Efficient management and propagation of temporal constraints is important for temporal planning as well as for scheduling. During plan development, new events and temporal constraints are added and existing constraints may be tightened; the consistency of the whole temporal network is frequently checked; and results of constraint propagation guide further search. Recent work shows that enforcing partial path consistency provides an efficient means of propagating temporal information for the popular Simple Temporal Network (STN). We show that partial path consistency can be enforced incrementally, thus exploiting the similarities of the constraint network between subsequent edge tightenings. We prove that the worst-case time complexity of our algorithm can be bounded both by the number of edges in the chordal graph (which is better than the previous bound of the number of vertices squared), and by the degree of the chordal graph times the number of vertices incident on updated edges. We show that for many sparse graphs, the latter bound is better than that of the previously best-known approaches. In addition, our algorithm requires space only linear in the number of edges of the chordal graph, whereas earlier work uses space quadratic in the number of vertices. Finally, empirical results show that when incrementally solving sparse STNs, stemming from problems such as Hierarchical Task Network planning, our approach outperforms extant algorithms.

We propose a partially informed depth-first search algorithm to cope with the Job Shop Scheduling Problem with makespan minimization. The algorithm is built from the well-known P. Brucker's branch and bound algorithm. We improved the heuristic estimation of Brucker's algorithm by means of constraint propagation rules and so devised a more informed heuristic which is proved to be monotonic. We conducted an experimental study across medium and large instances. The results show that the proposed algorithm reaches optimal solutions for medium instances taking less time than branch and bound and that for large instances it reaches much better lower and upper bounds when both algorithms are given the same amount of time.

When planning in an uncertain environment, one is often interested in finding a contingent plan that prescribes appropriate actions for all possible states that may be encountered during the execution of the plan. We consider the problem of finding strong cyclic plans for fully observable nondeterministic (FOND) planning problems. The algorithm we choose is LAO*, an informed explicit state search algorithm. We investigate the use of pattern database (PDB) heuristics to guide LAO* towards goal states. To obtain a fully domain-independent planning system, we use an automatic pattern selection procedure that performs local search in the space of pattern collections. The evaluation of our system on the FOND benchmarks of the Uncertainty Part of the International Planning Competition 2008 shows that our approach is competitive with symbolic regression search in terms of problem coverage, speed, and plan quality.

We present a new paradigm for planning by learning, where the planner is given a model of the world and  a small set of states of interest, but no indication of optimal actions in these states. The additional information can help focus the planner on regions of the state space that are of interest and lead to improved performance. We demonstrate this idea by introducing novel model-checking reduction operations for First Order Decision Diagrams (FODD), a representation that has been used to implement decision-theoretic planning with Relational Markov Decision Processes (RMDP). Intuitively, these reductions modify the construction of the value function by removing any complex specifications that are irrelevant to the set of training examples, thereby focusing on the region of interest. We show that such training examples can be constructed on the fly from a description of the planning problem thus we can bootstrap to get a self-taught planning system. Additionally, we provide a new heuristic to embed universal and conjunctive goals within the framework of RMDP planners, expanding the scope and applicability of such systems. We show that these ideas lead to significant improvements in performance in terms of both speed and coverage of the planner, yielding state of the art planning performance on problems from the International Planning Competition.

When using a planner-based agent architecture, many things can go wrong. First and foremost, an agent might fail to execute one of the planned actions for some reasons. Even more annoying, however, is a situation where the agent is incompetent, i.e., unable to come up with a plan. This might be due to the fact that there are principal reasons that prohibit a successful plan or simply because the task's description is incomplete or incorrect. In either case, an explanation for such a failure would be very helpful. We will address this problem and provide a formalization of coming up with excuses for not being able to find a plan. Based on that, we will present an algorithm that is able to find excuses and demonstrate that such excuses can be found in practical settings in reasonable time.

Factored planning methods aim to exploit locality to efficiently solve large but "loosely coupled" planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of coordination points between local plans considered); the satisfaction of those bounds by a given problem instance is difficult to establish a priori, and the influence of those parameters on the problem complexity is unclear. We present an instance of the factored planning framework using a representation of the (regular) sets of local plans by finite automata, which does not require any such bound. By substituting weighted automata, we can even do factored cost-optimal planning. We test an implementation of the method on the few standard planning benchmarks that we have found to be amenable to factoring. We show that this method runs in polynomial time under conditions similar to those considered in previous work, but not only under those conditions. Thus, what constitutes an essential measure of "factorability" remains obscure.

DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by embedding the domain-independent satisficing YAHSP planner and using the critical path h1 heuristic. Experiments with the resulting algorithm are performed on a selection of IPC benchmarks from classical, cost-based and temporal domains. Under the experimental conditions of the IPC, and in particular with a universal parameter setting common to all domains, DAEYAHSP is compared to the best planner for each type of domain. Results show that DAEYAHSP performs very well both on coverage and quality metrics. It is particularly noticeable that DAEX improves a lot on plan quality when compared to YAHSP, which is known to provide largely suboptimal solutions; making it competitive with state-of-the-art planners. This article gives a full account of the algorithm, reports on the experiments and provides some insights on the algorithm behavior.

Over the last few years there has been a revival of interest in the idea of least-commitment planning with a number of researchers returning to the partial-order planning approaches of UCPOP and VHPOP. In this paper we explore the potential of a forward-chaining state-based search strategy to support partial-order planning in the solution of temporal-numeric problems. Our planner, POPF, is built on the foundations of grounded forward search, in combination with linear programming to handle continuous linear numeric change. To achieve a partial ordering we delay commitment to ordering decisions, timestamps and the values of numeric parameters, managing sets of constraints as actions are started and ended. In the context of a partially ordered collection of actions, constructing the linear program is complicated and we propose an efficient method for achieving this. Our late-commitment approach achieves flexibility, while benefiting from the informative search control of forward planning, and allows temporal and metric decisions to be made - as is most efficient - by the LP solver rather than by the discrete reasoning of the planner. We compare POPF with the approach of constructing a sequenced plan and then lifting a partial order from it, showing that our approach can offer improvements in terms of makespan, and time to find a solution, in several benchmark domains.

We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.

An interesting class of planning domains, including planning for daily activities of Mars rovers, involves achievement of goals with time constraints and concurrent actions with probabilistic durations. Current probabilistic approaches, which rely on a discrete time model, introduce a blow up in the search state-space when the two factors of action concurrency and action duration uncertainty are combined. Simulation-based and sampling probabilistic planning approaches would cope with this state explosion by avoiding storing all the explored states in memory, but they remain approximate solution approaches. In this paper, we present an alternative approach relying on a continuous time model which avoids the state explosion caused by time stamping in the presence of action concurrency and action duration uncertainty. Time is represented as a continuous random variable. The dependency between state time variables is conveyed by a Bayesian network, which is dynamically generated by a state-based forward-chaining search based on the action descriptions. A generated plan is characterized by a probability of satisfying a goal. The evaluation of this probability is done by making a query the Bayesian network.