In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasible solution quickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA*, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. While improving its bound, ARA* reuses previous search efforts and, as a result, is significantly more efficient than other anytime search methods. In addition to our theoretical analysis, we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and a dynamic path planning problem for an outdoor rover.

A mobile robot acting in the world is faced with a large amount of sensory dataand uncertainty in its action outcomes. Indeed, almost all interesting sequentialdecision-making domains involve large state spaces and large, stochastic action sets. We investigate a way to act intelligently asquickly as possible in domains where finding a complete policy would take a hopelessly long time. This approach, Relational Envelopebased Planning(REBP) tackles large, noisy problems along two axes.

The design of cooperative multi-robot systems is a highly active research area in robotics. Two lines of research in particular have generated interest: thesolution of large, weakly coupled MDPs, and the design and implementation ofmarket architectures. We propose a new algorithm which joins together these two lines of research. For a class of coupled MDPs, our algorithm automatically designs a market architecture which causes a decentralized multi-robot system to converge to a consistent policy. We can show that this policy is the same as the one which would be produced by a particular centralized planning algorithm. We demonstrate the new algorithm on three simulation examples: multi-robot towing, multi-robot path planning with a limited fuel resource, and coordinating behaviors in a game of paint ball.

We explore approximate policy iteration, replacing the usual costfunction learningstep with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical planningdomains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.

In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasible solutionquickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA*, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. While improving its bound, ARA* reuses previous search efforts and, as a result, is significantly more efficient thanother anytime search methods. In addition to our theoretical analysis, we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and a dynamic path planning problem foran outdoor rover.

The Fourteenth International Conference on Automated Planning and Scheduling (ICAPS-04) was held in Canada in June of 2004. It covered the latest theoretical and empirical advances in planning and scheduling. The conference program consisted of tutorials, workshops, a doctoral consortium, and three days of technical paper presentations in a single plenary track, one day of which was jointly organized with the Ninth International Conference on Principles of Knowledge Representation and Reasoning. ICAPS-04 also hosted the International Planning Competition, including a classical track and a newly formed probabilistic track.

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search. We also abstract the approach in order to generalize it to other domains that need an integrated approach to planning and scheduling.

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search. We also abstract the approach in order to generalize it to other domains that need an integrated approach to planning and scheduling.

The Fourteenth International Conference on Automated Planning and Scheduling (ICAPS-04) was held in Canada in June of 2004. It covered the latest theoretical and empirical advances in planning and scheduling. The conference program consisted of tutorials, workshops, a doctoral consortium, and three days of technical paper presentations in a single plenary track, one day of which was jointly organized with the Ninth International Conference on Principles of Knowledge Representation and Reasoning. ICAPS-04 also hosted the International Planning Competition, including a classical track and a newly formed probabilistic track. This report describes the conference in more detail.

(Niles and Pease 2001). Considering that tremendous scheduling that are robust in the face of realworld progress has been made in commonsense reasoning concerns like time zones, daylight savings in specialized topics such as thermodynamics time, and international calendar variations. in physical systems (Collins and Forbus 1989), it is surprising that our best content theories Given the importance of an ontology of of people are still struggling to get past time across so many different commonsense simple notions of belief and intentionality (van der Hoek and Wooldridge 2003). However, search is the generation of competency theories systems that can successfully reason about that have a degree of depth necessary to solve people are likely to be substantially more valuable inferential problems that people are easily able than those that reason about thermodynamics to handle. in most future applications. Yet competency in content theories is only Content theories for reasoning about people half of the challenge. Commonsense reasoning are best characterized collectively as a theory of in AI theories will require that computers not commonsense psychology, in contrast to those only make deep humanlike inferences but also that are associated with commonsense (naïve) ensure that the scope of these inferences is as physics. The scope of commonsense physics, broad as humans can handle, as well. That is, best outlined in Patrick Hayes's first and second in addition to competency, content theories will "Naïve Physics Manifestos" (Hayes 1979, need adequate coverage over the full breadth of 1984), includes content theories of time, space, concepts that are manipulated in human-level physical entities, and their dynamics. It is only by achieving psychology, in contrast, concerns all some adequate level of coverage that we of the aspects of the way that people think they can begin to construct reasoning systems that think. It should include notions of plans and integrate fully into real-world AI applications, goals, opportunities and threats, decisions and where pragmatic considerations and expressive preferences, emotions and memories, along user interfaces raise the bar significantly.