Tokenplan is a planner based on the use of Petri nets. Its main feature is the flexibility it offers in the way it builds the planning graph. The next step is to demonstrate the benefits we expect from our planner in planning problems involving optimization and uncertainty handling.

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.

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.

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).

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.

System R is a variant of the original planning algorithm used in strips. It was the only planner that competed in both the automatic and hand-tailored tracks in the Fifth International Conference on Artificial Intelligence Planning and Scheduling competition.

AltAlt is designed to exploit the complementary strengths of two of the currently popular competing approaches for plan generation: (1) graphplan and (2) heuristic state search. It uses the planning graph to derive effective heuristics that are then used to guide heuristic state search. The heuristics derived from the planning graph do a better job of taking the subgoal interactions into account and, as such, are significantly more effective than existing heuristics. AltAlt was implemented on top of two state-of-the-art planning systems: (1) stan3.0, a graphplan-style planner, and (2) hsp-r, a heuristic search planner.

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.

TALplanner is a forward-chaining planner that utilizes domain-dependent knowledge to control search in the state space generated by action invocation. The domain-dependent control knowledge, background knowledge, plans, and goals are all represented using formulas in a temporal logic called tal, which has been developed independently as a formalism for specifying agent narratives and reasoning about them. In the Fifth International Artificial Intelligence Planning and Scheduling Conference planning competition, TALplanner exhibited impressive performance, winning the Outstanding Performance Award in the Domain-Dependent Planning Competition.