A QUASI-SIMPLEX METHOD FOR STRICTLY CONVEX QUADRATIC PROGRAMMING

本文提出一个不用 Kuhn- Tucker条件而直接搜索严格凸二次规划最优目标点的鲁棒方法 .在搜索过程中 ,目标点沿约束多面体边界上的一条折线移动 .这种移动目标点的思想可以被认为是线性规划单纯形法的自然推广 ,在单纯形法中 ,目标点从一个顶点移到另一个顶点。

Advanced Programming Language Features for Executable Design Patterns "Better Patterns Through Reflection

The Design Patterns book [GOF95] presents 24 time-tested patterns that consistently appear in well-designed software systems. Each pattern is presented with a description of the design problem the pattern addresses, as well as sample implementation code and design considerations. This paper explores how the patterns from the "Gang of Four'', or "GOF'' book, as it is often called, appear when similar problems are addressed using a dynamic, higher-order, object-oriented programming language. Some of the patterns disappear -- that is, they are supported directly by language features, some patterns are simpler or have a different focus, and some are essentially unchanged.

The Role of Programming in the Formulation of Ideas

Classical mechanics is deceptively simple. It is surprisingly easy to get the right answer with fallacious reasoning or without real understanding. To address this problem we use computational techniques to communicate a deeper understanding of Classical Mechanics. Computational algorithms are used to express the methods used in the analysis of dynamical phenomena. Expressing the methods in a computer language forces them to be unambiguous and computationally effective. The task of formulating a method as a computer-executable program and debugging that program is a powerful exercise in the learning process. Also, once formalized procedurally, a mathematical idea becomes a tool that can be used directly to compute results.

The Grasping Problem: Toward Task-Level Programming for an Articulated Hand

This report presents a system for generating a stable, feasible, and reachable grasp of a polyhedral object. A set of contact points on the object is found that can result in a stable grasp; a feasible grasp is found in which the robot contacts the object at those contact points; and a path is constructed from the initial configuration of the robot to the stable, feasible final grasp configuration. The algorithm described in the report is designed for the Salisbury hand mounted on a Puma 560 arm, but a similar approach could be used to develop grasping systems for other robots.

The Definition and Implementation of a Computer Programming Language Based on Constraints

The constraint paradigm is a model of computation in which values are deduced whenever possible, under the limitation that deductions be local in a certain sense. One may visualize a constraint 'program' as a network of devices connected by wires. Data values may flow along the wires, and computation is performed by the devices. A device computes using only locally available information (with a few exceptions), and places newly derived values on other, locally attached wires. In this way computed values are propagated. An advantage of the constraint paradigm (not unique to it) is that a single relationship can be used in more than one direction. The connections to a device are not labelled as inputs and outputs; a device will compute with whatever values are available, and produce as many new values as it can. General theorem provers are capable of such behavior, but tend to suffer from combinatorial explosion; it is not usually useful to derive all the possible consequences of a set of hypotheses. The constraint paradigm places a certain kind of limitation on the deduction process. The limitations imposed by the constraint paradigm are not the only one possible. It is argued, however, that they are restrictive enough to forestall combinatorial explosion in many interesting computational situations, yet permissive enough to allow useful computations in practical situations. Moreover, the paradigm is intuitive: It is easy to visualize the computational effects of these particular limitations, and the paradigm is a natural way of expressing programs for certain applications, in particular relationships arising in computer-aided design. A number of implementations of constraint-based programming languages are presented. A progression of ever more powerful languages is described, complete implementations are presented and design difficulties and alternatives are discussed. The goal approached, though not quite reached, is a complete programming system which will implicitly support the constraint paradigm to the same extent that LISP, say, supports automatic storage management.

