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.

Botball is a national program in which teams of middle and high school students design, build, and program small autonomous mobile robots to compete in a highly charged interactive (but nondestructive) tournament. Botball students learn to program in c, construct feedback and control loops, create electromechanical systems, and integrate it all together while they work on a team. Botball takes place in regional tournaments across the country and culminates in a National Botball Tournament traditionally hosted by the Association for the Advancement of Artificial Intelligence at its annual conference. This program puts reusable equipment into schools and, at the Botball Teacher Workshops, trains teachers in robotics and the integration of robotics into their curriculum.

Summer 2001 marked the tenth AAAI Mobile Robot Competition and Exhibition. A decade of contests and exhibitions have inspired innovation and research in AI robotics. We also reflect on how the contest has served as an arena for important debates in the AI and robotics communities. The article closes with a speculative look forward to the next decade of AAAI robot competitions.

The AAAI/RoboCup Robot Rescue event is designed to push researchers to design robotic systems for urban search and rescue. The rules were written to approximate a real rescue situation in a simulated environment constructed by the National Institute of Standards and Technology.

In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2001 Spring Symposium Series on Monday through Wednesday, 26 to 28 March 2001, at Stanford University. The titles of the seven symposia were (1) Answer Set Programming: Toward Efficient and Scalable Knowledge, Representation and Reasoning, (2) Artificial Intelligence and Interactive Entertainment, (3) Game-Theoretic and Decision-Theoretic Agents, (4) Learning Grounded Representations, (5) Model-Based Validation of Intelligence, (6) Robotics and Education, and (7) Robust Autonomy.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2001 Spring Symposium Series on Monday through Wednesday, 26 to 28 March 2001, at Stanford University. The titles of the seven symposia were (1) Answer Set Programming: Toward Efficient and Scalable Knowledge, Representation and Reasoning, (2) Artificial Intelligence and Interactive Entertainment, (3) Game-Theoretic and Decision-Theoretic Agents, (4) Learning Grounded Representations, (5) Model-Based Validation of Intelligence, (6) Robotics and Education, and (7) Robust Autonomy.

The Association for the Advancement of Artificial Intelligence presented the 2000 Fall Symposium Series was held on Friday through Sunday, 3 to 5 November, at the Sea Crest Oceanfront Conference Center. The titles of the five symposia were (1) Building Dialogue Systems for Tutorial Applications, (2) Learning How to Do Things, (3) Parallel Cognition for Embodied Agents, (4) Simulating Human Agents, and (5) Socially Intelligent Agents: The Human in the Loop.

BDDplan was created to perform certain reasoning processes in the fluent calculus, a flexible framework for reasoning about action and change based on first-order logic with equality (plus some second-order extensions in some cases). The reasoning is done by mapping the problems into propositional logic, which, in turn, can be implemented as operations on binary decision diagrams (BDDs).

LifeCode is a natural language processing (NLP) and expert system that extracts demographic and clinical information from free-text clinical records. The LifeCode NLP engine uses a large number of specialist readers whose particular output are combined at various levels to form an integrated picture of the patient's medical condition(s), course of treatment, and disposition. The LifeCode expert system performs the tasks of combining complementary information, deleting redundant information, assessing the level of medical risk and level of service represented in the clinical record, and producing an output that is appropriate for input to an electronic medical record (EMR) system or a hospital information system. The LifeCode NLP and expert systems reside in various delivery packages, including online transaction processing, a web browser interface, and an automated speech recognition (ASR) interface.