Finite-state controllers represent an effective action selection mechanisms widely used in domains such as video-games and mobile robotics. In contrast to the policies obtained from MDPs and POMDPs, finite-state controllers have two advantages: they are often extremely compact, and they are general, applying to many problems and not just one. A limitation of finite-state controllers, on the other hand, is that they are written by hand. In this paper, we address this limitation, presenting a method for deriving controllers automatically from models. The models represent a class of contingent problems where actions are deterministic and some fluents are observable. The problem of deriving a controller is converted into a conformant problem that is solved using classical planners, taking advantage of a complete translation into classical planning introduced recently. The controllers derived are ‘general’ in the sense that they do not solve the original problem only, but many variations as well, including changes in the size of the problem or in the uncertainty of the initial situation and action effects. Several experiments illustrating the automatic derivation of controllers are presented.

The Workshop program of the Twenty-First Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AIDriven Technologies for Service-Oriented Computing; Auction Mechanisms for Robot Coordination; Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics; Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness; Educational Data Mining; Evaluation Methods for Machine Learning; Event Extraction and Synthesis; Heuristic Search, Memory- Based Heuristics, and Their Applications; Human Implications of Human-Robot Interaction; Intelligent Techniques in Web Personalization; Learning for Search; Modeling and Retrieval of Context; Modeling Others from Observations; and Statistical and Empirical Approaches for Spoken Dialogue Systems.

The Workshop program of the Twenty-First Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AIDriven Technologies for Service-Oriented Computing; Auction Mechanisms for Robot Coordination; Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics; Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness; Educational Data Mining; Evaluation Methods for Machine Learning; Event Extraction and Synthesis; Heuristic Search, Memory- Based Heuristics, and Their Applications; Human Implications of Human-Robot Interaction; Intelligent Techniques in Web Personalization; Learning for Search; Modeling and Retrieval of Context; Modeling Others from Observations; and Statistical and Empirical Approaches for Spoken Dialogue Systems.

This general planner implements a scheduler that tries different variants concurrently with different (time) resource bounds. We also describe how hsp2.0 can be used as an optimal (and near-optimal) planning algorithm and compare its performance with two other optimal planners, stan and blackbox.

Other achieves a tradeoff between optimality and heuristic search planners in the AIPS2000 Contest speed. The transition function f maps states s file domain.pddl. The syntax for the instance into states s′ s - Del(a) Add(a) for and domain files is given by the PDDL standard. The action costs c(a) are all equal to 1. that includes The states s S are sets of atoms from can also choose a schedule of options as done A. in The set of actions A(s) applicable in s is found. In addition, the different options can are the operators op in O that are relevant be run concurrently as threads.

In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field.

In 1998, the international planning community was invited to take part in the first planning competition, hosted by the Artificial Intelligence Planning Systems Conference, to provide a new impetus for empirical evaluation and direct comparison of automatic domain-independent planning systems. This article describes the systems that competed in the event, examines the results, and considers some of the implications for the future of the field.