The innovative reapplication of a multiagent system for human-in-the-loop (HIL) simulation was a consequence of appropriate agent-oriented design. The use of intelligent agents for simulating human decision making offers the potential for analysis and design methodologies that do not distinguish between agent and human until implementation. With this as a driver in the design process, the construction of systems in which humans and agents can be interchanged is simplified. The experiences gained from this process indicate that it is simpler, both in design and implementation, to add humans to a system designed for intelligent agents than it is to add intelligent agents to a system designed for humans.

Because of military drawdowns and the need for additional transportation lift requirements, the United States Marine Corps developed a concept that enabled it to modify a commercial container ship to support deployed aviation units. However, a problem soon emerged in that there were too few people who were expert enough to do the unique type of planning required for this ship. Additionally, once someone did develop some expertise, it was time for him/her to move on, retire, or leave active duty. TALPS is now a fielded, certified application for Marine Corps aviation.

The Modified Antarctic Mapping Mission MAMM) was conducted from September to November 2000 onboard RADARSAT. The mission plan consisted of more than 2400 synthetic aperture radar data acquisitions of Antarctica that achieved the scientific objectives and obeyed RADARSAT's resource and operational constraints. Mission planning is a time- and knowledge-intensive effort. It required over a workyear to manually develop a comparable plan for AMM-1, the precursor mission to MAMM. This article describes the design and use of the automated mission planning system for MAMM, which dramatically reduced mission-planning costs to just a few workweeks and enabled rapid generation of what-if scenarios for evaluating alternative mission designs.

The Thirteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2001) was held on 7 to 9 August 2001 in Seattle, Washington, in conjunction with the Seventeenth International Joint Conference on Artificial Intelligence. As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed applications are systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are technologies and systems that are close to deployment and clearly show an innovative implementation of AI technologies. All these case studies are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the myriad of technical challenges provided by real-world problems.

This book looks at some of the results of this synergy among AI, cognitive science, and education. Examples include virtual students whose misconceptions force students to reflect on their own knowledge, intelligent tutoring systems, and speech recognition technology that helps students learn to read. Some of the systems described are already used in classrooms and have been evaluated; a few are still laboratory efforts. The book also addresses cultural and political issues involved in the deployment of new educational technologies. ISBN 0-0-262-56141-7 To order call 800-405-1619.

To a forum for Ph.D. students to discuss Many of you have been receiving the evaluate submitted proposals more effectively, research and career interests.

Because of military drawdowns and the need for additional transportation lift requirements, the United States Marine Corps developed a concept that enabled it to modify a commercial container ship to support deployed aviation units. However, a problem soon emerged in that there were too few people who were expert enough to do the unique type of planning required for this ship. Additionally, once someone did develop some expertise, it was time for him/her to move on, retire, or leave active duty. There needed to be a way to capture this knowledge. This condition was the impetus for the T-AVB AUTOMATED LOAD-PLANNING SYSTEM (TALPS) effort. TALPS is now a fielded, certified application for Marine Corps aviation.

The innovative reapplication of a multiagent system for human-in-the-loop (HIL) simulation was a consequence of appropriate agent-oriented design. The use of intelligent agents for simulating human decision making offers the potential for analysis and design methodologies that do not distinguish between agent and human until implementation. With this as a driver in the design process, the construction of systems in which humans and agents can be interchanged is simplified. Two systems have been constructed and deployed to provide defense analysts with the tools required to advise and assist the Australian Defense Force in the conduct of maritime surveillance and patrol. The experiences gained from this process indicate that it is simpler, both in design and implementation, to add humans to a system designed for intelligent agents than it is to add intelligent agents to a system designed for humans.

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

The RoboCup Rescue Physical Agent League Competition was held in the summer of 2001 in conjunction with the AAAI Mobile Robot Competition Urban Search and Rescue event, eerily preceding the September 11 World Trade Center (WTC) disaster. Four teams responded to the WTC disaster through the auspices of the Center for Robot-Assisted Search and Rescue (CRASAR), directed by John Blitch. Blitch, through his position as program manager for the Defense Advanced Research Projects Agency (DARPA) Tactical Mobile Robots Program, was a supporter of the competition; he also served as a member of the rules committee and a judge. USF participated by chairing the rules committee, judging, assisting with the logistics, providing commentary, and demonstrating tethered and wireless robots whenever entrants had to skip around during the competition.