The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

We introduce the first system towards the novel task of answering complex multi-sentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules: question understanding and answering. For question understanding, we define an SQL-like query language that captures the semantic intent of a question; it supports operators like subset, negation, preference and similarity, which are often found in recommendation questions. We train and compare traditional CRFs as well as bidirectional LSTM-based models for converting a question to its semantic representation. We extend these models to a semi-supervised setting with partially labeled sequences gathered through crowdsourc-ing. We find that our best model performs semi-supervised training of BiDiL-STM CRF with hand-designed features and CCM(Chang et al., 2007) constraints. Finally, in an end to end QA system, our answering component converts our question representation into queries fired on underlying knowledge sources. Our experiments on two different answer corpora demonstrate that our system can significantly outperform baselines with up to 20 pt higher accuracy and 17 pt higher recall.

Roughly 30 people attended this workshop. This article summarizes the papers presented at the workshop and highlights some of the questions and issues raised during the discussion. The task of selecting the best representation for solving a problem by means of automatically reformulating the problem is a core challenge in AI. Saul Amarel (1968) outlined the impact of different representations of the missionaries and cannibals problem on the performance of the algorithms used to solve the problem. He also proposed automating the reformulation process and, consequently, the process of selecting representations: The general problem of representation is concerned with the relationship between different ways of formulating a problem to a problem solving system and the efficiency with which the system can be expected to find a solution to the problem.

The symposium took place in July 2009 in Lake Arrowhead, California. Consequently, ARA techniques have been studied in various subfields in AI and related disciplines and have been used in various settings including automated reasoning, cognitive modeling, constraint programming, design, diagnosis, machine learning, model-based reasoning, planning, reasoning, scheduling, search, theorem proving, and intelligent tutoring. 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 system ensures a high standard of quality in customer service, airport safety, and use of stand resources. This article describes our experience in developing an AI system using standard off-the-shelf software components. Although there were some initial hitches when the new airport opened on 6 July 1998, operations quickly returned to normal within a week's time. Within a month, operational statistics surpassed those of the old airport--80 percent of all flights were on time or within 15 minutes of schedule, all passengers cleared immigration within 15 minutes, and average baggage waiting time was only 10 minutes. During the 1998 Christmas holiday, HKIA serviced about 100,000 passengers daily and maintained equally high service standards.

You wait forever for one to come along, and then two come along at once. In this case, there has been a large gap in the market for a theoretical introduction to constraint programming ever since Edward Tsang's Foundations of Constraint Satisfaction (1993) went out of print. Therefore, we are very pleased to see two books written by two of the leading researchers in this field come along to fill the gap. Constraint programming is a very active research area within AI. It is a highly successful technology for solving a wide range of combinatorial problems, including scheduling, rostering, assignment, routing, and design. A number of companies, like ILOG, Dash Optimization, and Parc Technologies, market model building and constraint programming toolkits, which are used by companies as diverse as Amazon.com, Constraint programming is a declarative style of modeling combinatorial problems in which the user identifies the decision variables, their possible domain of values, and specifies constraints over the allowed values (for example, no two of these variables can take the same value). Sophisticated but general purpose AI search techniques like constraint propagation (to prune irrelevant parts of the search tree) and dependency directed backtracking can then be used to find solutions. Given the many advances made in constraint programming over the last decade, a new text would have been needed even if Edward Tsang's book had remained in print. These two new texts are written by two of the leading researchers in this field. Principles of Constraint Programming by Krzysztof Apt contains chapters that cover topics like local consistency, constraint propagation, linear equations, interval reasoning, and search. Constraint Processing by Rina Dechter covers similar ground but also has chapters that cover topics like local search, tree decomposition methods, optimization, and probabilistic networks more extensively. Dechter's book also contains a chapter by David Cohen and Peter Jeavons on tractability and one by Francesca Rossi on constraint logic programming. There is much in common between the two books. This is perhaps not so surprising since Krzysztof Apt thanks Rina Dechter for much useful discussion that helped him enter the field and start doing research in the area.

A prerequisite for intelligent behavior is the ability to reason about actions and their effects. This ability is the essence of the classical AI planning problem in which plans are constructed by reasoning about how available actions can be applied to achieve various goals. For this reasoning process to occur, the planner must be aware of its available actions, the situations in which they are applicable, and the changes affected in the world by their execution. Classical AI planners typically use a highlevel, symbolic representation of actions (for example, well-formed formulas from predicate calculus). Although this type of representational scheme is attractive from a computational standpoint, it cannot adequately represent the intricacies of a domain that includes complex actions, such as robotic assembly (consider, for example, that any geometric configuration of the robotic manipulator is a rather complex function of six joint angles).

Complex electromechanical products, such as high-end printers and photocopiers, are designed as families, with reusable modules put together in different manufacturable configurations, and the ability to add new modules in the field. The modules are controlled locally by software that must take into account the entire configuration. This poses two problems for the manufacturer. The first is how to make the overall control architecture adapt to, and use productively, the inclusion of particular modules. The second is to decide, at design time, whether a proposed module is a worthwhile addition to the system: will the resulting system perform enough better to outweigh the costs of including the module?

The SAT Conference on Theory and Applications of Satisfiability Testing was held in Lisbon, Portugal, 28-31 May 2007. The conference, which attracted a record-breaking 80 participants, featured 34 papers and two invited presentations. The venue also included the SAT competition, the QBF evaluation, the PB evaluation, and the MAX-SAT evaluation. Moreover, SAT and extensions of SAT find many practical applications, including planning, software and hardware model checking, bioinformatics, equivalence check ing, test-pattern generation, software package installation, and cryptography. The annual SAT conference is now widely recognized as "the venue" for AI Magazine Volume 28 Number 4 (2007) ( AAAI) This year marked the tenth SAT meeting.

I consider how to represent and reason with users' preferences. While areas of economics like social choice and game theory have traditionally considered such topics, I will argue that computer science and artificial intelligence bring some fresh perspectives to the study of representing and reasoning with preferences. For instance, I consider how we can elicit preferences efficiently and effectively. With one agent, the agent's desired goal may not be feasible. The agent wants a cheap, low-mileage Ferrari, but no such car exists.