The 2014 International Planning Competition: Progress and Trends

We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems

Towards a Protocol for Benchmark Selection in IPC

The planning competition has traditionally played an important role in motivating research and advances in Planning & Scheduling techniques. Despite its pivotal role in the planning community, some aspects of the competition have not been engineered yet. This is the case for the protocol for selecting benchmark instances. Benchmarks are of critical importance, since they can significantly affect competition results. In this paper we describe desirable properties of a selection protocol, discuss methods exploited in past SAT and planning competitions, and identify challenges that organisers of future competitions have to address in order to improve reliability and usefulness of the insights gained by looking at competitions’ results

Portfolio-based Planning: State of the Art, Common Practice and Open Challenges

In recent years the field of automated planning has significantly advanced and several powerful domain-independent planners have been developed. However, none of these systems clearly outperforms all the others in every known benchmark domain. This observation motivated the idea of configuring and exploiting a portfolio of planners to perform better than any individual planner: some recent planning systems based on this idea achieved significantly good results in experimental analysis and International Planning Competitions. Such results let us suppose that future challenges of the Automated Planning community will converge on designing different approaches for combining existing planning algorithms. This paper reviews existing techniques and provides an exhaustive guide to portfolio-based planning. In addition, the paper outlines open issues of existing approaches and highlights possible future evolution of these techniques

Planning through Automatic Portfolio Configuration: The PbP Approach

In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbP�s behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions

Accelerating Heuristic Search for AI Planning

AI Planning is an important research field. Heuristic search is the most commonly used method in solving planning problems. Despite recent advances in improving the quality of heuristics and devising better search strategies, the high computational cost of heuristic search remains a barrier that severely limits its application to real world problems. In this dissertation, we propose theories, algorithms and systems to accelerate heuristic search for AI planning. We make four major contributions in this dissertation. First, we propose a state-space reduction method called Stratified Planning to accelerate heuristic search. Stratified Planning can be combined with any heuristic search to prune redundant paths in state space, without sacrificing the optimality and completeness of search algorithms. Second, we propose a general theory for partial order reduction in planning. The proposed theory unifies previous reduction algorithms for planning, and ushers in new partial order reduction algorithms that can further accelerate heuristic search by pruning more nodes in state space than previously proposed algorithms. Third, we study the local structure of state space and propose using random walks to accelerate plateau exploration for heuristic search. We also implement two state-of-the-art planners that perform competitively in the Seventh International Planning Competition. Last, we utilize cloud computing to further accelerate search for planning. We propose a portfolio stochastic search algorithm that takes advantage of the cloud. We also implement a cloud-based planning system to which users can submit planning tasks and make full use of the computational resources provided by the cloud. We push the state of the art in AI planning by developing theories and algorithms that can accelerate heuristic search for planning. We implement state-of-the-art planning systems that have strong speed and quality performance