In both individual and collective decision making, the space of alternatives from which the agent (or the group of agents) has to choose often has a combinatorial (or multiattribute) structure. We give an introduction to preference handling in combinatorial do - mains in the context of collective decision making and show that the considerable body of work on preference representation and elicitation that AI researchers have been working on for several years is particularly relevant. After giving an overview of languages for compact representation of preferences, we discuss problems in voting in combinatorial domains and then focus on multiagent resource allocation and fair division. These issues belong to a larger field, which is known as computational social choice and which brings together ideas from AI and social choice theory, to investigate mechanisms for collective decision making from a computational point of view. We conclude by briefly describing some of the other research topics studied in computational social choice.

Interactive artificial intelligence systems employ preferences in both their reasoning and their interaction with the user. This survey considers preference handling in applications such as recommender systems, personal assistant agents, and personalized user interfaces. We survey the major questions and approaches, present illustrative examples, and give an outlook on potential benefits and challenges. A popular example is Netflix, which gives customized movie recommendations to each user, using past movie ratings of the user and his or her friends. Netflix and other preference-aware interactive systems share the common aim of aiding the user in carrying out tasks--from finding a product to editing a document--by eliciting preferences from the user, inferring a preference model, and using the model to decide when and how to take action.

Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or mon itoring; for example, they can plan an attack avoiding existing pa trols. Hence, randomized patrolling or monitoring is impor tant, but randomization must provide distinct weights to dif ferent actions based on their complex costs and benefits. To this end, this article describes a promising transition of the lat est in multiagent algorithms into a deployed application. In particular, it describes a software assistant agent called AR MOR (assistant for randomized monitoring over routes) that casts this patrolling and monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines two key features.

Such false-name manipulations have traditionally not been considered in the theory of mechanism design. In this article, we review recent efforts to extend the theory to address this. Because some of these results are very negative, we also discuss alternative models that allow us to circumvent some of these negative results. Some of the most exciting applications of this involve making decisions based on the agents' preferences (for a more detailed discussion, see Conitzer [2010]). For example, in electronic commerce, agents can bid on items in online auctions.

We present a deployed AI system, Calvin, for cosmogenic isotope dating, a domain that is fraught with these difficult issues. Calvin solves these problems using an argumentation framework and a system of confidence that uses twodimensional vectors to express the quality of heuristics and the applicability of evidence. The arguments it produces are strikingly similar to published expert arguments. Calvin is in daily use by isotope dating experts. An automated tool can do boring and repetitive reasoning, freeing experts to do more difficult and creative work.

Modeling interaction with robots raises new and different challenges for dialogue modeling than traditional dialogue modeling with lessembodied machines. We present four case studies of implementing a typical human-robot interaction (HRI) scenario with different stateof-the-art dialogue frameworks in order to identify challenges and pitfalls specific to HRI and potential solutions. The results are discussed with a special focus on the interplay between dialogue and task modeling on robots. Instead of designing interactions for information-oriented query systems--which have also been extended to virtual agents--it has now become necessary to take physical situatedness into account. This means that questions of reactability to dynamic environments, possibly involving multiple modalities, and of potentially open-ended, unstructured interactions, involving multiple tasks at a time, play an important role and have to be considered in the dialogue model.

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-

The six symposia held were AI, the Fundamental Social Aggregation Challenge (cochaired by W. F. Lawless, Don Sofge, Mark Klein, and Laurent Chaudron); Designing Intelligent Robots (cochaired by George Konidaris, Byron Boots, Stephen Hart, Todd Hester, Sarah Osentoski, and David Wingate); Game Theory for Security, Sustainability, and Health (cochaired by Bo An and Manish Jain); Intelligent Web Services Meet Social Computing (cochaired by Tomas Vitvar, Harith Alani, and David Martin); Self-Tracking and Collective Intelligence for Personal Wellness (cochaired by Takashi Kido and Keiki Takadama); and Wisdom of the Crowd (cochaired by Caroline Pantofaru, Sonia Chernova, and Alex Sorokin). The papers of the six symposia were published in the AAAI technical report series. The focus of the AI, The Fundamental Social Aggregation Challenge, and the Autonomy of Hybrid Agent Groups symposium was to explore issues associated with the control of teams of humans, autonomous machines, and robots working together as hybrid agent groups. Bill Lawless of Paine College kicked off the meeting by pointing out the need for a new theory of social dynamics. He showed that majority rule is far better than consensus for group decision processes and proposed a new mathematical model for characterizing social group dynamics based on interdependence.

We focus on two paradigms: logics for cognitive models of agency, and logics used to model the strategic structure of a multiagent system. Logic can be a powerful tool for reasoning about multiagent systems. First of all, logics provide a language in which to specify properties -- properties of an agent, of other agents, and of the environment. Ideally, such a language then also provides a means to implement an agent or a multiagent system, either by somehow executing the specification, or by transforming the specification into some computational form. Second, given that such properties are expressed as logical formulas that form part of some inference system, they can be used to deduce other properties.