Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.

Using the evaluation framework, leading recommendation solutions were analyzed, compared and ranked. Findings are summarized in Aldrich (2011). This article describes the business models, components, and tasks of today's commercial recommender solutions; describes how these systems are deployed in practice; analyzes how recommendation solutions are evaluated by businesses; presents the current recommendation solution landscape; identifies the shortcomings of current solutions from a commercial perspective; and ends with some ideas of what the future might hold for recommendation solutions in commercial environments. In the following pages, I refer to the company providing the recommendation technology as the vendor, the company implementing this technology on its website as the client, and the user interacting with the website to acquire a product or obtain a service as the customer or simply user. This diagram represents Certona's Resonance recommendation platform, which is typical of commercial recommender systems in its highlevel architecture.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI International Conference on Pattern Magazine also maintains a calendar listing that includes nonaffiliated conferences Recognition Applications and Methods. AIIDE-11 will be Flairs-2012 will be held May 23-25, HRI2012 will be held March 5-8, held October 11-14, 2011 at Stanford 2012, in Marco Island, Florida. ICEIS 2012 will be held June 28 Trinity College in Dublin, Ireland. ICINCO 2012 will be held March 26-28, 2012 at Stanford Third International Joint Conference held July 28-31, 2012 in Rome, Italy.

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

Recommender systems have been part of the Internet for almost two decades. Dozens of vendors have built recommendation technologies and taken them to market in two waves, roughly aligning with the web 1.0 and 2.0 revolutions. Today recommender systems are found in a multitude of online services. They have been developed using a variety of techniques and user interfaces. They have been nurtured with millions of users’ explicit and implicit preferences (most often with their permission). Frequently they provide relevant recommendations that increase the revenue or user engagement of the online services that operate them. However, when we evaluate the current generation of recommender systems from the point of view of the “recommendee,” we find that most recommender systems serve the goals of the business instead of their users’ interests. Thus we believe that the big promise of recommender systems has yet to be fulfilled. We foresee a third wave of recommender systems that act directly on behalf of their users across a range of domains instead of acting as a sales assistant. We also predict that such new recommender systems will better deal with information overload, take advantage of contextual clues from mobile devices, and utilize the vast information and computation stores available through cloud-computing services to maximize users’ long-term goals

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

With the recent dramatic transformations in the world of digital music, a music listener is now just a couple of clicks away from being able to listen to nearly any song that has ever been recorded. With so much music readily available, tools that help a user find new, interesting music that matches her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening taste, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.

This year, the Robotics Exhibition included two such robotics challenge problems: manipulation and learning by demonstration. In the Small-Scale Manipulation Challenge four teams demonstrated systems playing robotic chess. This exhibit was organized by David Touretzky and Monica D. Anderson. In the Learning by Demonstration Challenge, three teams demonstrated systems learning a block-sorting task. This exhibit was organized by Sonia Chernova. Additionally, this year marked another successful turnout for the Robotics Education Track, organized by Zachary Dodds, which highlights student-and educator-led robotics projects. In this article we give a summary of these three components of the exhibition.

The 2011 AAAI Classic Paper Award was given to the authors of the most influential papers from the Tenth National Conference on Artificial Intelligence, held in 1992 in San Jose, California. The award was presented to Mitchell received his BSc in cognitive process. The winning papers were selected Hector Levesque, David Mitchell, and science and artificial intelligence at by the program chairs with the Bart Selman for their two papers, Hard the University of Toronto, his MSc in help of area chairs and members of the and Easy Distribution of SAT Problems computing science from Simon Fraser senior program committee. Honors and A New Method for Solving Hard University, and his PhD in computer went to Jessica Davies (University of Satisfiability Problems. Paris Sud 11), Nina Narodytska to the area of automated Bart Selman is a professor of computer (NICTA and University of New South reasoning via methods and analyses science at Cornell University.