In many real-life domains, such as trading environments, selfinterested entities need to operate subject to limited time and information. Additionally, the web has mediated an ever broader range of transactions, urging participants to concurrently trade across multiple markets. All these have generated the need for technologies that empower prompt investigation of large volumes of data and rapid evaluation of numerous alternative strategies in the face of constantly changing market conditions (Bichler, Gupta, and Ketter 2010). AI and machine-learning techniques, including neural networks and genetic algorithms, are continuously gaining ground in the support of such trading scenarios. User modeling, price forecasting, market equilibrium prediction, and strategy optimization are typical cases where AI typically provides reliable solutions. Yet, the adoption and deployment of AI practices in real trading environments remains limited, since the proprietary nature of markets precludes open benchmarking, which is critical for further scientific progress.

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).

Then we add some world knowledge, in the form of commonsense statements, to help in the text understanding. The result combines this knowledge to form a multidimensional space where concepts, people, groups, and projects are all represented as vectors. From that space we retrieve information relevant to lab visitors--dynamically creating their presence in the vector space by creating a vector from the projects they have chosen as favorites. We then use the vector space to determine the relevance of objects in the space to each other--determining which projects are similar, which projects would be good fits for a lab visitor, and which projects fit which lab themes. Additionally, we have designed a user interface that makes this system easy and social to interact with. The following subections discuss our approach to interface design, our methods for extracting semantic information from the text base, and for assessing similarity of user interests with that knowledge.

We then describe the process of getting to deployment, covering Task learning provides tremendous value for technical challenges encountered, unit CPOF by enabling individual users and collective engagement activities, and an Army-led assessment command staffs to create customized, automated of the technology. Next, we discuss the fielding information-management schemes tailored to of the technology, including tradeoffs made to individual preferences and the staff's standard ensure deployability, the impact of the deployed operating procedures, without needing software technology, and lessons learned. We close with a engineers for extensive recoding. Task learning can summary of ongoing work to deploy additional reduce work load and stress, can enable managing functionality and to broaden the user base for task more tasks with better effectiveness, and can facilitate learning in CPOF.

NewsFinder automates the steps involved in finding, selecting, categorizing, and publishing news stories that meet relevance criteria for the Artificial Intelligence community. The software combines a broad search of online news sources with topic-specific trained models and heuristics. Since August 2010, the program has been used to operate the AI in the News service that is part of the AAAI AITopics website.

The AAAI-12 technical Christos Papadimtriou (University of abstracts, and poker competition program will kick off with the opening California, Berkeley) will deliver the posters.

This article is the first report in the best of AI in Japan series. This series will focus on the prominent accomplishments made in the AI field, not only the research and development but also the AI-related events in society. As the first in the forthcoming series, this opening article features a historical background and the contemporary AI-research activities in Japan. It then highlights some recent prominent results from the industry. Finally, a future perspective is given.

Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability. The third aims to build a hypoglycemia predictor that could alert patients to dangerously low blood glucose levels in time to take preventive action. All are products of the 4 Diabetes Support SystemTM project, which uses AI to promote the health and wellbeing of people with type 1 diabetes. These emerging applications could potentially benefit 20 million patients who are at risk for devastating complications, thereby improving quality of life and reducing health care cost expenditures.

From 2008-2010 we built an experimental personalized news system where readers subscribe to organized channels of topical information that are curated by experts. AI technology was employed to efficiently present the right information to each reader and to radically reduce the workload of curators. The system went through three implementation cycles and processed over 20 million news stories from about 12,000 RSS feeds on over 8000 topics organized by 160 curators for over 600 registered readers. This paper describes the approach, engineering and AI technology of the system.

Labor monitoring is crucial in modern health care, as it can be used to detect (and help avoid) significant problems with the fetus. In this article we focus on detecting hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to life-long disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are obtained using standard labor monitoring devices. The key idea is to learn models of the fetal response to signals from its environment. Then, we use the parameters of these models as attributes in a binary classification problem. A running count of pathological classifications over several time periods is taken to provide the current label for the fetus. We use a unique database of real clinical recordings, both from normal and pathological cases. Our approach classifies correctly more than half the pathological cases, 1.5 hours before delivery. These are cases that were missed by clinicians; early detection of this type would have allowed the physician to perform a Caesarean section, possibly avoiding the negative outcome.