This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is an agent that has been designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy of the adaptive agent from the expert that is most suitable for the unknown environment. If the agent is a passive observer, then the optimal solution is the well-known Bayesian predictor. However, if the agent is active, then its past actions need to be treated as causal interventions on the I/O stream rather than normal probability conditions. Here it is shown that the solution to this new variational problem is given by a stochastic controller called the Bayesian control rule, which implements adaptive behavior as a mixture of experts. Furthermore, it is shown that under mild assumptions, the Bayesian control rule converges to the control law of the most suitable expert.

The CausalityWorkbench project provides an environment to test causal discovery algorithms. Via a web portal, we provide a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we explore the opportunities offered by development applications.

Teams of University have mentored middle and high school students primary and secondary school students participate in one of from Durham Public Schools as part of the Duke three competitions: rescue, dance, or soccer. Rescue teams RoboCupJunior program, a project-oriented, team-based build and program a robot capable of navigating a course academic enrichment program. The objective of the while identifying and rescuing victims by following lines program is to foster interest and competence in computing, and responding to color cues. Dance teams choreograph a science, and mathematics, while simultaneously dance routine using robots they build and decorate, developing problem-solving skills, enabling creative costumes they prepare, and music they select. Soccer teams thinking and design, and providing a domain for build and program two robots, which then face off against application of scientific concepts. Robotics is a popular other teams' robots in a soccer match. Winners of regional domain for attracting students to computing and competitions are eligible to attend the annual RoboCup engineering (Sklar, Parsons 2002) and can be used in World Finals, held at various locations around the globe.

Integrating robotics activities in science curriculum provides rich opportunities to engage students in real world science and help them to develop conceptual understanding of physics principles through the process of investigation, data analysis, engineering design, and construction. In addition, students become more confident learners and develop better problem-solving and teamwork skills. In this paper we describe a successful use of LEGO® MINDSTORMS® in designing robotics-based activities for teaching high school physics classes. Students design and perform novel science investigations with a toolset that helps them achieve a high reproducibility in their experimental designs. Several example projects that utilize LEGO MINDSTORMS are presented.

Roboticists need to have a solid understanding of hardware and software. The standard computer science education in the United States, however, tends to teach students only about software. To remedy this situation, we explore new ways of teaching them about hardware in a playful way. Realizing that pinball machines are simple robots, we have developed a pinball machine interface between a PC and a recent Lord of the Rings pinball machine, which enables students to implement pinball games and gain knowledge of hardware and interface programming in the process. This paper describes both our pinball machine interface and our experience developing it. As far as we know, this is the first time that anyone has managed to control an existing pinball machine completely.

As educators, we have high hopes for Electronic Tangibles (ETs), we expect ETs to: Interest more students in the study of computing Broaden students' views of computing Invite non-majors to learn something about the computing Attract students to computer science as a major Help students learn about particular ETs Attract students to our classes by incorporating a flashy ET in the course material Improve student understanding of some difficult topics Maintain student interest throughout the class However some important questions arise: Can we and should we extend these benefits throughout the K-20 curriculum? And if we can't, are we guilty of bait-and-switch?

This paper discusses the development of a sequence of undergraduate courses forming the core curriculum in the Robotics Engineering (RBE) B.S. program at Worcester Polytechnic Institute (WPI). The laboratory robotics kit developed for the junior-level courses is presented in detail. The platform is designed to be modular and cost-effective and it is suitable for laboratory based robotics education. The system is ideal not only for undergraduate coursework but also may be adapted for graduate and undergraduate research as well as for exposing K-12 students to STEM.

In 3 experiments, participants learned bone anatomy by using a hand-held controller to rotate an on-screen 3D bone model. The on-screen bone included (OR condition) or did not include (no-OR condition) orientation references—visible lines marking its axes. The learning task involved rotating the on-screen bone to match target orientations. Learning outcomes were assessed by having participants identify anatomical features from different orientations. On the learning task, the OR group performed more accurately, directly, and quickly than the control group and high-spatial individuals outperformed low-spatial individuals. Assessments of anatomy learning indicated that under more challenging conditions, ORs elevated learning by low-spatial individuals to near that of high-spatial individuals. In Experiment 3, orientation references were shown to help learners avoid disorientation due to the symmetrical shape of the object.

Medical experts routinely need to identify the shapes of anatomical structures, and surgeons report that they depend substantially on touch to help them with this process. In this paper, we discuss possible reasons why touch may be especially important for anatomical shape recognition in surgery, and why in this domain haptic cues may be at least as informative about shape as visual cues. We go on to discuss modern surgical methods, in which these haptic cues are substantially diminished. We conclude that a potential future challenge is to find ways to reinstate these important cues and to help surgeons recognize shapes in the restricted sensory conditions of minimally invasive surgery.