In this paper a planning framework based on Ant Colony Optimization techniques is presented. It is well known that finding optimal solutions to planning problems is a very hard computational problem. Stochastic methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several approaches based both on backward and forward search over the state space, using several heuristics and testing different pheromone models in order to solve sequential optimization planning problems.

We consider real-time planning problems in which some states are unsolvable, i.e., have no path to a goal. Such problems are difficult for real-time planning algorithms such as RTA* in which all states must be solvable. We identify a property called k-safeness, in which the consequences of a bad choice become apparent within k moves after the choice is made. When k is not too large, this makes it possible to identify unsolvable states in real time. We provide a modified version of RTA* that is provably complete on all k -safe problems. We derive k -safeness conditions for real-time deterministic versions of the well-known Tireworld and Racetrack domains, and provide experimental results showing that our modified version of RTA* works quite well in these domains.

The field of automated plan generation has recently significantly advanced. However, while several powerful domainindependent PbP has two variants: PbP.s focusing on speed, and planners have been developed, no one of these PbP.q focusing on plan quality. PbP.s entered the learning clearly outperforms all the others in every known benchmark track of the sixth international planning competition (IPC6), domain. It would then be useful to have a multi-planner system and was the overall winner of this competition track (Fern, that automatically selects and combines the most efficient Khardon and Tadepalli 2008). The paper includes some experimental planner(s) for each given domain.

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.

This paper proposes an iterative improvement approach for solving the Resource Constraint Project Scheduling Problem with Time-Windows (RCPSP/max), a well-known and challenging NP-hard scheduling problem. The algorithm is based on Iterative Flattening Search (IFS), an effective heuristic strategy for solving multi-capacity optimization scheduling problems. Given an initial solution, IFS iteratively performs two-steps: a relaxation-step , that randomly removes a subset of solution constraints and a solving-step , that incrementally recomputes a new solution. At the end, the best solution found is returned. The main contribution of this paper is the extension to RCPSP/max of the IFS optimization procedures developed for solving scheduling problems without time-windows. An experimental evaluation performed on medium-large size and web-available benchmark sets confirms the effectiveness of the proposed procedures. In particular, we have improved the average quality w.r.t. the current bests, while discovering three new optimal solutions, thus demonstrating the general efficacy of IFS.

Despite its ubiquitous presence, very little is known about the odds of winning the simple card game of Klondike Solitaire. The main goal of this paper is to investigate the use of probabilistic planning to shed light on this issue. Unfortunatley, most probabilistic planning techniques are not well suited for Klondike due to the difficulties of representing the domain in standard planning languages and the complexity of the required search. Klondike thus serves as an interesting addition to the complement of probabilistic planning domains. In this paper, we study Klondike using several sampling-based planning approaches including UCT, hindsight optimization, and sparse sampling, and establish lower bounds on their performance. We also introduce novel combinations of these approaches and evaluate them in Klondike. We provide a theoretical bound on the sample complexity of a method that naturally combines sparse sampling and UCT. Our results demonstrate that there is a policy that within tight confidence intervals wins over 35% of Klondike games. This result is the first reported lower bound of an optimal Klondike policy.

Current state-of-the-art planners solve problems, easy and hard alike, by search, expanding hundreds or thousands of nodes. Yet, given the ability of people to solve easy problems and to explain their solutions, it seems that an essential inferential component may be missing. The reasons expressed by people for selecting  actions appear to be related to causal chains: sequences of causal links a i → p i + 1 , i = 0, ..., n – 1, such that a 0 is applicable in the current state, p i is a precondition of action a i , and p n is a goal. Some of these causal chains or paths appear to be good, some bad, others appear to be impossible. In this work, we focus on such paths and develop three techniques for performing inference over them  from which a  path-based planner is obtained. We define the conditions under which a path is consistent, provide an heuristic estimate of the cost of achieving the goal along a consistent path, and introduce a planning algorithm that uses paths as decomposition backbones. The resulting planner, called C3, is not complete and does not perform as well as recent planners that carry extensive but extremely efficient searches such as LAMA, but is competitive with FF and in particular, with FF running in EHC mode which yields very focused but incomplete searches, and thus provides, a more apt comparison. Moreover, many domains are solved backtrack-free, with no search at all, suggesting that planning with paths may be a meaningful idea both cognitively and computationally.

Many potential real-world planning applications are on the border of planning and scheduling. To handle the complex choices of actions and temporal and resource constraints of these problems we need to integrate planning and scheduling techniques. Here we propose a transition-based formulation of temporal planning problems, that enables us to represent features like deadlines, time windows, release times etc. in a simple way. We describe a CSP encoding of the transition-based formulation and its potential advantages in integrating planning and scheduling techniques.

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.

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.