We at the NASA laboratory believed that our best work came when we simultaneously advanced AI theory and provided immediately usable solutions for current NASA problems. "Solving Large-Scale Constraint Satisfaction and Scheduling Problems Using a Heuristic Repair Method," by Steve Minton, Mark Johnston, Andy Phillips, and Phil Laird clearly achieved both. It proved that local search and repair was applicable to a wide class of constraint satisfaction problems and clearly explicated the theory behind that proof.

The basic notion of false-name-proofness allows for useful mechanisms under certain circumstances, but in general there are impossibility results that show that false-name-proof mechanisms have severe limitations. One may react to these impossibility results by saying that, since false-name-proof mechanisms are unsatisfactory, we should not run any important mechanisms in highly anonymous settings--unless, perhaps, we can find some methodology that directly prevents false-name manipulation even in such settings, so that we are back in a more typical mechanism design context. Because the Internet is so attractive as a platform for running certain types of mechanisms, it seems unlikely that the organizations running these mechanisms will take them offline. As a result, perhaps the most promising approaches at this point are those that combine techniques from mechanism design with other techniques discussed in this article.

We provide an overview of more than two decades of work, mostly in AI, that studies computational complexity as a barrier against manipulation in elections. We provide an overview of more than two decades of work, mostly in AI, that studies computational complexity as a barrier against manipulation in elections.

Much of AI is concerned with the design of intelligent agents. 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.

We briefly survey the rise of game theory as a topic of study in artificial intelligence, and explain the term algorithmic game theory. Finally, we give short summaries of each of the six articles appearing in this issue.

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.

The basic notion of false-name-proofness allows for useful mechanisms under certain circumstances, but in general there are impossibility results that show that false-name-proof mechanisms have severe limitations. One may react to these impossibility results by saying that, since false-name-proof mechanisms are unsatisfactory, we should not run any important mechanisms in highly anonymous settings—unless, perhaps, we can find some methodology that directly prevents false-name manipulation even in such settings, so that we are back in a more typical mechanism design context. However, it seems unlikely that the phenomenon of false-name manipulation will disappear anytime soon. Because the Internet is so attractive as a platform for running certain types of mechanisms, it seems unlikely that the organizations running these mechanisms will take them offline. Moreover, because a goal of these organizations is often to get as many users to participate as possible, they will be reluctant to use high-overhead solutions that discourage users from participating. As a result, perhaps the most promising approaches at this point are those that combine techniques from mechanism design with other techniques discussed in this article. It appears that this is a rich domain for new, creative approaches that can have significant practical impact.

This article includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI.

We at the NASA laboratory believed that our best work came when we simultaneously advanced AI theory and provided immediately usable solutions for current NASA problems. “Solving Large-Scale Constraint Satisfaction and Scheduling Problems Using a Heuristic Repair Method,” by Steve Minton, Mark Johnston, Andy Phillips, and Phil Laird clearly achieved both. It proved that local search and repair was applicable to a wide class of constraint satisfaction problems and clearly explicated the theory behind that proof.

The Doctoral Consortium materials; a workshop for of ideas between basic and applied AI. (DC) provides an opportunity for a mentoring new faculty, instructors, IAAI-11 will consider papers in two group of Ph.D. students to discuss and and graduate students on teaching; an tracks: (1) deployed application case explore their research interests and career Educational Video Track within the studies and (2) emerging applications objectives with a panel of established AAAI-11 Video program; and a Student/Educator or methodologies.