The AAAI-10 Workshop program was held Sunday and Monday, July 11–12, 2010 at the Westin Peachtree Plaza in Atlanta, Georgia. The AAAI-10 workshop program included 13 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Fun, Bridging the Gap between Task and Motion Planning, Collaboratively-Built Knowledge Sources and Artificial Intelligence, Goal-Directed Autonomy, Intelligent Security, Interactive Decision Theory and Game Theory, Metacognition for Robust Social Systems, Model Checking and Artificial Intelligence, Neural-Symbolic Learning and Reasoning, Plan, Activity, and Intent Recognition, Statistical Relational AI, Visual Representations and Reasoning, and Abstraction, Reformulation, and Approximation. This article presents short summaries of those events.

Friedgut, Kalai, and Nisan also assume that there is a single manipulator. In the first part of the survey we discussed worstcase Their main insight is that a completely random hardness as a barrier against manipulation in manipulation may succeed with nonnegligible elections.

Indeed, game theory now serves as perhaps the main analytical framework in microeconomic theory, as evidenced by its prominent role in economics textbooks (for example, Mas-Colell, Whinston, and Green 1995) and by the many Nobel prizes in economic sciences awarded to prominent game theorists. Artificial intelligence got its start shortly after game theory (McCarthy et al. 1955), and indeed pioneers such as von Neumann and Simon made early contributions to both fields (see, for example, Findler [1988], Simon [1981]). Both game theory and AI draw (nonexclusively) on decision theory (von Neumann and Morgenstern 1947); for example, one prominent view defines artificial intelligence as "the study and construction of rational agents" (Russell and Norvig 2003), and hence takes a decision-theoretic approach when the world is stochastic. However, artificial intelligence spent most of its first 40 years focused on the design and analysis of agents that act in isolation, and hence had little need for game-theoretic analysis. Starting in the mid to late 1990s, game theory became a major topic of study for computer scientists, for at least two main reasons. First, economists began to be interested in systems whose computational properties posed serious barriers to practical use, and hence reached out to computer scientists; notably, this occurred around the study of combinatorial auctions (see, for example, Cramton, Shoham, and Steinberg 2006). Second, the rise of distributed computing in general and the Internet in particular made it increasingly necessary for computer scientists to study settings in which intelligent agents reason about and interact with other agents.

In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why? What factors and goals are most important in their design? The primary goal of a prediction mechanism is to obtain and aggregate dispersed information, which often exists in tacit forms as beliefs, opinions, or judgements of agents. Coalescing information is a necessary first step for decision making in almost all domains. For example, consider seasonal influenza, a significant cause of illness and death around the world. Although it recurs every year, the geographic location, timing, magnitude, and duration of outbreaks vary widely. Many people possess relevant pieces of the full information puzzle, including doctors who meet patients, clinical microbiologists who perform respiratory culture tests, pharmacists who fill prescriptions, people who have the flu, and people who know people who have the flu.

Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in real-world settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision-making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences. In addition to tracing some recent developments, we point to ongoing research challenges.

Computational pool is a relatively recent entrant into the group of games played by computer agents. It features a unique combination of properties that distinguish it from oth- ers such games, including continuous action and state spaces, uncertainty in execution, a unique turn-taking structure, and of course an adversarial nature. This article discusses some of the work done to date, focusing on the software side of the pool-playing problem. We discuss in some depth CueCard, the program that won the 2008 computational pool tournament. Research questions and ideas spawned by work on this problem are also discussed. We close by announcing the 2011 computational pool tournament, which will take place in conjunction with the Twenty-Fifth AAAI Conference.

Many complex systems, ranging from neural cell assemblies to insect societies, involve and rely on some division of labor. How to enforce such a division in a decentralized and distributed way, is tackled in this paper, using a spiking neuron network architecture. Specifically, a spatio-temporal model called SpikeAnts is shown to enforce the emergence of synchronized activities in an ant colony. Each ant is modelled from two spiking neurons; the ant colony is a sparsely connected spiking neuron network. Each ant makes its decision (among foraging, sleeping and self-grooming) from the competition between its two neurons, after the signals received from its neighbor ants. Interestingly, three types of temporal patterns emerge in the ant colony: asynchronous, synchronous, and synchronous periodic foraging activities - similar to the actual behavior of some living ant colonies. A phase diagram of the emergent activity patterns with respect to two control parameters, respectively accounting for ant sociability and receptivity, is presented and discussed.

Games of incomplete information, or Bayesian games, are an important game-theoretic model and have many applications in economics. We propose Bayesian action-graph games (BAGGs), a novel graphical representation for Bayesian games. BAGGs can represent arbitrary Bayesian games, and furthermore can compactly express Bayesian games exhibiting commonly encountered types of structure including symmetry, action- and type-specific utility independence, and probabilistic independence of type distributions. We provide an algorithm for computing expected utility in BAGGs, and discuss conditions under which the algorithm runs in polynomial time. Bayes-Nash equilibria of BAGGs can be computed by adapting existing algorithms for complete-information normal form games and leveraging our expected utility algorithm. We show both theoretically and empirically that our approaches improve significantly on the state of the art.

We study worst-case bounds on the quality of any fixed point assignment of the max-product algorithm for Markov Random Fields (MRF). We start proving a bound independent of the MRF structure and parameters. Afterwards, we show how this bound can be improved for MRFs with particular structures such as bipartite graphs or grids. Our results provide interesting insight into the behavior of max-product. For example, we prove that max-product provides very good results (at least 90% of the optimal) on MRFs with large variable-disjoint cycles (MRFs in which all cycles are variable-disjoint, namely that they do not share any edge and in which each cycle contains at least 20 variables).

We study several classes of interactive assistants from the points of view of decision theory and computational complexity. We first introduce a class of POMDPs called hidden-goal MDPs (HGMDPs), which formalize the problem of interactively assisting an agent whose goal is hidden and whose actions are observable. In spite of its restricted nature, we show that optimal action selection in finite horizon HGMDPs is PSPACE-complete even in domains with deterministic dynamics. We then introduce a more restricted model called helper action MDPs (HAMDPs), where the assistant's action is accepted by the agent when it is helpful, and can be easily ignored by the agent otherwise. We show classes of HAMDPs that are complete for PSPACE and NP along with a polynomial time class. Furthermore, we show that for general HAMDPs a simple myopic policy achieves a regret, compared to an omniscient assistant, that is bounded by the entropy of the initial goal distribution. A variation of this policy is shown to achieve worst-case regret that is logarithmic in the number of goals for any goal distribution.