In this paper, we examine Robot Defense, a computer game that serves as a pedagogical platform for students to explore methods typically covered in an Introductory Artificial Intelligence course. Robot Defense is the synergistic outcome of two NSF funded Course, Curriculum, and Laboratory Improvement (CCLI) projects and was first presented in (Wallace, Russell and Markov 2008). The primary contribution of this paper is to discuss the implementation of the Robot Defense platform and the outcome of its first use in the classroom.

Using robots as part of any curriculum requires careful management of the significant complexity that physical embodiment introduces. Students need to be made aware of this complexity without being overwhelmed by it, and navigating students through this complexity is the biggest challenge faced by an instructor.  Achieving this requires a framework that allows complexity to be introduced in stages, as students' abilities improve. Such a framework should also be flexible enough to provide a range of application environments that can grow with student  sophistication, and be able to quickly change between applications.  It should be portable and maintainable, and require a minimum of overhead to manage in a classroom. Finally, the framework should provide repeatability and control for evaluating the students' work, as well as for performing research. In this paper, we discuss the advantages of a mixed reality approach to applying robotics to education in order to accomplish these challenges.  We introduce a framework for managing mixed reality in the classroom, and discuss our experiences with using this framework for teaching robotics and AI.

We present a tutorial introduction to the area of preference handling - one of the core issues in the design of any system that automates or supports decision making. The main goal of this tutorial is to provide a framework, or perspective, within which current work on preference handling -representation, reasoning, and elicitation - can be understood. Our intention is not to provide a technical description of the diverse methods used, but rather, to provide a general perspective on the problem and its varied solutions and to highlight central ideas and techniques.

This year's conferences were held in Perhaps one of the true litmus tests of any conference is the caliber of the invited speakers. Sensibility: Sentiment Analysis, Opinion and research manager at Microsoft Research) The distinguished Robert S. Englemore Mining, and the Computational who gave his AAAI presidential Memorial Award Lecture was delivered Treatment of Subjective Language"), address, "Artificial Intelligence in the by Kenneth Ford (Florida Institute while Seth C. Goldstein (Carnegie Open World." Mel lon University) discussed revolutionary Chris Urmson (Carnegie Mellon In his lecture, "Toward Cognitive work in self-reconfiguring programmable University), a leading member of the Prostheses," Ford discussed human-centered matter composed of ensembles of submillimeter robots in his DARPA Urban Grand Challenge winning computing to amplify talk, "Realizing Claytronics: A Challenge team, described the race and winning human cognition and perception. Instead of the learning for network analysis in ("From Images to Scenes: Using popular competition, which has his talk, "Making Sense of Complex Lots of Data to Infer Geometric, Photometric, pushed the envelope of mobile robotics Networks." David Haussler (University and Semantic Scene Properties since its inception, this year was of California, Santa Cruz) traced the from a Single Image"), and Lillian host to a Robot Workshop and Exhibition.

The Fourth International Conference on Knowledge Capture was held October 28-31, 2007, in Whistler, British Columbia. The topics covered in the invited talks, technical papers, posters, and demonstrations included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixedinitiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice-taking systems. These events, which were from web-based game-playing systems. The title of his talk was "Human Ken Barker and John Gennari Derek Sleeman noted in his introductory Etzioni's invited talk and had primary responsibilities for comments, knowledge capture is gave some technical details of the systems the conference and workshop programs. In the The best technical paper Since the K-CAP series was initiated, last decade or so, knowledge capture award was presented to Kai Eckert, the K-CAP and European Knowledge has again expanded its horizons significantly Heiner Stuckenschmidt, and Magnus Acquisition Workshop (EKAW) meetings to embrace information-extraction Pfeffer for their paper "Interactive have been held in alternate years, techniques, and more recently Thesaurus Assessment for Automatic with the K-CAP meetings taking place the web and enhanced connectivity Document Annotation."

Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research. Andrew Ng of Stanford University (along with students Ashutosh Saxena and Ellen Klingbeil) focuses on opening arbitrary doors through learning a few visual keypoints, such as the location and type of door handle.

SPARK'08 was the first edition of a workshop series designed to provide a stable, longterm forum where researchers could discuss Workshop (SPARK) was established to help address this issue. Building on precursory events, SPARK'08 was the first workshop designed Scheduling (ICAPS-08) held in Sydney, Australia, in September 2008. Like its immediate predecessor (the ICAPS'07 Workshop on Moving Planning and Scheduling Systems), the 2008 SPARK workshop was collocated with the International Conference on Automated Planning and Scheduling (ICAPS), a premier forum for research in AI planning and scheduling, and the International Conference on Principles and Practice of Constraint Programming (CP). A handful of outstanding application-oriented papers are presented each year at the ICAPS conference. Time and again, in invited talks and in open microphone discussion sessions such as ICAPS's Festivus (where conference participants air their grievances in an open and entertaining way), researchers have lamented the small number of applications papers accepted at conferences such as ICAPS, CP, and the AAAI Conference on Artificial Intelligence.

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13–14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy.

Early work in AI focused on the notion of a goal--an explicit target that must be achieved--and this paradigm is still dominant in AI problem solving. But as application domains become more complex and realistic, it is apparent that the dichotomic notion of a goal, while adequate for certain puzzles, is too crude in general. The problem is that in many contemporary application domains, for example, information retrieval from large databases or the web, or planning in complex domains, the user has little knowledge about the set of possible solutions or feasible items, and what she or he typically seeks is the best that's out there. But since the user does not know what is the best achievable plan or the best available document or product, he or she typically cannot characterize it or its properties specifically. As a result, the user will end up either asking for an unachievable goal, getting no solution in response, or asking for too little, obtaining a solution that can be substantially improved. Of course, the user can gradually adjust the stated goals. This, however, is not a very appealing mode of interaction because the space of alternative solutions in such applications can be combinatorially huge, or even infinite. Moreover, such incremental goal refinement is simply infeasible when the goal must be supplied offline, as in the case of autonomous agents (whether on the web or on Mars).

Human-computer interaction (HCI) is the interdisciplinary study of interaction between people (users) and computers. Its main goal is making computers more user-friendly and easier to use. HCI is concerned with methodologies and processes for designing interfaces, with methods for implementing interfaces, with techniques for evaluating and comparing interfaces, with developing new interfaces and interaction techniques and with developing descriptive and predictive models and theories of interaction [9]. More often than not, user interfaces for theorem provers are developed as a mere add-on to the main proving engine. The result is an interaction design suitable for proof experts only.