In this article, we first survey the three major types of computer music systems based on AI techniques: (1) compositional, (2) improvisational, and (3) performance systems. Representative examples of each type are briefly described. Then, we look in more detail at the problem of endowing the resulting performances with the expressiveness that characterizes human-generated music. This is one of the most challenging aspects of computer music that has been addressed just recently. The main problem in modeling expressiveness is to grasp the performer's "touch," that is, the knowledge applied when performing a score.

This special issue of AI Magazine is dedicated to the proposition that problems populate the path to insight, implying that experiences and lessons learned should be shared. In this view, problems are signposts, not roadblocks, which guide us towards better solutions. Bugs, surprises, and anomalies also become powerful instructional tools that reveal assumptions, expose design flaws, and chart the boundaries of current technology. This perspective motivated our work on this special issue. When researchers publish success stories, we commonly leave process out of the narrative.

This special issue is based on the premise that in order to achieve human-level artificial intelligence researchers will have to find ways to integrate insights from multiple computational frameworks and to exploit insights from other fields that study intelligence. Articles in this issue describe recent approaches for integrating algorithms and data structures from diverse subfields of AI. Much of this work incorporates insights from neuroscience, social and cognitive psychology or linguistics. The new applications and significant improvements to existing applications this work has enabled demonstrates the ability of integrated systems and research to continue progress towards human-level artificial intelligence. However, we believe that progress towards human-level artificial intelligence and the applications it enables requires a deeper and more comprehensive understanding that cannot be achieved by studying individual areas in isolation.

The Seventeenth American Association of Artificial Intelligence (AAAI) and the Twelfth Innovative Applications of Artificial Intelligence (IAAI) conferences were jointly held in Austin, Texas, on 30 July through 3 August. They continue to provide a growing wealth of research stretching into many of the areas of AI. Coupled with demonstrations of the emerging and deployed IAAI techniques, the joint conference results in a complete survey of breaking technology. With the decade's events in highperformance desktops and "invisible" computing, AI has flourished and now expands from its classical cognitive architecture roots into intense gaming and wireless devices. AI successfully reaches these areas with its continuing multidisciplinary collaborations and creativity.

In this article we review the 2011 International Planning Competition. We give an overview of the history of the competition, discussing how it has developed since its first edition in 1998. The 2011 competition was run in three main separate tracks: the deterministic (classical) track; the learning track; and the uncertainty track. Each track proposed its own distinct set of new challenges and the participants rose to these admirably, the results of each track showing promising progress in each area. The competition attracted a record number of participants this year, showing its continued and strong position as a major central pillar of the international planning research community.

Complex, real-world domains require rethinking traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment implies a continual approach in which planning and execution are interleaved, uncertainty in the current and projected world state is recognized and handled appropriately, and replanning can be performed when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, which we term distributed, continual planning (DCP). We argue that developing DCP systems will be necessary for planning applications to be successful in these environments.

They are held on a Saturday and Sunday, with a tutorial or two on the preceding Friday. This year the workshop was preceded by two days of tutorials: an introductory tutorial on Thursday and a more advanced tutorial on Friday. The tutorials were held at the University of Michigan's Advanced Technology Lab and at the workshop site. There were 37 talks this year as well as a discussion session with 57 attendees. Seven sites made one presentation, sometimes involving multiple researchers.

That intelligence is a form of information processing and that the framework of modern digital computers provides pretty much all that is needed for representing and processing information for doing AI are two of the most foundational of such assumptions. Turing (1950) explicitly articulated this idea in the late 1940s, and later Newell and Simon (1976) proposed the physical symbol system hypothesis (PSSH) as a newer form of the same set of intuitions about the relation between computation and thinking. In this tradition, the computational approach is not just one way of making intelligent systems, but representing and processing information within the computational framework is necessary for intelligence as a process, wherever it is implemented. The language of thought (LOT) hypothesis, of which Fodor (1975) has given the most well-known exposition, is a variant of the computational hypothesis in AI. LOT holds that underlying thinking is a medium that has the properties of formal symbolic languages that we are familiar with in computer science.

The main contribution of the book Rules of Encounter: Designing Conventions for Automated Negotiation, by Jeffrey S. Rosenschein and Gilad Zlotkin, is the formulation of a principled framework within which to study interactions among artificial heterogeneous agents. This framework is based on the theory of games, which is aimed at decision problems faced by agents in situations in which the agent's welfare depends not only on its own actions but also on the actions of other agents. The examples are numerous: The personal digital assistants (PDAs) that might one day keep track of their users' itinerary will have to negotiate with PDAs of other people to adjust and synchronize their meeting schedules. Software agents looking for the right kinds of information on the Internet on behalf of their users might have to negotiate with other such agents over the access to resources. Computer agents that control a telecommunications network will have to interact with computers that control other networks and might find it beneficial to come to agreement with them.

Of course, the book's authors, psychologist Keith Holyoak and philosopher Paul Thagard, have good reason for this discussion: to focus on the "analogy war" that went on for years in the upper echelons of the U.S. government. Politicians think by analogy all the time, and the fates of nations hang on their idiosyncratic analogical instincts, wise or not. Military leaders, too, are guided by precedents, and Holyoak and Thagard ironically note that generals often prepare for the war that they last fought. However, they also point out that one can select one's precedents in a deeper manner than that. In fact, they devote three pages to George Ball, undersecretary of state in the Johnson administration, "who history must now credit as the greatest American political analogist of his time" (p. To be sure, Ball saw the appeal of the Korea, Munich, and dominochain analogies, but in each, he also saw serious weaknesses; more important, he felt he saw deeper similarities to the situation the ...