As stipulated AAI's leadership underwent a major change in March of this year. Martha Pollack, who had been in the AAAI bylaws, Kautz will serve in this capacity until the 2010 AAAI annual business meeting, after which he will begin his full two-year term as president, starting one year ahead of schedule. In addition, Eric Horvitz, who has already served one year as AAAI past president, has agreed to serve one additional year so that the position will remain filled throughout Kautz's tenure as president. An election will be held this year for the now-open position of president elect. It was with great regret that I came to the decision AAAI is a very strong organization, and Eric that I had to resign my position as president of Horvitz, Henry Kautz, Ted Senator, and Carol AAAI.

ICINCO 2010 will be held July 15-18, 2010, in Funchal (Madeira) Portugal. IE '10 will be held July 20-21 2010, in Kuala Lumpur, Malaysia This page includes forthcoming AAAI sponsored conferences, conferences presented Magazine also maintains a calendar listing that includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. The Thirty-Second Annual Conference IAAI-11 will be held August 7-11, of the Cognitive Science Society. AAAI-10 and IAAI-10 will be held July Twenty-Sixth AAAI Conference on Tenth International Conference on 11-15, 2010, in Atlanta, Georgia USA. EAAI will be held July 13-14, 2010, in Atlanta, Georgia USA.

On Tuesday morning, July 12, the program chairs will welcome attendees, and conference and AAAI awards will be presented. The awards ceremony will be followed by the AAAI-10 keynote address, to be include 199 oral presentations in the is the definitive point of interaction delivered by Leslie Pack Kaelbling main track, as well as 75 additional between entertainment software developers (Massachusetts Institute of Technology) presentations in the special tracks on interested in AI and academic entitled "Intelligent Interaction Bioinformatics, AI and the Web, Challenges and industrial AI researchers. AAAI-10 has an in AI, Integrated Intelligence, by AAAI, the conference is targeted outstanding program of invited presentations, Physically Grounded AI, Nectar, and at both the research and featuring Carla P. Gomes Senior Member, as well as poster presentations commercial communities, promoting (Cornell University), Barry O'Sullivan by a select number of exceptional AI research and practice in the context (University College Cork), David C. technical papers, short papers, of interactive digital entertainment Parkes (Harvard University), and student abstracts, and doctoral systems with an emphasis on commercial Michael Thielscher (The University of consortium abstracts. Registration information with Jay M. Tenenbaum (CollabRx The week is filled with a host of and other program details will Inc.), the 2010 recipient of the other programs, including the AI be available on the AIIDE-10 website Robert S. Engelmore Memorial Lecture Video Competition, the AI Poker at www.aaai.org/aiide10 The IAAI-10 program Semantic Robot Vision Challenge, the Michael Youngblood (University of will also feature talks by Majd Alwan General Game Playing Competition, North Carolina Charlotte). Care Empowered by Applied AI," Registration for AAAI-10, IAAI-10, and Vernor Vinge (San Diego State and EAAI-10 is included in one joint University) on "Species of Mind." fee.

The considerable interest in ARA techniques and the great diversity of the researchers involved had led to work on ARA being presented at many different venues. Consequently, there was a need to have a single forum where researchers of different backgrounds and disciplines could discuss their work on ARA. As a result, the Symposium on Abstraction, Reformulation, and Approximation (SARA) was established in 1994 after a series of workshops in 1988, 1990, and 1992. The current SARA, held at Lake Arrowhead, California, USA, on July 7-10, 2009, is the eighth in this series, following symposia in 1994, 1995, 1998, 2000, 2002, 2005, and 2007. Following a SARA tradition, this symposium brought together researchers with different backgrounds and facilitated lively discussions during and after the talks. There were 30 researchers from North and South America, Europe, and Australia. Additionally, SARA attendees were able to mingle and have fruitful discussions with members of the collocated Symposium on Combinatorial Search (SoCS). The collocation of SoCS was particularly useful in that many modern techniques in combinatorial search frequently utilize ARA methods. Finally, in addition to the regular and poster talks, there were three invited talks delivered by Jeff Orkin (Massachusetts Institute of Technology), Michael Genesereth (Stanford University), and Robert Holte (University of Alberta).

