This document gathers the abstracts for the papers that were presented as part of the Previously Published Paper Track .

The separation of planner logic from domain knowledge supports the use of reformulation and configuration techniques, such as macro-actions and entanglements, which transform the model representation in order to improve a planner's performance. One drawback of such an approach is that it may require a potentially expensive training phase. In this paper, we introduce heuristic approaches for the online configuration of planning domain models. The proposed heuristics consider different aspects of PDDL-encoded operators for reordering such operators in the domain model, relying on the assumption that the way in which operators are encoded carries useful information about their expected use.

The game of Sokoban is an interesting platform for algorithm research. It is hard for humans and computers alike. Even small levels can take a lot of computation for all known algorithms. In this paper we will describe how a search based Sokoban solver can be structured and which algorithms can be used to realize each critical part. We implement a variety of those, construct a number of different solvers and combine them into an algorithm portfolio. The solver we construct this way can outperform existing solvers when run in parallel, that is, our solver with 16 processors outperforms the previous sequential solvers.

We propose an approach to solving Markov Decision Processes with non-Markovian rewards specified in Linear Temporal Logic interpreted over finite traces (LTL-f). Our approach integrates automata representations of LTL-f formulae into compiled MDPs that can be solved by off-the-shelf MDP planners, exploiting reward shaping to help guide search. Experiments with state-of-the-art UCT-based MDP planner PROST show automata-based reward shaping to be an effective method to guide search, producing solutions of superior quality, while maintaining policy optimality guarantees.

We adapt the relaxation heuristics h max , h add and h FF to interval based numeric relaxation frameworks, combining them with two different relaxation techniques and with two different search techniques. In contrast to previous approaches, the heuristics presented here are not limited to a subset of numeric planning and support action costs.

Admissible heuristics are essential for optimal planning in the context of search algorithms like A*, and they can also be used in the context of suboptimal planning in order to find quality-bounded solutions. In satisfacing planning, on the other hand, admissible heuristics are not exploited by the best-first search algorithms of existing planners even when a time window is available for improving the first solution found. For example, in the well-know planner LAMA, better solutions within such a time window are sought by restarting a Weighted-A* search guided by inadmissible heuristics, each time a better solution is found. In this paper, we investigate the use of admissible heuristics in the context of LAMA for pruning nodes that cannot lead to better solutions. The revised search of LAMA is experimentally evaluated using two alternative admissible heuristics for pruning and three types of problems: planning with soft goals, planning with action costs, and planning with both action costs and soft goals. Soft goals are compiled into hard goals following the approach of Keyder and Geffner. The empirical results show that the use of admissible heuristics in LAMA can be of great help to improve the planner performance.

Any-angle path finding on grids is an important problem with applications in autonomous robot navigation. In this paper, we show that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-connected grids, can be straightforwardly adapted to the 2 k neighborhoods, a family of neighborhoods that allow an increasing number of movements (and angles) as k is increased. This observation yields a pathfinder that computes 2 k -optimal paths very quickly. Compared to ANYA, an optimal true any-angle planner, over a variety of benchmarks, our planner is one order of magnitude faster while being less than 0.0005% suboptimal. Important to our planner's performance was the development of an iterative 2 k heuristic, linear in k, which is also a contribution of this paper.

Pattern databases are among the strongest known heuristics for many classical search benchmarks such as sliding-tile puzzles, the 4-peg Towers of Hanoi puzzles, Rubik's Cube, and TopSpin. Min-compression is a generally applicable technique for augmenting pattern database heuristics that has led to marked experimental improvements in some settings, while being ineffective in others. We provide a theoretical explanation for these experimental phenomena by studying the interaction between the ranking function used to order abstract states in a pattern database, the compression scheme used to abstract states, and the dependencies between state variables in the problem representation.

Star-Topology Decoupled Search has recently been introduced in classical planning. It splits the planning task into a set of components whose dependencies take a star structure, where one center component interacts with possibly many leaf components. Here we address a weakness of decoupled search, namely large leaf components, whose state space is enumerated explicitly. We propose a symbolic representation of the leaf state spaces via decision diagrams, which can be dramatically smaller, and also more runtime efficient. We further introduce a symbolic version of the LM-cut heuristic, that nicely connects to our new leaf representation. We show empirically that the symbolic representation indeed pays off when the leaf components are large.

Dynamic Potential Search (DPS) is a recently introduced search algorithm that returns a bounded-suboptimal cost solution. DPS orders nodes in the open-list based on their potential which is a combination of both the g - and h -values of a node. In this paper we study the behavior of DPS on weighted graphs. In particular, we develop a new variant of DPS, called DPSU which calculates the potential by counting one for each edge regardless of its costs. We develop an eager version and a restrained version of DPSU. We then compare all these algorithms on a number of weighted graphs and study the pros and cons of each of them.