(IKS 2002).

This article is an edited transcript of a lecture given at IJCAI-99, Stockholm, Sweden, on 4 August 1999. The article summarizes concepts, principles, and tools that were found useful in applications involving causal modeling. The principles are based on structural-model semantics in which functional (or counterfactual) relationships representing autonomous physical processes are the fundamental building blocks. The article presents the conceptual basis of this semantics, illustrates its application in simple problems, and discusses its ramifications to computational and cognitive problems concerning causation.

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with modelbased reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems. Past progress, current trends, and issues for future research are discussed.

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. The four teams were Foster- Miller and iRobot (both robot manufacturers from the Boston area), the United States Navy's Space Warfare Center (SPAWAR) group from San Diego, and the University of South Florida (USF). 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. Based on our experiences and history, we were asked to comment on the validity of the competition. The CRASAR collective experience suggests that most of the basic rules of the competition matched reality because the rules accurately reflected deployment scenarios, but the National Institute of Standards and Technology (NIST) Standard Test Course, and hardware or software approaches forwarded by competitors in last summer's event, missed the mark. This article briefly reviews the types of robots and missions used by CRASAR at the WTC site, then discusses the robotassisted search and rescue effort in terms of lessons for the competition.

The search and rescue efforts involving structural joint rules committee from RoboCup and collapse and other urban environments (Fire AAAI brought two communities together to 1993). The main task of USAR is to recover live develop the rules and scoring method. Robots involved with USAR must were four registered teams in the competition: identify victims and send back the locations to (1) Sharif University, (2) Swarthmore College, trained medical rescue personnel for removal (3) Utah State University, and (4) the University of the victims from the collapsed area. Additionally, several teams Robot Rescue League rules, designed by the exhibited their robots in the rescue arena, rules committee, keep the USAR task in focus including the University of South Florida and by addressing several issues that arise in real the University of Minnesota. This article discusses USAR situations, such as the time to transport the 2001 Robot Rescue event: the and set up the robot; the number of personnel course, the rules, the research approaches of required to run the robot; and, most importantly, the participants, and the final scores.

Running times are the median of five runs on an UltraSparc II (296MHz CPU, 256Mb RAM).

Antarctica contains the most fertile meteorite hunting grounds on Earth. The pristine, dry and cold environment ensures that meteorites deposited there are preserved for long periods. Subsequent glacial flow of the ice sheets where they land concentrates them in particular areas. To date, most meteorites recovered throughout history have been done so in Antarctica in the last 20 years. Furthermore, they are less likely to be contaminated by terrestrial compounds.

Running times are the median of five runs on an UltraSparc II (296MHz CPU, 256Mb RAM).

Antarctica contains the most fertile meteorite hunting grounds on Earth. The pristine, dry and cold environment ensures that meteorites deposited there are preserved for long periods. Subsequent glacial flow of the ice sheets where they land concentrates them in particular areas. To date, most meteorites recovered throughout history have been done so in Antarctica in the last 20 years. Furthermore, they are less likely to be contaminated by terrestrial compounds.

This allows for the representation and exact solution of a wide range of problems in which transitions or rewards vary over time. We examine problems based on route planning with public transportation andtelescope observation scheduling. 1 Introduction Imagine trying to plan a route from home to work that minimizes expected time. One approach is to use a tool such as "Mapquest", which annotates maps with information about estimated driving time, then applies a standard graph-search algorithm to produce a shortest route. Even if driving times are stochastic, the annotations canbe expected times, so this presents no additional challenge. However, consider what happens if we would like to include public transportation in our route planning. Buses, trains, and subways vary in their expected travel time according to the time of day: buses and subways come more frequently during rush hour; trains leave on or close to scheduled departure times. In fact, even highway driving times vary with time of day, with heavier traffic and longer travel times during rush hour.