In 2011 the Autonomous Agents and Multiagent Systems (AAMAS) conference series celebrated its 10th anniversary, having begun as the successful merger of three related events that had run for some years previously.

Every year, AI Magazine devotes one fourth of its annual production to a special issue based on the Innovative Applications of Artificial Intelligence conference. Because IAAI is the premier venue for documenting the transition of AI technology into application, these special issues provide a snapshot of the state of the art in AI with the practical syllogism in mind; they present work that has value because it delivers value in use.

As a result, it is good to read these articles from a practical perspective. Papers that document deployed systems clarify the motivating application constraints, the match (and mismatch) between problems and technology, the innovations required to surmount barriers to deployment, and the impact of technology on application through practical measures of cost and benefit. Other articles describe applications that are almost feasible, drawn from papers in the IAAI emergent applications track. These papers provide a window into the search for viable applications at an earlier stage in the process of mating task with technology. All of the articles supply insight into the core question of what is feasible and why, which is a useful lens for us, as readers, to employ in viewing our own work. This special issue of AI Magazine contains expanded versions of five papers that describe deployed applications and two papers that discuss emergent applications from IAAI-11 (the article by Warrick and colleagues is from IAAI-10).

This special issue of AI Magazine on dialog with robots brings together a collection of articles on situated dialog. The contributing authors have been working in interrelated fields of human-robot interaction, dialog systems, virtual agents, and other related areas and address core concepts in spoken dialog with embodied robots or agents. Several of the contributors participated in the AAAI Fall Symposium on Dialog with Robots, held in November 2010, and several articles in this issue are extensions of work presented there. The articles in this collection address diverse aspects of dialog with robots, but are unified in addressing opportunities with spoken language interaction, physical embodiment, and enriched representations of context.

In parallel with these efforts, significant advances have also been made in robotics. Innovations in sensing, reasoning, and manipulation have allowed autonomous robots to move beyond the walls of computing labs into the workplace, home, and street. Bringing robots into real-world environments has made it clear to researchers that robots need not only accurately navigate and manipulate objects, but also to work alongside and, ultimately, interact and collaborate with humans. Subsequently, efforts at the intersection of spoken dialogue and human-robot interaction (HRI) have sought to broaden studies of spoken dialogue to richer, more natural, physically situated settings, and have brought to the fore the rich research area of situated dialogue, focused on challenges and opportunities at the intersection of natural language, robotics, and commonsense reasoning. Projects in this realm have addressed challenges with the use of dialogue as enabling coordination among multiple actors, taking into consideration not only the details of the task at hand, but also the dynamic physical and social context in which the actors are immersed and the affordances that embodiment provides. This special issue of AI Magazine on dialogue with robots brings together a collection of articles on situated dialogue.

Recommender systems are tools for interacting with large and complex information spaces. 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. 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.

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.

TLP and the field as a whole made great strides in each of these dimensions. Indeed, the program has helped TL become a recognized subdiscipline of machine learning. Other articles in this special issue detail the work accomplished in TLP; this article focuses on a broad framing of the research conducted and an assessment of its progress, limitations, and challenges, from an admittedly personal but DARPAinfluenced perspective. Traditionally every DARPA program has focused its research by requiring a precise measure of progress. The DARPA TLP decided to measure transfer by comparing the learning of tasks A and B versus the learning of B alone. In figure 1 the curve labeled B represents a traditional learning curve of the performance on target task B as a function of the number of training instances.

Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications.

This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the general problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.