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.

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.

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.

NASA and the United States Geological Survey (USGS) are collaborating to produce a global map of the Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) remote sensor data from the period of 2004 through 2007. The map is comprised of thousands of scene locations and, for each location, there are tens of different images of varying quality to chose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map generation problem. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. The paper also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance.

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

The DARPA Urban Challenge was a competition to develop autonomous vehicles capable of safely, reliably and robustly driving in traffic. In this article we introduce Boss, the autonomous vehicle that won the challenge. Boss is complex artificially intelligent software system embodied in a 2007 Chevy Tahoe. To navigate safely, the vehicle builds a model of the world around it in real time. This model is used to generate safe routes and motion plans in both on roads and in unstructured zones. An essential part of Boss’ success stems from its ability to safely handle both abnormal situations and system glitches.

We describe a new method for visualizing topics, the distributions over terms that are automatically extracted from large text corpora using latent variable models. Our method finds significant $n$-grams related to a topic, which are then used to help understand and interpret the underlying distribution. Compared with the usual visualization, which simply lists the most probable topical terms, the multi-word expressions provide a better intuitive impression for what a topic is "about." Our approach is based on a language model of arbitrary length expressions, for which we develop a new methodology based on nested permutation tests to find significant phrases. We show that this method outperforms the more standard use of $\chi^2$ and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles.

Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a number of interesting challenges which are distinct from traditional optimization research. Planning problems demand solutions that can satisfy a number of competing objectives on multiple scales related to robustness, adaptiveness, risk, etc. The scenario method is a key approach for planning. Scenarios can be defined for long-term as well as short-term plans. This paper introduces computational scenario-based planning problems and proposes ways to accommodate strategic positioning within the tactical planning domain. We demonstrate the methodology in a resource planning problem that is solved with a multi-objective evolutionary algorithm. Our discussion and results highlight the fact that scenario-based planning is naturally framed within a multi-objective setting. However, the conflicting objectives occur on different system levels rather than within a single system alone. This paper also contends that planning problems are of vital interest in many human endeavors and that Evolutionary Computation may be well positioned for this problem domain.

Scenarios are pen-pictures of plausible futures, used for strategic planning. The aim of this investigation is to expand the horizon of scenario-based planning through computational models that are able to aid the analyst in the planning process. The investigation builds upon the advances of Information and Communication Technology (ICT) to create a novel, flexible and customizable computational capability-based planning methodology that is practical and theoretically sound. We will show how evolutionary computation, in particular evolutionary multi-objective optimization, can play a central role - both as an optimizer and as a source for innovation.

Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment algorithm for multi-target air-borne threats.