The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures. More than 40,000 learners worldwide have used TLCTS courses. TLCTS utilizes artificial intelligence technologies during the authoring process, and at run time to process learner speech, engage in dialog, and evaluate and assess learner performance. This paper describes the architecture of TLCTS and the artificial intelligence technologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learning foreign language and culture using this approach.

Public school choice at the primary and secondary levels is a keyelement of the U.S. No Child Left Behind Act of 2001 (NCLB). If aschool does not meet assessment goals for two consecutive years, bylaw the district must offer students the opportunity to transfer to aschool that is meeting its goals. Thus we have developed an online,content-based recommender system, called SmartChoice. Itprovides parents with school recommendations for individual studentsbased on parents' preferences and students' needs, interests,abilities, and talents.

The goal of the Innovative Applications of Artificial Intelligence (IAAI) conference is to highlight new, innovative, systems and application areas of AI technology and to point out the often-overlooked difficulties involved in deploying complex technology to end users. Those of us who have ventured out of the realm of pure research and tried to build applications to be used by our fellow humans realize that it takes a lot more than just brilliant algorithms to make an application survive in the real world. Each application that succeeds is worth celebrating and the teams behind them are due wholehearted congratulations. It is in this spirit that we bring you this special issue covering select applications from the IAAI conference held last year in Chicago.

The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures.  More than 40,000 learners worldwide have used TLCTS courses.  TLCTS utilizes artificial intelligence technologies during the authoring process, and at run time to process learner speech, engage in dialog, and evaluate and assess learner performance. This paper describes the architecture of TLCTS and the artificial intelligence technologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learning foreign language and culture using this approach.

The 2008 Robert S. Engelmore Memorial Lecture Award will be presented to Kenneth

These underpinnings in genetics and fields are vast, variegated, informed by memetics, studying phenomena such disparate theoretical and technical disciplines, as coalition formation in an artificial and interrelated. Other applications provided an updated perspective ethical concerns related to the use of included case-based retrieval of to a previous symposium held in fall eldercare technology to ensure that narratives culturally relevant to a 2005 on the same topic. Some models focused One major theme of the symposium The symposium ended with a more directly on adaptation, from machine-learning was to investigate the use of sensor brainstorming session on possible solutions and game-theoretic networks in the home environment to for two real-life scenarios for perspectives, but discussions suggested provide safety, to monitor activities of ailing elders and their caregivers. The ways in which those adaptations daily living, to assess physical and cognitive exercise was helpful in grounding the might vary from one cultural context function, and to identify participants in the lives of older adults to another. Work was also should address real needs.

CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions—including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic—can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of our dialogue system, as well as a survey of related works, we will illustrate the system structure, and describe its pedagogical functions with the underlying AI techniques in detail such as NLP and rule-based reasoning. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process. Finally, we will conclude with potential improvements.

Public school choice at the primary and secondary levels is a keyelement of the U.S. No Child Left Behind Act of 2001 (NCLB).  If aschool does not meet assessment goals for two consecutive years, bylaw the district must offer students the opportunity to transfer to aschool that is meeting its goals.  Making a choice with such potentialimpact on a child's future is clearly monumental, yet astonishinglyfew parents take advantage of the opportunity.  Our research has shownthat a significant part of the problem arises from issues ininformation access and information overload, particularly for lowsocioeconomic status families.  Thus we have developed an online,content-based recommender system, called SmartChoice.  Itprovides parents with school recommendations for individual studentsbased on parents' preferences and students' needs, interests,abilities, and talents.  The first version of the online applicationwas deployed and live for focus group participants who used it for theJanuary and March/April 2008 Charlotte-Mecklenburg school choiceperiods.  This article describes the SmartChoice Program and theresults of our initial and followup studies with participants.

This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning. The learned bases provide a set of anchor points to form a local coordinate system, such that each data point $x$ on the manifold can be locally approximated by a linear combination of its nearby anchor points, with the linear weights offering a local-coordinate coding of $x$. We show that a high dimensional nonlinear function can be approximated by a global linear function with respect to this coding scheme, and the approximation quality is ensured by the locality of such coding. The method turns a difficult nonlinear learning problem into a simple global linear learning problem, which overcomes some drawbacks of traditional local learning methods. The work also gives a theoretical justification to the empirical success of some biologically-inspired models using sparse coding of sensory data, since a local coding scheme must be sufficiently sparse. However, sparsity does not always satisfy locality conditions, and can thus possibly lead to suboptimal results. The properties and performances of the method are empirically verified on synthetic data, handwritten digit classification, and object recognition tasks.

CP-networks have been proposed as a simple and intuitive graphical tool for representing conditional ceteris paribus preference statements over the values of a set of variables. While the problem of reasoning with CP-networks has been receiving some attention, there are very few works that address the problem of learning CP-networks. In this work we investigate the task of learning CP-networks, given access to a set of pairwise comparisons. We first prove that the learning problem is intractable, even under several simplifying assumptions. We then present an algorithm that, under certain assumptions about the observed pairwise comparisons, identifies a CP-network that entails these comparisons. We finally show that the proposed algorithm is a PAC-learner, and, thus, that the CP-networks it induces accurately predict the user's preferences on previously unseen situations.