The Tekkotsu "crew" is a collection of interacting software components designed to relieve a programmer of much of the burden of specifying low-level robot behaviors. Using this abstract approach to robot programming we can teach beginning roboticists to develop interesting robot applications with relatively little effort.

We present a new robot platform, the Finch, that was designed to align with the learning goals and concepts taught in introductory computer science courses. The Finch was developed in the context of the CSbots program, the goal of which is to improve retention and learning in computer science courses through the use of robots and other physically embodied hardware. This paper concentrates on design constraints that were determined in earlier CSbots studies and how those constraints were instantiated by the Finch. We also present some preliminary results from pilot studies in which Finch robots were used in CS1 and CS2 classes.

In this paper, we describe the outline for a course-long information retrieval (IR) project. The project guides the students in constructing a working IR system from the ground up. The first half of the project is structured and closely follows common foundational IR concepts. During this portion of the project, a bare-bones IR system is constructed. For the last half of the project, students (in groups) implement research-driven extensions to the basic system with the additional constraint that their project must integrate with the base system. By the end, the students have worked on a large software project (~40 classes with thousands of lines of code) in a group setting as well as been introduced to the research process. This project plan has been successfully used in an undergraduate course; resources including starter code, solutions, and an example IR system with project write-ups are available.

In Australia, the Scientists-in-Schools program partners professional scientists with teachers from K-12 schools to improve early engagement and educational outcomes in the sciences and mathematics.  An overview of the developing syllabus of a K-6 course resulting from the pairing of a senior AI researcher with teachers from a K-6 (primary) school is presented. Now entering its third year, the course introduces the basic concepts, vocabulary and history of science generally and AI specifically in a manner that emphasises student engagement and provides a challenging but age appropriate syllabus. Reflecting on the course at this time provides an action research basis for ongoing maturation of the syllabus, and the paper is presented in that light.

Mixed reality is an important classroom tool for managing complexity from both the students' and instructor's standpoints. It can be used to provide important scaffolds when introducing robotics, by allowing elements of perception and control to be abstracted, and these abstractions removed as a course progresses (or left in place to introduce robotics to younger groups of students). In prior work, we have illustrated the potential of this approach both in providing scaffolding, building an inexpensive robotics laboratory, and also providing control of evaluation of robotics environments for student evaluation and scientific experimentation. In this paper, we explore integrating extensions and improvements to the mixed reality components themselves as part of a course in applied artificial intelligence and robotics. We present a set of assignments that in addition to exploring robotics concepts, actively integrate creating or improving mixed reality components. We find that this approach better leverages the advantages brought about by mixed reality in terms of student motivation, and also provides some very useful software engineering experience to the students.

On October 8-10, 2009 an interdisciplinary group met at the Wylie Center in Beverley, Massachusetts to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in computational modeling is described by three different levels of representation; (2) there is a paucity of studies at the highest, most abstract level aimed at inferring the meaning or message of the narrative; and (3) there is a need to establish a standard data bank of annotated narratives, analogous to the Penn Treebank.

This article reports on the 2008 Reinforcement Learning Competition,  which began in November 2007 and ended with a workshop at the  International Conference on Machine Learning (ICML) in July 2008 in  Helsinki, Finland.  Researchers from around the world developed  reinforcement learning agents to compete in six problems of various  complexity and difficulty.  The competition employed fundamentally  redesigned evaluation frameworks that, unlike those in previous  competitions, aimed to systematically encourage the submission of  robust learning methods. We describe the unique challenges of  empirical evaluation in reinforcement learning and briefly review  the history of the previous competitions and the evaluation  frameworks they employed.  We also describe the novel frameworks  developed for the 2008 competition as well as the software  infrastructure on which they rely.  Furthermore, we describe the six  competition domains and present a summary of selected competition  results.  Finally, we discuss the implications of these results and  outline ideas for the future of the competition.

The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

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