Two extreme approaches can be applied to solve a probabilistic planning problem, namely closed loop algorithms and open loop (a.k.a. replanning) algorithms. While closed loop algorithms invest significant computational effort to generate a closed form solution, open loop algorithms compute open form solutions and interact with the environment in order to refine the computed solution. In this paper, we introduce short-sighted Stochastic Shortest Path (SSP), a new model in which solutions computed based on it can be executed for at least t steps as a closed form solution. Using short-sighted SSPs, we present a novel probabilistic planner called Short-sighted Open Loop Planner (SOLP) that bridges the gap between open and closed loop planners by varying the parameter t: as t increases, more actions can be executed without replanning and, for t sufficiently large, a closed form solution is obtained. We prove that SOLP is asymptotically optimal. To the best of our knowledge, SOLP is the unique probabilistic planner that at the same time provides both replanning and optimality guarantees. We empirically compare SOLP with the winners of the previous probabilistic planning competitions and SOLP outperforms all of them in 33.3% of the problems and ties with the best planner in 48.3% of the problems.

The Liner Shipping Fleet Repositioning Problem (LSFRP) poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between services in a liner shipping network. The LSFRP is characterized by chains of interacting activities, many of which have costs that are a function of their duration; for example, sailing slowly between two ports is cheaper than sailing quickly. Despite its great industrial importance, the LSFRP has received little attention in the literature. We show how the LSFRP can be solved sub-optimally using the planner POPF and optimally with a mixed-integer program (MIP) and a novel method called Temporal Optimization Planning (TOP). We evaluate the performance of each of these techniques on a dataset of real-world instances from our industrial collaborator, and show that automated planning scales to the size of problems faced by industry.

Stability analysis consists of identifying conditions under which the number of jobs in a system is guaranteed to remain bounded over time. To date, such long-run performance guarantees have not been available for periodic approaches to dynamic scheduling problems. However, stability has been extensively studied in queueing theory. In this paper, we introduce stability to the dynamic scheduling literature and demonstrate that stability guarantees can be obtained for methods that build the schedule for a dynamic problem by periodically solving static deterministic sub-problems. Specifically, we analyze the stability of two dynamic environments: a two-machine flow shop, which has received significant attention in scheduling research, and a polling system with a flow-shop server, an extension of systems typically considered in queueing. We demonstrate that, among stable policies, methods based on periodic optimization of static schedules may achieve better mean flow times than traditional queueing approaches.

We present the domain-independent HRFF algorithm, which solves goal-oriented HMDPs by incrementally aggregating plans generated by the METRIC-FF planner into a policy defined over discrete and continuous state variables. HRFF takes into account non-monotonic state variables, and complex combinations of many discrete and continuous probability distributions. We introduce new data structures and algorithmic paradigms to deal with continuous state spaces: hybrid hierarchical hash tables, domain determinization based on dynamic domain sampling or on static computation of probability distributions' modes, optimization settings under METRIC-FF based on plan probability and length. We deeply analyze the behavior of HRFF on a probabilistically-interesting structured navigation problem with continuous dead-ends and non-monotonic continuous state variables. We compare with HAO* on the Rover domain and show that HRFF outperforms HAO* by many order of magnitudes in terms of computation time and memory usage. We also experiment challenging and combinatorial HMDP versions of benchmarks from numeric classical planning.

Moving-target search, where a hunter has to catch a moving target, is an important problem for video game developers. In our case, the hunter repeatedly moves towards the target and thus has to solve similar search problems repeatedly. We develop Incremental ARA* (I-ARA*) for this purpose, the first incremental anytime search algorithm for moving-target search in known terrain. We provide an error bound on the lengths of the paths found by I-ARA* and show experimentally in known four-neighbor gridworlds that I-ARA* can be used with smaller time limits between moves of the hunter than competing state-of-the-art moving-target search algorithms, namely repeated A*, G-FRA*, FRA*, and sometimes repeated ARA*. The hunter tends to make more moves with I-ARA* than repeated A*, G-FRA* or FRA*, which find shortest paths for the hunter, but fewer moves with I-ARA* than repeated ARA*, which finds suboptimal paths for the hunter like I-ARA*. Also, the error bounds on the lengths of the paths of the hunter tend to be smaller with I-ARA* than repeated ARA*.

We consider the problem of computing shortest paths subject to an additional resource constraint such as a hard limit on the (positive) height difference of the path. This is typically of interest in the context of bicycle route planning, or when energy consumption is to be limited. So far, the exact computation of such constrained shortest paths was not feasible on large networks; we show that state-of-the-art speed-up techniques for the shortest path problem, like contraction hierarchies, can be instrumented to solve this problem efficiently in practice despite the NP-hardness in general.

Human users who execute an automatically generated plan want to understand the rationale behind it. Knowledge-rich plans are particularly suitable for this purpose, because they provide the means to give reason for causal, temporal, and hierarchical relationships between actions. Based on this information, focused arguments can be generated that constitute explanations on an appropriate level of abstraction. In this paper, we present a formal approach to plan explanation. Information about plans is represented as first-order logic formulae and explanations are constructed as proofs in the resulting axiomatic system. With that, plan explanations are provably correct w.r.t. the planning system that produced the plan. A prototype plan explanation system implements our approach and first experiments give evidence that finding plan explanations is feasible in real-time.

Engineering companies working with machine tools will often be required to calibrate those machines to international standards. The calibration process requires various errors in the machine to be measured by a skilled expert. In addition to conducting the tests, the engineer must also plan the order in which the tests should take place, and also which instruments should be used to perform each test. It is critical to find as short a calibration plan as possible so that the machine is not out of service for too long. In this work, automated planning is applied to the problem of generating machine tool calibration plans. Given a description of a machine, and its various axes, we produce a calibration plan that minimises the time taken to measure all of the errors of a machine. We also consider the case when there is not enough time to test all errors of the machine, and the calibration plan must maximise the importance of the set of errors tested in the limited time available.

We present a novel optimization-based algorithm for motion planning in dynamic environments. Our approach uses a stochastic trajectory optimization framework to avoid collisions and satisfy smoothness and dynamics constraints. Our algorithm does not require a priori knowledge about global motion or trajectories of dynamic obstacles. Rather, we compute a conservative local bound on the position or trajectory of each obstacle over a short time and use the bound to compute a collision-free trajectory for the robot in an incremental manner. Moreover, we interleave planning and execution of the robot in an adaptive manner to balance between the planning horizon and responsiveness to obstacle. We highlight the performance of our planner in a simulated dynamic environment with the 7-DOF PR2 robot arm and dynamic obstacles.

This paper provides an analysis of the efficacy of a known iterative improvement meta-heuristic approach from the AI area in solving the Blocking Job Shop Scheduling Problem (BJSSP) class of problems. The BJSSP is known to have significant fallouts on practical domains, and differs from the classical Job Shop Scheduling Problem (JSSP) in that it assumes that there are no intermediate buffers for storing a job as it moves from one machine to another; according to the BJSSP definition, each job has to wait on a machine until it can be processed on the next machine. In our analysis, two specific variants of the iterative improvement meta-heuristic are evaluated: (1) an adaptation of an existing scheduling algorithm based on the Iterative Flattening Search and (2) an off-the-shelf optimization tool, the IBM ILOG CP Optimizer, which implements Self-Adapting Large Neighborhood Search. Both are applied to a reference benchmark problem set and comparative performance results are presented. The results confirm the effectiveness of the iterative improvement approach in solving the BJSSP; both variants perform well individually and together succeed in improving the entire set of benchmark instances.