be complementary and highlight different programming aspects and (b) which factors might increase the cognitive difficulty of co-ordinating these additional representations and the program code. More theoretical knowledge about the way these additional representations influence the comprehension of computer programs seems to be needed

Learning Dimensions: Lessons from Field Studies

In this paper, we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies involving a range of age groups to explore how programming tasks could best be framed to motivate learners. Our empirical findings from these four studies, described here, contributed to the design of a set of programming "Learning Dimensions" (LDs). The LDs provide educators with insights to support key design decisions for the creation of engaging programming learning experiences. This paper describes the background to the identification of these LDs and how they could address the design and delivery of highly engaging programming learning tasks. A web application has been authored to support educators in the application of the LDs to their lesson design

A review of Australasian investigations into problem solving and the novice programmer

This Australasian focused review compares a number of recent studies that have identified difficulties encountered by novices while learning programming and problem solving. These studies have shown that novices are not performing at expected levels and many novices have only a fragile knowledge of programming, which may prevent them from learning and applying problem solving strategies. The review goes on to explore proposals for explicitly incorporating problem solving strategy instruction into introductory programming curricula and assessment, in an attempt to produce improved learning outcomes for novices. Finally, directions suggested by the reviewed studies are gathered and some unanswered questions are raised

Designing Engaging Learning Experiences in Programming

In this paper we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies to explore how programming tasks could be framed to motivate learners. Our empirical findings from these four field studies are summarized here, with a particular focus upon one – Whack a Mole – which compared the use of a physical interface with the use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions reported by two sets of participant undergraduate students were analyzed, identifying the links between the emotions experienced during programming and their origin. Evidence was collected of the very positive emotions experienced by learners programming with a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface. A follow-up study provided further evidence of the motivation of personalized design of programming tangible physical artefacts. Collating all the evidence led to the design of a set of ‘Learning Dimensions’ which may provide educators with insights to support key design decisions for the creation of engaging programming learning experiences

Study of material interpolation for 3D lightweight structures

The use of computer tools has become essential in modern engineering, from the use of specific software to programming it. That is why this work seeks to provide the tools to understand the essential knowledge of programming for a modern engineer, which allows the easy correction, understanding and efficiency of the code. In addition, the thesis seeks to solve one of the heuristic problems of topology optimization. It is a research field that obtains optimal design structures in terms of volume usage. The most popular approach, the SIMP method, an auto-penalized interpolation function in a relaxed topology optimization problem, does not always propose actual solutions. For this reason, an alternative solution to the SIMP method is to be found for any case scenario. Finally, it is intended to apply these knowledge by rewriting part of the Swan code. By refactoring a previously programmed code from the subject ’aerospace structures’, called ”Codi Cante”, the sections object oriented programming, work with cloud backups, test driven development and clean code are developed. These are tools used by modern software engineers. Regarding topology optimization, an alternative method called SIMP-All is used to solve the drawbacks of the SIMP method. From the previously defined methods, knowledge is acquired to program efficiently and structurally. At this point, object oriented programming, cloud-backed work, test driven development and clean code are discussed. On the other hand, the results concerning topology optimization allow the obtaining of optimal and actual structures. Firstly, the SIMP-All method solution for three-dimensional cases is found. The solution is then shown to be part of the physical limits called ”Hashin-Shtrikman bounds” and a comprehensive solution for the SIMP-All method is proposed. These results are compared with the SIMP method in two and three dimensions. Finally, all the new knowledge is applied to rewrite part of the Swan code. In conclusion, from the refactoring of the ”Codi Cante”, sufficient tools have been developed to understand essential knowledge of programming by a modern engineer. Besides that, it has been possible to obtain a general equation for the SIMP-ALL method. Not only does the generalized equation allow to find optimal results, but the generated structures are also possible in the reality. Furthermore, the equation has been implemented in the Swan program using the new programming knowledge, thus allowing an easy correction, understanding and efficiency of the code