Inspection Methods in Programming

The work reported here lies in the area of overlap between artificial intelligence software engineering. As research in artificial intelligence, it is a step towards a model of problem solving in the domain of programming. In particular, this work focuses on the routine aspects of programming which involve the application of previous experience with similar programs. I call this programming by inspection. Programming is viewed here as a kind of engineering activity. Analysis and synthesis by inspection area prominent part of expert problem solving in many other engineering disciplines, such as electrical and mechanical engineering. The notion of inspections methods in programming developed in this work is motivated by similar notions in other areas of engineering. This work is also motivated by current practical concerns in the area of software engineering. The inadequacy of current programming technology is universally recognized. Part of the solution to this problem will be to increase the level of automation in programming. I believe that the next major step in the evolution of more automated programming will be interactive systems which provide a mixture of partially automated program analysis, synthesis and verification. One such system being developed at MIT, called the programmer's apprentice, is the immediate intended application of this work. This report concentrates on the knowledge are of the programmer's apprentice, which is the form of a taxonomy of commonly used algorithms and data structures. To the extent that a programmer is able to construct and manipulate programs in terms of the forms in such a taxonomy, he may relieve himself of many details and generally raise the conceptual level of his interaction with the system, as compared with present day programming environments. Also, since it is practical to expand a great deal of effort pre-analyzing the entries in a library, the difficulty of verifying the correctness of programs constructed this way is correspondingly reduced. The feasibility of this approach is demonstrated by the design of an initial library of common techniques for manipulating symbolic data. This document also reports on the further development of a formalism called the plan calculus for specifying computations in a programming language independent manner. This formalism combines both data and control abstraction in a uniform framework that has facilities for representing multiple points of view and side effects.

Freedom to learn: a radically revised pedagogy to facilitate lifewide learning in the academic curriculum.

Higher education has progressed fairly steadily to a common pedagogical approach which centres on the idea of alignment. In this arrangement, intended learning outcomes are identified and declared; learning activities which will enable the desired learning and development to be achieved are conceived and undertaken with the support of appropriate and effective teaching; and assessment which calls for these outcomes is (ideally) carefully designed and implemented. All three elements are aligned in advance. The same principles and practices underpinned by notions of alignment have been applied to date in most of the purposeful schemes for personal development planning. In this chapter I argue that lifewide learning, wherein learning and development often occur incidentally in multiple and varied real-world situations throughout an individual’s life course, calls for a different approach, and a different pedagogy. Higher education should therefore visualise lifewide learning as an emergent phenomenon wherein the outcomes of learning emerge later on, and are often unintended. Consequently, they cannot be defined in advance of the activities through which they are formed. The main purpose of this chapter is to offer some practical ideas to support the development of pedagogies that would enable programme designers to embed in their programmes the principle and practice of lifewide education.

On Genetic Programming and Knowledge Discovery in Transcriptome

Rowland, J. J. (2004) On Genetic Programming and Knowledge Discovery in Transcriptome Data. Proc. IEEE Congress on Evolutionary Computation, Portland, Oregon. pp 158-165. ISBN 0-7803-8515-2

Application of Inductive Logic Programming to Structure-Based Drug Design

Enot, D. and King, R. D. (2003) Application of Inductive Logic Programming to Structure-Based Drug Design. 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD '03). Springer LNAI 2838 p156-167

An Empirical Study of the Use of Relevance Information in Inductive Logic Programming

Srinivasan, A., King, R. D. and Bain, M.E. (2003) An Empirical Study of the Use of Relevance Information in Inductive Logic Programming. Journal of Machine Learning Research. 4(Jul):369-383

Structure based drug design with inductive logic programming

David P. Enot and Ross D. King (2003). Structure based drug design with inductive logic programming. The ACS National Meeting Spring 2003, New Orleans

The use of Inductive Logic Programming in drug design

David P. Enot and Ross D. King (2002) The use of Inductive Logic Programming in drug design. Proceedings of the 14th EuroQSAR Symposium (EuroQSAR 2002). Blackwell Publishing, p247-250

Adaptive denoising in spectral analysis by genetic programming

Rowland, J.J. and Taylor, J. (2002). Adaptive denoising in spectral analysis by genetic programming. Proc. IEEE Congress on Evolutionary Computation (part of WCCI), May 2002. pp 133-138. ISBN 0-7803-7281-6