This volume contains the papers accepted for presentation at ECP 2001, the Sixth European Conference on Planning, held in Toledo, Spain, on September 12-14, 2001. ECP continued the traditional high standards of AIPS and ECP as an archival forum for new research in the field of automated planning and scheduling. ECP conferences were first organized in 1991.

The papers in this proceedings present the latest advances in the field of automated planning and scheduling, ranging in scope from theoretical analyses of planning and scheduling problems and processes, to new algorithms for planning and scheduling under various constraints and assumptions, and the empirical evaluation of planning and scheduling techniques. They reflect recent research trends in subareas such as optimal planning, probabilistic and nondeterministic planning, path planning, multiagent planning, and new developments in heuristics and their analysis for planning algorithms.

Effective coordination of distributed human planners requires timely communication of relevant information to ensure the overall coherence of activities and the compatibility of assumptions. The CODA system provides targeted information dissemination among distributed human planners as a way of improving coordination. Within CODA, each planner declares interest in different types of plan changes that could impact his or her local plan development. As individuals develop plans using a plan authoring tool, their activities are monitored; changes that match declared interests trigger automatic notification of appropriate planners. In this way, distributed planners can receive focused, real-time updates of plan changes that are relevant to their local planning efforts.

In this paper, we present a tree-based algorithm for construction robots. Inspired by the TERMES project of Harvard University, robots in this domain are required to gather construction blocks from a reservoir and build user-specified structures much larger than themselves. While the robots are of roughly the same size as the blocks, they can scale greater heights by using temporarily constructed ramps in the substructures. In this paper, we consider the problem of minimizing the number of pickup and drop-off operations performed on blocks in order to build user-specified structures. Our polynomial-time algorithm heuristically solves this problem and is based on the idea of performing dynamic programming on a spanning tree in the inner loop and searching for a good tree to do so in the outer loop. Our algorithm performs very well in simulation and scales easily to large problem instances. For planning problems of this nature that are akin to construction domains, we believe that valuable lessons can be learned from comparing the success of our algorithm with the failure of off-the-shelf planning technologies.

Simple Temporal Networks (STNs) allow minimum and maximum distance constraints between time-points to be represented. They are often used when tackling planning and scheduling problems that involve temporal aspects. This paper is a summary of the journal article "Time-dependent Simple Temporal Networks: Properties and Algorithms" published in RAIRO - Operations Research. This journal article introduces an extension of STN called Time-dependent STN (TSTN), which covers temporal constraints for which the temporal distance required between two time-points is not necessarily constant. Such constraints are useful to model time-dependent scheduling problems, in which the duration of an activity may depend on its starting time. The paper introduces the TSTN framework, its properties, resolution techniques, as well as examples of applications.

We introduce a novel algorithm for temporal planning in Golog using shared resources, and describe the Bulk Freight Rail Scheduling Problem, a motivating example of such a temporal domain. We use the framework of column generation to tackle complex resource constrained temporal planning problems that are beyond the scope of current planning technology by combining: the global view of a linear programming relaxation of the problem; the strength of search infinding action sequences; and the domain knowledge that can be encoded in a Golog program. We show that our approach significantly outperforms state-of-the-art temporal planning and constraint programming approaches in this domain, in addition to existing temporal Golog implementations. We also apply our algorithm to a temporal variant of blocks-world where our decomposition speeds proof of optimality significantly compared to other anytime algorithms. We discuss the potential of the underlying algorithm being applicable to STRIPS planning, with further work.

Monte-Carlo tree search (MCTS) has been drawing great interest in recent years for planning under uncertainty. One of the key challenges is the trade-off between exploration and exploitation. To address this, we introduce a novel online planning algorithm for large POMDPs using Thompson sampling based MCTS that balances between cumulative and simple regrets. The proposed algorithm  Dirichlet-Dirichlet-NormalGamma based Partially Observable Monte-Carlo Planning (D 2 NG-POMCP) treats the accumulated reward of performing an action from a belief state in the MCTS search tree as a random variable following an unknown distribution with hidden parameters. Bayesian method is used to model and infer the posterior distribution of these parameters by choosing the conjugate prior in the form of a combination of two Dirichlet and one NormalGamma distributions. Thompson sampling is exploited to guide the action selection in the search tree. Experimental results confirmed that our algorithm outperforms the state-of-the-art approaches on several common benchmark problems.

We propose a new problem we refer to as goal recognitiondesign ( grd) , in which we take a domain theory and a set ofgoals and ask the following questions: to what extent do theactions performed by an agent within the model reveal its objective, and what is the best way to modify a model so thatany agent acting in the model reveals its objective as early aspossible. Our contribution is the introduction of a new measure we call worst case distinctiveness ( wcd ) with which weassess a  grd model. The  wcd represents the maximal lengthof a prefix of an optimal path an agent may take within a system before it becomes clear at which goal it is aiming. Tomodel and solve the  grd problem we choose to use the models and tools from the closely related field of automated planning. We present two methods for calculating the  wcd of a grd model, one of which is based on a novel compilation to aclassical planning problem. We then propose a way to reducethe  wcd of a model by limiting the set of available actions anagent can perform and provide a method for calculating theoptimal set of actions to be removed from the model. Our empirical evaluation shows the proposed solution to be effectivein computing and minimizing  wcd .

In (Otte and Correll 2013) we present C-FOREST, a parallelization framework for single-query sampling-based shortest-path planning algorithms. C-FOREST has been observed to have super linear speedup on many problems, e.g., paths of quality Ltarget are found 350X faster by 64 CPUs working in parallel than by 1 CPU. In (Otte and Correll 2013) C-FOREST is tested in conjunction with the RRT* algorithm. In the current work we perform additional experiments that show C-FOREST provides similar advantages when used conjunction with the SPRT algorithm. This reinforces our original claim that C-FOREST is generally applicable to a wide range of sampling based motion planning algorithms.

In order to overcome a large variety of run-time constraints, robots are being designed to be more resourceful by incorporating more sensory and motor options for any given task. The added flexibility provides a basis for dexterous problem solving, but challenges planners by increasing the complexity of search. Moreover, the cost of functionally equivalent options can vary dramatically. In the worst case, naive approaches to planning avoid expensive actions until inexpensive options are explored exhaustively leading to poor overall search performance. We present a dexterous robot that introduces multiple types of locomotor actions with significant differences in cost and situational value and apply standard search techniques to demonstrate the additional challenges that arise in the context of dexterous mobility. Results highlight incentives, opportunities, and impact for overcoming these challenges. Additionally, we present a prototype for a path planner that uses environmental features to define an efficient set of subgoals for dexterous motion planning.