The planning competition has become a regular part of the biennial Artificial Intelligence Planning and Scheduling (AIPS) conferences. The 2000 competition featured a much larger group of participants and a wide variety of different approaches to planning. Besides the dramatic increase in participation, the 2000 competition demonstrated that planning technology has taken a giant leap forward in performance since 1998. The 2000 competition featured planning systems that were orders of magnitude faster than the planners of just two years prior.

The long-term future of space exploration at the National Aeronautics and Space Administration (NASA) is dependent on the full exploitation of autonomous and adaptive systems, but mission managers are worried about the reliability of these more intelligent systems. The main focus of the workshop was to address these worries; hence, we invited NASA engineers working on autonomous and adaptive systems and researchers interested in the verification and validation of software systems. The dual purpose of the meeting was to (1) make NASA engineers aware of the verification and validation techniques they could be using and (2) make the verification and validation community aware of the complexity of the systems NASA is developing. The workshop was held 5 to 7 December 2000 at the Asilomar Conference Center in Pacific Grove, California.

In 1950, Claude Shannon published his seminal work on how to program a computer to play chess. In Shannon's time, it would have seemed unlikely that only a scant 50 years would be needed to develop programs that play world-class backgammon, checkers, chess, Othello, and Scrabble. Computer games research is one of the important success stories of AI. This article reviews the past successes, current projects, and future research directions for AI using computer games as a research test bed.

Shop is a hierarchical task network planning algorithm that is provably sound and complete across a large class of planning domains. It plans for tasks in the same order that they will later be executed, and thus, it knows the current world state at each step of the planning process. For example, shop's preconditions can include logical inferences, complex numeric computations, and calls to external programs.

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.

We propose that AI for interactive computer games is an emerging application area in which this goal of human-level AI can successfully be pursued. Interactive computer games have increasingly complex and realistic worlds and increasingly complex and intelligent computer-controlled characters. In this article, we further motivate our proposal of using interactive computer games for AI research, review previous research on AI and games, and present the different game genres and the roles that human-level AI could play within these genres. Our conclusion is that interactive computer games provide a rich environment for incremental research on human-level AI.

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.

The SciFinance software synthesis system, licensed to major investment banks, automates programming for financial risk-management activities -- from algorithms research to production pricing to risk control. SciFinance's high-level, extensible specification language, aspen, lets quantitative analysts generate code from concise model descriptions written in application-specific and mathematical terminology; typically, a page or less produces thousands of lines of c. aspen's abstractions help analysts focus on their primary tasks -- model description, validation, and analysis -- rather than on programming details. Compared with manual programming, automation produces codes that are more sophisticated, accurate, and consistent. The shared knowledge base is used by the specification checker, synthesis system, and information portal.

This article describes the winning entries in the 2000 Association for the Advancement of Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication.

Like the well-known hsp system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. In more concrete terms, perceptions are f-granular, meaning that (1) the boundaries of perceived classes are unsharp and (2) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and function. The computational theory of perceptions (CTP), which is outlined in this article, adds to the armamentarium of AI a capability to compute and reason with perception-based information.