This article reports on the 2008 Reinforcement Learning Competition,  which began in November 2007 and ended with a workshop at the  International Conference on Machine Learning (ICML) in July 2008 in  Helsinki, Finland.  Researchers from around the world developed  reinforcement learning agents to compete in six problems of various  complexity and difficulty.  The competition employed fundamentally  redesigned evaluation frameworks that, unlike those in previous  competitions, aimed to systematically encourage the submission of  robust learning methods. We describe the unique challenges of  empirical evaluation in reinforcement learning and briefly review  the history of the previous competitions and the evaluation  frameworks they employed.  We also describe the novel frameworks  developed for the 2008 competition as well as the software  infrastructure on which they rely.  Furthermore, we describe the six  competition domains and present a summary of selected competition  results.  Finally, we discuss the implications of these results and  outline ideas for the future of the competition.

The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.

Developing intelligent agents and cognitive models is a complex software engineering activity. This article shows how all intelligent agent creation tools can be improved by taking advantage of established software engineering principles such as high-level languages, maintenance-oriented development environments, and software reuse. We describe how these principles have been realized in the Herbal integrated development environment, a collection of tools that allows agent developers to exploit modern software engineering principles.

Process integrated mechanisms (PIM) offer a new approach to the problem of coordinating the activity of physically distributed systems or devices. Current approaches to coordination all have well-recognized strengths and weaknesses. We propose a novel architecture to add to the mix, called the Process Integrated Mechanism (PIM), which enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to its rejection in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously. The PIM models offers promise as an effective infrastructure for handling tasks that require a high degree of time-sensitive coordination between the components, as well as a clean mechanism for coordinating the high-level goals of loosely coupled systems. PIM models enable coordination without the fragility and high communication overhead of centralized control, but also without the uncertainty associated with the system-level behavior of a MAS.The PIM model provides an ease of programming with advantages over both multi-agent sys-tems and centralized architectures. It has the robustness of a multi-agent system without the significant complexity and overhead required for inter-agent communication and negotiation. In contrast to centralized approaches, it does not require managing the large amounts of data that the coordinating process needs to compute a global view. In a PIM, the process moves to the data and may perform computations on the components where the data is locally available, sharing only the information needed for coordination of the other components. While there are many remaining research issues to be addressed, we believe that PIMs offer an important and novel tech-nique for the control of distributed systems.

Abduction is a fundamental and important form of non-monotonic reasoning. Given a knowledge base explaining how the world behaves it aims at finding an explanation for some observed manifestation. In this paper we focus on propositional abduction, where the knowledge base and the manifestation are represented by propositional formulae. The problem of deciding whether there exists an explanation has been shown to be SigmaP2-complete in general. We consider variants obtained by restricting the allowed connectives in the formulae to certain sets of Boolean functions. We give a complete classification of the complexity for all considerable sets of Boolean functions. In this way, we identify easier cases, namely NP-complete and polynomial cases; and we highlight sources of intractability. Further, we address the problem of counting the explanations and draw a complete picture for the counting complexity.

We tackle the problem of multi-class relational sequence learning using relevant patterns discovered from a set of labelled sequences. To deal with this problem, firstly each relational sequence is mapped into a feature vector using the result of a feature construction method. Since, the efficacy of sequence learning algorithms strongly depends on the features used to represent the sequences, the second step is to find an optimal subset of the constructed features leading to high classification accuracy. This feature selection task has been solved adopting a wrapper approach that uses a stochastic local search algorithm embedding a naive Bayes classifier. The performance of the proposed method applied to a real-world dataset shows an improvement when compared to other established methods, such as hidden Markov models, Fisher kernels and conditional random fields for relational sequences.