Fall calendar of AI-related events.

In recent years, we have witnessed the success of autonomous agents applying machine-learning techniques across a wide range of applications. However, agents applying the same machine-learning techniques in online applications have not been so successful. Even agent-based hybrid recommender systems that combine information filtering techniques with collaborative filtering techniques have been applied with considerable success only to simple consumer goods such as movies, books, clothing, and food. Yet complex, adaptive autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgages have emerged. To a large extent, the reinforcement learning methods developed to aid agents in learning have been more successfully deployed in offline applications. The inherent limitations in these methods have rendered them somewhat ineffective in online applications. In this article, we postulate that a small amount of prior knowledge and human-provided input can dramatically speed up online learning. We demonstrate that our agent HumanE -- with its prior knowledge or "experiences" about the real estate domain -- can effectively assist users in identifying requirements, especially unstated ones, quickly and unobtrusively.

Six high-level requirements drive the implementation of intelligent synthetic adversaries for training: (1) competence, (2) taskability, (3) observational fidelity, (4) behavior variability, most difficult tasks soldiers perform. Frequent Competence: The adversaries must perform training is an essential element in reducing the tactics and missions humans perform in casualties. For this application, the adversaries' environments is costly and restricted to physical goal is to defend a small multistoried mockups of buildings and small towns. The agents must move Environments (VIRTE) program is developing immersive virtual trainers for military operations through the environment, identify tactically on urbanized terrain (MOUT). In this relevant features (such as escape routes), and trainer, four-person fire teams of U.S. Marines communicate and coordinate with other are situated in a virtual urban environment and agents. Virtual opponents new missions for different training scenarios, are required to populate the environment and and they must change their objectives challenge the trainees.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Our approach is designed to take the noisy results of traditional data-mining tools and exploit causal knowledge about attacks to relate activities and uncover the intent underlying them. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application. In particular, we outline the need to bring the analysts into the process and for heuristic improvements to the plan-recognition algorithm.

Additional initial ontologies include: search was used, queries were expanded to include (4) single part issues--relate to only one more words to search for, and thus, relevant vehicle component, such as a ding in a fender; documents could be found. Since the documents (5) multiple part issues--relate to two or more being searched were in a limited parts, especially misalignments, unsatisfactory domain, there were few problems with multiple gaps, malformations of joints between parts; senses of words introducing problems that (6) data analysis--results of analysis of measurement hurt precision. In our database, case entries are data generated by optical and mechanical similar--the textual fields do not contain long gages; and (7) plant locations--zones descriptions, and the content is limited to and stations organized topologically or functionally.

Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe Vmodel, a system we have created that uses visual representations and that enables middle-school students to create qualitative models. Software coaches use simple analyses of model structure plus qualitative simulation to provide feedback and explanations. This system has been used in several studies in Chicago public school classrooms, using curricula developed in collaboration with teachers. We discuss the design of the visual representation language, how Vmodel works, and evidence from school studies that indicate it is successful in helping students.

Preference Crystal City, Arlington Virginia, adjacent languages such as RDF and OWL. will be given to student scholars to Washington, DC.

This document summarizes the major events at the AAAI-05 and IAAI-05 conferences, held in Pittsburgh, Pennsylvania in July 2005.

In this article I discuss a deployed application at Ford Motor Company that utilizes AI technology for the analysis of potential ergonomic concerns at Ford's assembly plants. The manufacture of motor vehicles is a complex and dynamic problem, and the costs related to workplace injuries and lost productivity due to bad ergonomic design can be very significant. Ford has developed two separate ergonomic analysis systems that have been integrated into the process planning for manufacturing system at Ford known as the Global Study and Process Allocation System (GSPAS). GSPAS has become the global repository for standardized engineering processes and data for assembling all Ford vehicles, including parts, tools, and standard labor time. One of the more significant benefits of GSPAS is the use of a controlled language, known as Standard Language, which is used throughout Ford to write the process assembly instructions. AI is already used within GSPAS for Standard Language validation and direct labor management. The work described here shows how Ford built upon its previous success with AI to expand the technology into the new domain of ergonomics analysis.

"This summer, three local students will explore News" collection that can be found--complete Warburg school, will spend the Web pages. The only cost of the computers for five to six hours a day, the class is about $300 for a kit, which Gtech Gives Girls Blueprint for Success. May activities to break up the day." The space agency also provided 27, 2004 (www.westuexaminer.com). "The free kits for another 30 online students, best way to increase girls' interest in engineering Girls Build Robots at RoboCamp.