This editorial introduces the articles published in the AI Magazine special issue on Innovative Applications of Artificial Intelligence (IAAI), based on a selection of papers that appeared in the IAAI-05 conference, which occurred July 9-13 2005 in Pittsburgh, Pennsylvania. IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI application technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The six articles selected for this special issue are extended versions of papers that appeared at the conference. Three of the articles describe deployed applications that are already in use in the field. Three articles from the emerging technology track were particularly innovative and demonstrated some unique technology features ripe for deployment.

The Electrical Systems Division at the NASA Kennedy Space Center has developed and deployed an agent-based tool to monitor the space shuttle's ground processing telemetry stream. The application, the NASA Engineering Shuttle Telemetry Agent (NESTA), increases situational awareness for system and hardware engineers during ground processing of the shuttle's subsystems. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation. Sandia National Labs' Java Expert System Shell is employed as the rule engine. The shell's predicate logic lends itself well to capturing the heuristics and specifying the engineering rules of this spaceport domain. The declarative paradigm of the rule- based agent yields a highly modular and scalable design spanning multiple subsystems of the shuttle. Several hundred monitoring rules have been written thus far with corresponding notifications sent to shuttle engineers. This article discusses the rule-based telemetry agent used for space shuttle ground processing and explains the problem domain, development of the agent software, benefits of AI technology, and deployment and sustaining engineering of the product.

Two overarching goals were promoted for the 2005 Mobile Robot Competition. The first was to give the competitions an exhibitionstyle format to make them as accessible to different areas of research as possible. This was change would place the competitions and exhibitions demonstrated at the Fourteenth Annual AAAI directly in line with the conference, Mobile Robot Competition and Exhibition, an teams would need to handle the challenges involved event hosted at the Twentieth National Conference with noisy, cluttered, and unstructured on Artificial Intelligence (AAAI 2005). The robot event had a particularly strong human environments. Scavenger Hunt: Autonomous robots were required to search a cluttered and crowded environment This year, AAAI changed the venue format for a defined list of objects and were from a convention center to a hotel setting. The Scavenger as defined by the team, and feedback Hunt event was organized by Douglas from the participants. Blank from Bryn Mawr College, the Robot Robot Challenge: Robots were required to attend Challenge and the Open Interaction Task were the conference autonomously, including organized by Ashley Stroupe from the Jet registering for the conference, navigating the Propulsion Laboratory, the research component conference hall, talking with attendees, and of the exhibition was organized by Magdalena answering questions.

The most basic assumption used in statistical learning theory is that training data and test data are drawn from the same underlying distribution. Unfortunately, in many applications, the "in-domain" test data is drawn from a distribution that is related, but not identical, to the "out-of-domain" distribution of the training data. We consider the common case in which labeled out-of-domain data is plentiful, but labeled in-domain data is scarce. We introduce a statistical formulation of this problem in terms of a simple mixture model and present an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts. We present efficient inference algorithms for this special case based on the technique of conditional expectation maximization. Our experimental results show that our approach leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.

A recurring requirement for human-level artificial intelligence is the incorporation of vast amounts of knowledge into a software agent that can use the knowledge in an efficient and organized fashion. This article discusses representations and processes for agents and behavior models that integrate large, diverse knowledge stores, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between approaches is often difficult. We introduce a new approach to comparing frameworks based on the notions of commitment, reconsideration, and a categorization of representations and processes. We review four agent frameworks, concentrating on the major representations and processes each directly supports. By organizing the approaches according to a common nomenclature, the analysis highlights points of similarity and difference and suggests directions for integrating and unifying disparate approaches and for incorporating research results from one framework into alternatives.

ACT grew out of and semantic nets. It differs from other cognitive research on human memory. Over the years, architectures in that it also includes signals, ACT has evolved into ACT* and more recently, images, and maps in its knowledge database, ACT-R. ACT-R is being used in several research and maintains a tight real-time coupling projects in an Advanced Decision Architectures between iconic and symbolic data structures in Collaborative Technology Alliance for the U.S. its world model. The 4D/RCS architecture is also Army (Gonzalez 2003). ACT-R is also being different in its (1) focus on task decomposition used by thousands of schools across the country as the fundamental organizing principle; as an algebra tutor--an instructional system (2) level of specificity in the assignment of duties that supports learning by doing. Another wellknown and responsibilities to agents and units in and widely used architecture is Soar the behavior-generating hierarchy; and (3) emphasis (Laird, Newell, and Rosenbloom 1987). Soar on controlling real machines in realworld grew out of research on human problem solving environments.

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This article describes the virtual humans developed as part of the Mission Rehearsal Exercise project, a virtual reality-based training system. This project is an ambitious exercise in integration, both in the sense of integrating technology with entertainment industry content, but also in that we have joined a number of component technologies that have not been integrated before. This integration has not only raised new research issues, but it has also suggested some new approaches to difficult problems. We describe the key capabilities of the virtual humans, including task representation and reasoning, natural language dialogue, and emotion reasoning, and show how these capabilities are integrated to provide more human-level intelligence than would otherwise be possible.

We are developing Companion Cognitive Systems, a new kind of software that can be effectively treated as a collaborator. Aside from their potential utility, we believe this effort is important because it focuses on three key problems that must be solved to achieve human-level AI: Robust reasoning and learning, interactivity, and longevity. We describe the ideas we are using to develop the first architecture for Companions: analogical processing, grounded in cognitive science for reasoning and learning, sketching and concept maps to improve interactivity, and a distributed agent architecture hosted on a cluster to achieve performance and longevity. We outline some results on learning by accumulating examples derived from our first experimental version.

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