The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4-6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information, and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environments. The highlights of each symposium are presented in this report. The goal of the AAAI Fall Symposium on Advances in Cognitive Systems was to bring together researchers who are interested in developing intelligent systems that demonstrate the full range of human cognitive abilities and to report progress on this daunting task. The original aims of artificial intelligence, when it was launched in the late 1950s, were to explain intelligence in computational terms and to reproduce the entire range of human cognitive abilities in computational artifacts. Although the field has seen impressive advances in the last few decades, many researchers have, in the process, forgotten or abandoned these important goals. The purpose of the Fall Symposium on Advances in Cognitive Systems was to bring together scientists who remained committed to AI's original vision. The meeting received 50 paper submissions and it was attended by more than 75 participants, suggesting that there remains substantial interest in this view on the discipline. Research in cognitive systems, as reflected by the contributors to the meeting, differs from what has become mainstream AI in five basic ways.

In this article we report on the exhibits and challenges shown at the AAAI 2011 Robotics Program in San Francisco. The event included a broad demonstration of innovative research at the intersection of robotics and artificial intelligence. Through these multiyear challenge events, our goal has been to focus the research community's energy toward common platforms and common problems to work toward the greater goal of embodied AI. The program has a long tradition of demonstrating innovative research at the intersection of robotics and artificial intelligence. In both the workshop and exhibition portions of the event, we strive to have the robotics program be a venue that pushes the science of embodied AI forward. Over the past few years, a central point of the event has been the discussion of common robot platforms and software, with the primary goal of focusing the research community's energy toward common "challenge" tasks. On the day before the exhibition the participants convened a workshop of 18 short talks. Each track's exhibitors presented a summary of their exhibit. In addition, four guest speakers provided a broader context for all of the exhibitors' efforts. The first guest speaker was the National Science Foundation's Sven Koenig, who highlighted several federal programs that support projects in embodied intelligence. Koenig also provided insights into some of these program's specific priorities, such as international collaborations and educational engagement. Guest speakers from Willow Garage and Bosch presented cutting-edge work with the PR2, Willow's mobile two-arm manipulator platform. Bosch detailed its Remote Lab, which provides researchers anywhere with full access to the sensing and mobile manipulation capabilities of a PR2. Willow Garage featured some of its most recent work, in which point clouds (Anderson et al. 2011) are parsed not only to build generic three-dimensional scene models but also task-specific structures such as cabinet and drawer handles. Those structures, in turn, seed the automatic creation of task sequences for object retrieval in unconstrained human environments. Nataniel Dukan of Nao Robotics presented the workshop's final guest talk, a broad overview of humanoid robotics's current resources, along with a compelling vision for where those technologies will be in the next three to five years. Without providing specifics of Aldebaran's unannounced plans, Dukan hinted that the actuation and sensing needed for com-

In this article, we introduce Mechanix, a sketch-based deployed tutoring system for engineering students enrolled in statics courses. Our system not only allows students to enter planar truss and free-body diagrams into the system, just as they would with pencil and paper, but our system also checks the student's work against a hand-drawn answer entered by the instructor, and then returns immediate and detailed feedback to the student. Students are allowed to correct any errors in their work and resubmit until the entire content is correct and thus all of the objectives are learned. Since Mechanix facilitates the grading and feedback processes, instructors are now able to assign more free-response questions, increasing teacher's knowledge of student comprehension. Furthermore, the iterative correction process allows students to learn during a test, rather than simply display memorized information.

We highlight progress in terms of macro-and microadaptivity. Macroadaptivity refers to a system's capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system's capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro-and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue, and natural language-processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language-processing techniques are key features of Deep-Tutor, the first intelligent tutoring system based on learning progressions.

Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for datadriven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference. AI methods have advanced considerably since those early days, and so have intelligent tutoring systems. Today, intelligent tutoring systems are in widespread use in K-12 schools and colleges and are enhancing the student learning experience (for example, Graesser et al. [2005]; Mitrovic [2003]; VanLehn [2006]).

The core of this personalized instruction is a student model: the ITS component in charge of assessing student traits and states relevant to tailor the tutorial interaction to specific student needs during problem solving. There are however, other educational activities that can help learners acquire the target skills and abilities at different stages of learning including, among others, exploring interactive simulations and playing educational games. This article de - scribes research on creating student models that support personalization for these novel types of interactions, their unique challenges, and how AI and machine learning can help. Intelligent tutoring systems (ITSs) are the ultimate example of this challenge: their goal is to provide instruction personalized to the specific needs of each learner, as good human tutors do. But understanding these needs can be extremely hard, because it entails modeling and capturing that complex ensemble of processes and states that constitutes human learning.

Intelligent game-based learning environments integrate commercial game technologies with AI methods from intelligent tutoring systems and intelligent narrative technologies. This article introduces the Crystal Island intelligent game-based learning environment, which has been under development in the authors' laboratory for the past seven years. After presenting Crystal Island, the principal technical problems of intelligent game-based learning environments are discussed: narrative-centered tutorial planning, student affect recognition, student knowledge modeling, and student goal recognition. The burgeoning field of game-based learning has made significant advances, including theoretical developments (Gee 2007), as well as the creation of gamebased learning environments for a broad range of K-12 subjects (Habgood and Ainsworth 2011; Ketelhut et al. 2010; Warren, Dondlinger, and Barab 2008) and training objectives (Johnson 2010; Kim et al. 2009). Of particular note are the results of recent empirical studies demonstrating that in addition to game-based learning environments' potential for motivation, they can enable students to achieve learning gains in controlled laboratory settings (Habgood and Ainsworth 2011) as well as classroom settings (Ketelhut et al. 2010).

Useful feedback makes use of models of domain-specific knowledge, especially models that are commonly held by potential students. To empirically determine what these models are, student data can be clustered to reveal common misconceptions or common problem-solving strategies. This article describes how analogical retrieval and generalization can be used to cluster automatically analyzed handdrawn sketches incorporating both spatial and conceptual information. We use this approach to cluster a corpus of hand-drawn student sketches to discover common answers. Common answer clusters can be used for the design of targeted feedback and for assessment.

Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). In this column I describe my experience adapting the content and infrastructure from massive, open, online courses (MOOCs) to enhance my courses in the Department of Electrical Engineering and Computer Science at Vanderbilt University. I begin with my informal, early use of MOOC content and then move to two deliberatively designed strategies for adapting MOOCs to campus (that is, wrappers and small private online classes [SPOCs]). I describe student reactions and touch on selected policy and institutional considerations. In the never-ending search for increasing student bang-for-the-buck, I was motivated to increase the bang, rather than reduce the buck, the latter being well above my pay grade.

This article presents new algorithms for inferring users' activities in a class of flexible and open-ended educational software called exploratory learning environments (ELEs). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in ELEs and visualizing these activities to students. It describes a new plan-recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries.