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.

This paper is concerned with local search methods to solve job shop scheduling problems with uncertain durations modelled as fuzzy numbers. Based on a neighbourhood structure from the literature, a reduced set of moves and the consequent structure are defined. Theoretical results show that the proposed neighbourhood contains all the improving solutions from the original neighbourhood and provide a sufficient condition for optimality. Additionally, a makespan lower bound is proposed which can be used to discard neighbours. Experimental results illustrate the good performance of both proposals, which considerably reduce the computational load of the local search, as well as a synergy effect when they are simultaneously used.

A planning problem is k- dependent if each action has at most k pre-conditions on variables unaffected by the action. This concept is well-founded since k is a constant for all but a few of the standard planning domains, and is known to have implications for tractability. In this paper, we present several new complexity results for P ( k ), the class of k- dependent planning problems with binary variables and polytree causal graphs. The problem of plan generation for P ( k ) is equivalent to determining how many times each variable can change. Using this fact, we present a polytime plan generation algorithm for P (2) and P (3). For constant k > 3, we introduce and use the notion of a cover to find conditions under which plan generation for P ( k ) is polynomial.

Planning systems for real-world applications need the ability to handle concurrency and numeric fluents. Nevertheless, the predominant approach to cope with concurrency followed by the most successful participants in the latest International Planning Competitions (IPC) is still to find a sequential plan that is rescheduled in a post-processing step. We present Temporal Fast Downward (TFD), a planning system for temporal problems that is capable of finding low-makespan plans by performing a heuristic search in a temporal search space. We show how the context-enhanced additive heuristic can be successfully used for temporal planning and how it can be extended to numeric fluents. TFD often produces plans of high quality and, evaluated according to the rating scheme of the last IPC, outperforms all state-of-the-art temporal planning systems.

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

Systems subject to (continuous) physical effects and controlled by (discrete) digital equipments, are today very common. Thus, many realistic domains where planning is required are represented by hybrid systems , i.e., systems containing both discrete and continuous values, with possibly a nonlinear continuous dynamics. The PDDL+ language allows one to model these domains, however the current tools can generally handle only planning problems on (possibly hybrid) systems with linear dynamics. Therefore, universal planning applied to hybrid systems and, in general, to non-linear systems is completely out of scope for such tools. In this paper, we propose the use of explicit model checking-based techniques to solve universal planning problems on such hardly-approachable domains.

PDDL 2.1 supports modelling of complex temporal planning domains in which solutions must exploit concurrency. Few existing temporal planners can solve problems that require concurrency and those that do typically pay a performance price to deploy reasoning machinery that is not always required. In this paper we show how to improve the performance of forward-search planners that attempt to solve the full temporal planning problem, both by narrowing the use of the concurrency machinery to situations that demand it and also by improving the power of inference to prune redundant branches of the search space for common patterns of interaction in temporal domains that do require concurrency. Results illustrate the effectiveness of our ideas in improving the efficiency of a temporal planner that can solve problems with required concurrency, both in domains that exploit this ability and in those that do not.

The growing presence of household robots in inhabited environments arises the need for new robot task planning techniques. These techniques should take into consideration not only the actions that the robot can perform or unexpected external events, but also the actions performed by a human sharing the same environment, in order to improve the cohabitation of the two agents, e.g., by avoiding undesired situations for the human. In this paper, we present a human-aware planner able to address this problem. This planner supports alternative hypotheses of the human plan, temporal duration for the actions of both the robot and the human, constraints on the interaction between robot and human, partial goal achievement and, most importantly, the possibility to use observations of human actions in the policy generated for the robot. The planner has been tested as a standalone component and in conjunction with our framework for human-robot interaction in a real environment.

Recently, Helmert and Geffner proposed the context-enhanced additive heuristic, where fact costs are evaluated relative to context states that arise from achieving first a pivot condition of each operator. As Helmert and Geffner pointed out, the method can be generalized to consider contexts arising from arbitrary precedence constraints over operator conditions instead. Herein, we provide such a generalization. We extend Helmert and Geffner's equations, and discuss a number of design choices that arise. Drawing on previous work on goal orderings, we design a family of methods for automatically generating precedence constraints. We run large-scale experiments, showing that the technique can help significantly, depending on the choice of precedence constraints. We shed some light on this by profiling the behavior of all possible precedence constraints, using a sampling technique.

In order to scale with modern processors, planning algorithms must become multi-threaded. In this paper, we present parallel shared-memory algorithms for two problems that underlie many planning systems: suboptimal and anytime heuristic search. We extend a recently-proposed approach for parallel optimal search to the suboptimal case, providing two new pruning rules for bounded suboptimal search. We also show how this new approach can be used for parallel anytime search. Using temporal logic, we prove the correctness of our framework, and in an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8-core machine, we show that it yields faster search performance than previous proposals.