Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multivalued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward’s approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving

We introduce an efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finite-domain state variables instead of the straight-forward propositional encoding. Translation is performed in four stages. Firstly, we transform the input task into an equivalent normal form expressed in a restricted fragment of PDDL. Secondly, we synthesize invariants of the planning task that identify groups of mutually exclusive propositions which can be represented by a single finite-domain variable. Thirdly, we perform an efficient relaxed reachability analysis using logic programming techniques to obtain a grounded representation of the input. Finally, we combine the results of the third and fourth stage to generate the final grounded finite-domain representation. The presented approach has originally been implemented as part of the Fast Downward planning system for the 4th International Planning Competition (IPC4). Since then, it has been used in a number of other contexts with considerable success, and the use of concise finite-domain representations has become a common feature of state-of-the-art planners.

The availability of informed (but inadmissible) planning heuristics has enabled the development of highly scalable planning systems. Due to this success, a body of work has grown around modifying these heuristics to handle extensions to classical planning. Most recently, there has been an interest in addressing partial satisfaction planning problems, but existing heuristics fail to address the complex interactions that occur in these problems between action and goal selection. In this paper we provide a unique heuristic based on linear programming that we use to solve a relaxed version of the partial satisfaction planning problem. We incorporate this heuristic in conjunction with a lookahead strategy in a branch and bound algorithm to solve a class of over-subscribed planning problems.

The PDDL3 specifications include soft goals and trajectory constraints for distinguishing highquality plans among the many feasible plans in a solution space. To reduce the complexity of solving a large PDDL3 planning problem, constraint partitioning can be used to decompose its constraints into subproblems of much lower complexity. However, constraint locality due to soft goals and trajectory constraints cannot be effectively exploited by existing subgoal-partitioning techniques developed for solving PDDL2.2 problems. In this paper, we present an improved partition-andresolve strategy for supporting the new features in PDDL3. We evaluate techniques for resolving violated global constraints, optimizing goal preferences, and achieving subgoals in a multi-valued representation. Empirical results on the 5th International Planning Competition (IPC5) benchmarks show that our approach is effective and significantly outperforms other competing planners. 1

Abstract. One of the most successful approaches in automated planning is to use heuristic state-space search. A popular heuristic that is used by a number of state-space planners is based on relaxing the planning task by ignoring the delete effects of the actions. In several planning domains, however, this relaxation produces rather weak estimates to guide search effectively. We present a relaxation using (integer) linear programming that respects delete effects but ignores action ordering, which in a number of problems provides better distance estimates. Moreover, our approach can be used as an admissible heuristic for optimal planning.

The use of mutual exclusion (mutex) has led to significant advances in propositional planning. However, previous mutex can only detect pairs of actions or facts that cannot be arranged at the same time step. In this paper, we introduce a new class of constraints that significantly generalizes mutex and can be efficiently computed. The proposed long-distance mutual exclusion (londex) can capture constraints over actions and facts not only at the same time step but also across multiple steps. Londex provides a powerful and general approach for improving planning efficiency. As an application, we have integrated londex into SATPLAN04, a leading optimal planner. Experimental results show that londex can effectively prune the search space and reduce the planning time. The resulting

In this paper, we describe our enhancements incorporated in SGPlan (hereafter called SGPLan5) for supporting the new features of the PDDL3.0 language used in the Fifth International Planning Competition (IPC5). Based on the architecture of SGPlan that competed in the Fourth IPC (hereafter called SGPLan4), SGPLan5 partitions a large planning problem into subproblems, each with its own subgoal, and resolves those inconsistent solutions using our extended saddle-point condition. Subgoal partitioning is effective for solving large planning problems because each partitioned subproblem involves a substantially smaller search space than that of the original problem. In SGPLan5, we generalize subgoal partitioning so that the goal state of a subproblem

Work in partial satisfaction planning (PSP) has hither to assumed that goals are independent. This implies that that individual goals have additive utility values. In many real-world problems we cannot make this assumption and thus goal utility is not additive. In this paper, we motivate the need for representing and handling goal utility dependencies in PSP and we provide a framework for representing them using the General Additive Independence (GAI) model (Bacchus & Grove 1995). We then present an algorithm based on forward heuristic planning to solve this problem using heuristics derived from the planning graph. To show the effectiveness of our framework, we provide empirical results on benchmark planning domains.

In this paper, we present the partition-and-resolve strategy in SGPlan (hereafter called SGPLan5) for fully supporting all language features in PDDL3.0. Based on the architecture of SGPlan that supported PDDL2.2 (hereafter called SGPLan4), SGPLan5 partitions a large planning problem into subproblems, each with its own subgoal, and resolves those inconsistent solutions using our extended saddle-point condition. Subgoal partitioning is effective for solving large planning problems because each partitioned subproblem involves a substantially smaller search space than that of the original problem. In SGPLan5, we generalize subgoal partitioning so that the goal state of a subproblem is no longer one goal fact as in SGPLan4, but can be any fact with loosely coupled constraints with other subproblems. We have further developed methods for representing a planning problem in a multi-valued form and for carrying out partitioning in the transformed space. The multi-valued representation leads to more efficient heuristics for resolving trajectory and temporal constraints and goal preferences.

Abstract. Planning as satisfiability is a powerful approach to solving domain independent planning problems. In this paper, we consider a relaxed semantics for plans with parallel operator application based on ∃-step semantics. Operators can be applied in parallel if there is at least one ordering in which they can be sequentially executed. Under certain conditions, we allow them to be executed simultaneously in a state s even if not all of them are applicable in s. In this case, we guarantee that they are enabled by other operators that are applied at the same time point. We formalize the semantics of parallel plans in this setting, and propose an effective translation for STRIPS problems into the propositional logic. We finally show that this relaxed semantics yields an approach to classical planning that is sometimes much more efficient than the existing SAT-based planners. 1