This editorial explains the benefits of preferences for AI systems and draws a picture of current AI research on preference handling. It thus provides an introduction to the topics covered by this special issue on preference handling. To act autonomously, the systems must choose among different actions and means of expression; to intelligently support humans' actions, they must understand and respond to the humans' choices. It is a natural assumption that agents who are acting in the world are experiencing the consequences of their actions and are not indifferent with respect to those experiences. An autonomous system such as a Mars rover can experience the consequences of moving along a path by measuring the energy consumption after the move, which will depend on the difficulty of the chosen path, and then judge whether the path was good or bad.

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 online e-commerce applications such as Amazon.com, This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas.

The International SAT Solver Competition is today an established series of competitive events aiming at objectively evaluating the progress in state-of-the-art procedures for solving Boolean satisfiability (SAT) instances. Over the years, the competitions have significantly contributed to the fast progress in SAT solver technology that has made SAT a practical success story of computer science. This short article provides an overview of the SAT solver competitions. In addition to its theoretical importance, major advances in the development of robust implementations of decision procedures for SAT, SAT solvers, have established SAT as an important declarative approach for attacking various complex search and optimization problems. Modern SAT solvers are routinely used as core solving engines in vast numbers of different AI and industrial applications.

In this report we motivate research in this area, give an overview of past RTS game AI competitions, and discuss future directions. TS games -- such as StarCraft by Blizzard Entertainment and Command and Conquer by Electronic Arts -- are popular video games that can be described as real-time war simulations in which players delegate units under their command to gather resources, build structures, combat and support units, scout opponent locations, and attack. The winner of an RTS game usually is the player or team that destroys the opponents' structures first. Unlike abstract board games like chess and go, moves in RTS games are executed simultaneously at a rate of at least eight frames per second. In addition, individual moves in RTS games can consist of issuing simultaneous orders to hundreds of units at any given time.

The research addressed in the autonomous agents field covers a wide spectrum of levels from the cognitive to the organizational, exploits diverse mechanisms and approaches, and has had a major impact on many aspects of artificial intelligence research. 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. The 2011 AAMAS conference received 575 submissions, and 126 papers were selected for publication as full papers. Representation under all submissions of topics (measured by first keyword) was broad, with top counts in areas such as teamwork, coalition formation, and coordination (31), distributed problem solving (30), game theory (30), planning (26), multiagent learning (24), and trust, reliability, and reputation (17). The tag cloud (figure 1), generated from the titles of the full papers at the conference, conveys a sense of the relative prominence of topics.

The competition consists of two main tracks: the ASP system track and the model and solve track. The traditional system track compares dedicated answer set solvers on ASP benchmarks, while the model and solve track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP Competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses the lessons learned so far. The main goal of ASP is to provide a versatile declarative modeling framework with many attractive characteristics. These features allow turning -- with little to no effort -- problem statements of computationally hard problems into executable formal specifications, also called answer set programs.

The workshops took place October 8-9, 2012, at Stanford University. This report contains summaries of the activities of those four workshops. With the advent of the BWAPI StarCraft programming interface, interest in real-time strategy (RTS) game AI has increased considerably. At the 2011 AIIDE conference, several papers on the subject were presented, ranging from build order planning, over state estimation, to plan recognition. In addition, a panel discussion on RTS game AI took place, the StarCraft competition was discussed, prizes were awarded, and two exhibition match replays were shown.

Within the time of a human generation, computer technology will be capable of producing computers with as many artificial neurons as there are neurons in the human brain. Within two human generations, intelligists (AI researchers) will have discovered how to use such massive computing capacity in brainlike ways. This situation raises the likelihood that twenty-first century global politics will be dominated by the question, Who or what is to be the dominant species on this planet? This article discusses rival political and technological scenarios about the rise of the artilect (artificial intellect, ultraintelligent machine) and launches a plea that a world conference be held on the socalled "artilect debate." Many years ago, while reading my first book on molecular biology, I realized not only that living creatures, including human beings, are biochemical machines, but also that one day, humanity would sufficiently understand the principles of life to be able to reproduce life artificially (Langton 1989) and even create a creature more intelligent than we are.

This meeting represented the most significant transformation in the history of IAAI. IAAI-97 consisted of two paper tracks as well as invited talks and panels. The first paper track, Deployed-Application Case Studies, comprised papers about deployed AI systems that are relied on for operations and have clearly defined business value. This track was equivalent to previous IAAI programs. The deployed applications track's standards for innovation recognize four types: (1) first application of an AI technique in a deployed application, (2) application of an AI technique to a new domain, (3) a high business payoff, and (4) a novel integration of techniques.

This special issue issue of AI Magazine presents six articles on some of the most interesting question-answering systems in development today. Included are articles on Vulcan's Project Halo, Cyc's Semantic Research Assistant, IBM's Watson, True Knowledge, and the University of Washington's TextRunner. Even though AI has diversified much beyond the notion of intelligent behavior proposed in the Turing test, QA remains a fundamental capability needed by a large class of systems. The QA problem extends beyond AI systems to many analytical tasks that involve gathering, correlating, and analyzing information in ways that can naturally be formulated as questions. Ultimately, questions are an interface to systems that provide such analytic capabilities, and the need to provide this interface has increased dramatically over the past decade with the explosion of information available in digital form.