LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A ∗ search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A ∗ search greatly improves results, and shows synergy effects with the use of landmarks. 1.

We define a probe to be a single action sequence computed greedily from a given state that either terminates in the goal or fails. We show that by designing these probes carefully using a number of existing and new polynomial techniques such as helpful actions, landmarks, commitments, and consistent subgoals, a single probe from the initial state solves by itself 683 out of 980 problems from previous IPCs, a number that compares well with the 627 problems solved by FF in EHC mode, with similar times and plan lengths. We also show that by launching one probe from each expanded state in a standard greedy best first search informed by the additive heuristic, the number of problems solved jumps to 900 (92%), as opposed to FF that solves 827 problems (84%), and LAMA that solves 879 (89%). The success of probes suggests that many domains can be solved easily once a suitable serialization of the landmarks is found, an observation that may open new connections between recent work in planning and more classical work concerning goal serialization and problem decomposition in planning and search.

BJOLP, The Big Joint Optimal Landmarks Planner uses landmarks to derive an admissible heuristic, which is then used to guide a search for a cost-optimal plan. In this paper we review landmarks and describe how they can be used to derive an admissible heuristic. We conclude with presenting the BJOLP planner.

contributor Fast Downward Stone Soup is a sequential portfolio planner that uses various heuristics and search algorithms that have been implemented in the Fast Downward planning system. We present a simple general method for concocting “planner soups”, sequential portfolios of planning algorithms, and describe the actual recipes used for Fast Downward Stone Soup in the sequential optimization and sequential satisficing tracks of IPC 2011. Before We Can Eat Since the original implementation of the Fast Downward planner (Helmert 2006; 2009) for the 4th International Planning Competition (IPC 2004), various researchers have used it as a starting point and testbed for a large number of additional search algorithms, heuristics, and other capabilities

Fast Downward Stone Soup is a sequential portfolio planner that uses various heuristics and search algorithms that have been implemented in the Fast Downward planning system. We present a simple general method for concocting “planner soups”, sequential portfolios of planning algorithms, and describe the actual recipes used for Fast Downward Stone Soup in the sequential optimization and sequential satisficing tracks of IPC 2011. This paper is, first and foremost, a planner description. Fast Downward Stone Soup was entered into the sequential (nonlearning) tracks of IPC 2011. Due to time constraints, we did not enter it into the learning competition at IPC 2011. However, we believe that the approach might still be of interest to the planning and learning community, as it represents a baseline against which other, more sophisticated portfolio learners can be usefully compared.

The LM-Cut planner uses the landmark-cut heuristic, introduced by the authors in 2009, within a standard A ∗ progression search framework to find optimal sequential plans for STRIPS-style planning tasks. This short paper recapitulates the main ideas surrounding the landmark-cut heuristic and provides pointers for further reading.

The SelMax planner combines two state-of-the-art admissible heuristics using an online learning approach. In this paper we describe the online learning approach employed by SelMax, briefly review the Fast Downward framework, and describe the SelMax planner.