This paper investigates how centralised, cooperative, multiagent planning problems with concurrent action constraints and heterogeneous agents can be encoded with some minor additions to PDDL, and how such encoded domains can be solved via a translation to temporal planning. Concurrency constraints are encoded on affordances (object-action tuples) and determine the conditions under which a part...

This paper introduces two new frameworks for learning action models for planning. In the mistake-bounded planning framework, the learner has access to a planner for the given model representation, a simulator, and a planning problem generator, and aims to learn a model with at most a polynomial number of faulty plans. In the planned exploration framework, the learner does not have access to a p...

In this paper, we report on our past and continuing cxperiences in applying an AI generative planning system to the problem of generating crisis action operations plans in a military domain. ~ We describe the application, the System for Operational Crisis Action Planning (SOCAP). and the lessons we learned creating it. We also report on the applied research we performed to address the lessons l...

MENTAL HEALTH PLANNING FOR SOCIAL ACTION. By George S. Stevenson. New York, The Blakiston Division, McGraw-Hill Book Company, Inc., 1956. 358 pp. $6.50. Few, if any, have done as much to advance the cause of mental health as the author of this book for which so many have waited with keen anticipation. Dr. Stevenson, with rare insight and judgment, has presented the challenge that "there is more...

We introduce a non-admissible heuristic for planning with action costs, called the set-additive heuristic, that combines the benefits of the additive heuristic used in the HSP planner and the relaxed plan heuristic used in FF. The set-additive heuristic ha is defined mathematically and handles non-uniform action costs like the additive heuristic ha, and yet like FF’s heuristic hFF, it encodes t...

ion-based Action Ordering in Planning Maria Fox and Derek Long and Julie Porteous Department of Computer and Information Sciences University of Strathclyde, Glasgow, UK

The action language BC provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how BC can be used for robot task planning to solve...

We apply decision theoretic techniques to construct nonplayer characters that are able to assist a human player in collaborative games. The method is based on solving Markov decision processes, which can be difficult when the game state is described by many variables. To scale to more complex games, the method allows decomposition of a game task into subtasks, each of which can be modelled by a...

In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because it requires formally representing the complex relationships among different objects in the environment. The problem is compounded in multi-agent environments where agents can take more types of actions. In this paper, we...

We consider the problem of learning action models for planning in two frameworks and present general sufficient conditions for efficient learning. In the mistake-bounded planning framework, the learner has access to a sound and complete planner for the given action model language, a simulator, and a planning problem generator. In the planned exploration framework, the learner has access to a pl...