International Conference on Automated Planning and Scheduling 

About: International Conference on Automated Planning and Scheduling is an academic conference. The conference publishes majorly in the area(s): Heuristics & Heuristic. Over the lifetime, 1147 publications have been published by the conference receiving 28235 citations.

Anytime dynamic A*: an anytime, replanning algorithm

Abstract: We present a graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion. Our algorithm tunes the quality of its solution based on available search time, at every step reusing previous search efforts. When updated information regarding the underlying graph is received, the algorithm incrementally repairs its previous solution. The result is an approach that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems. We present theoretical analysis of the algorithm, experimental results on a simulated robot kinematic arm, and two current applications in dynamic path planning for outdoor mobile robots.

Landmarks, critical paths and abstractions: what's the difference anyway?

Abstract: Current heuristic estimators for classical domain-independent planning are usually based on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently, landmarks. Previously, these different ideas for deriving heuristic functions were largely unconnected. We prove that admissible heuristics based on these ideas are in fact very closely related. Exploiting this relationship, we introduce a new admissible heuristic called the landmark cut heuristic, which compares favourably with the state of the art in terms of heuristic accuracy and overall performance.

Labeled RTDP: improving the convergence of real-time dynamic programming

Abstract: RTDP is a recent heuristic-search DP algorithm for solving non-deterministic planning problems with full observability. In relation to other dynamic programming methods, RTDP has two benefits: first, it does not have to evaluate the entire state space in order to deliver an optimal policy, and second, it can often deliver good policies pretty fast. On the other hand, RTDP final convergence is slow. In this paper we introduce a labeling scheme into RTDP that speeds up its convergence while retaining its good anytime behavior. The idea is to label a state s as solved when the heuristic values, and thus, the greedy policy defined by them, have converged over s and the states that can be reached from s with the greedy policy. While due to the presence of cycles, these labels cannot be computed in a recursive, bottom-up fashion in general, we show nonetheless that they can be computed quite fast, and that the overhead is compensated by the recomputations avoided. In addition, when the labeling procedure cannot label a state as solved, it improves the heuristic value of a relevant state. This results in the number of Labeled RTDP trials needed for convergence, unlike the number of RTDP trials, to be bounded. From a practical point of view, Labeled RTDP (LRTDP) converges orders of magnitude faster than RTDP, and faster also than another recent heuristic-search DP algorithm, LAO*. Moreover, LRTDP often converges faster than value iteration, even with the heuristic h = 0, thus suggesting that LRTDP has a quite general scope.

Automated composition of web services by planning in asynchronous domains

Abstract: We propose a novel planning framework for the automated composition of web services We consider services that are specified and implemented in industrial standard languages for business processes modeling and execution, like BPEL4WS These languages describe web services whose behavior is intrinsically asynchronous For this reason, the key aspect of our framework is the modeling of asynchronous planning problems In the paper we describe the framework and propose a planning approach that is based on state of the art techniques for planning under uncertainty Our experiments show that this approach can scale up to significant cases, ie, to cases in which the manual development of BPEL4WS composed services is not trivial and is time consuming

FF-Replan: a baseline for probabilistic planning

Abstract: FF-Replan was the winner of the 2004 International Probabilistic Planning Competition (IPPC-04) (Younes & Littman 2004a) and was also the top performer on IPPC-06 domains, though it was not an official entry. This success was quite surprising, due to the simplicity of the approach. In particular, FF-Replan calls FF on a carefully constructed deterministic variant of the planning problem and selects actions according to the plan until observing an unexpected effect, upon which it replans. Despite the obvious shortcomings of the approach and its strawman nature, it is the state-of-the-art in probabilistic planning as measured on recent competition benchmarks. This paper gives the first technical description of FF-Replan and provides an analysis of its results on all of the recent IPPC-04 and IPPC-06 domains. We hope that this will inspire extensions and insight into the approach and planning domains themselves that will soon lead to the dethroning of FF-Replan.