In contrast to previous competitions, where the problems were goal-based, the 2011 International Probabilistic Planning Competition (IPPC-2011) emphasized finite-horizon reward maximization problems with large branching factors. These MDPs modeled more realistic planning scenarios and presented challenges to the previous state-of-the-art planners (e.g., those from IPPC-2008), which were primarily based on domain determinization — a technique more suited to goal-oriented MDPs with small branching factors. Moreover, large branching factors render the existing implementations of RTDP- and LAO-style algorithms inefficient as well. In this paper we present GLUTTON, our planner at IPPC-2011 that performed well on these challenging MDPs. The main algorithm used by GLUTTON is LR2TDP, an LRTDP-based optimal algorithm for finite-horizon problems centered around the novel idea of reverse iterative deepening. We detail LR2TDP itself as well as a series of optimizations included in GLUTTON that help LR2TDP achieve competitive performance on difficult problems with large branching factors -- subsampling the transition function, separating out natural dynamics, caching transition function samples, and others. Experiments show that GLUTTON and PROST, the IPPC-2011 winner, have complementary strengths, with GLUTTON demonstrating superior performance on problems with few high-reward terminal states.

There has been much interest recently in problems that com-bine high-level task planning with low-level motion planning.In this paper, we present a problem of this kind that arises inmulti-vehicle mission planning. It tightly integrates task al-location and scheduling, who will do what when, with pathplanning, how each task will actually be performed. It ex-tends classical vehicle routing in that the cost of executing aset of high-level tasks can vary significantly in time and costaccording to the low-level paths selected. It extends classi-cal motion planning in that each path must minimize costwhile also respecting temporal constraints, including thoseimposed by the agent’s other tasks and the tasks assigned toother agents. Furthermore, the problem is a subtask withinan interactive system and therefore must operate within se-vere time constraints. We present an approach to the problembased on a combination of tabu search, linear programming,and heuristic search. We evaluate our planner on represen-tative problem instances and find that its performance meetsthe demanding requirements of our application. These resultsdemonstrate how integrating multiple diverse techniques cansuccessfully solve challenging real-world planning problemsthat are beyond the reach of any single method.

Heuristics based on the delete relaxation are at the forefront of modern domain-independent planning techniques. Here we introduce a principled and flexible technique for augmenting delete-relaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task. Differently from previous work in this direction, conditional effects are used to limit the growth of the task to be linear, rather than exponential, in the number of conjunctions that are introduced, making its use for obtaining heuristic functions feasible. We discuss how to obtain an informative set of conjunctions to be represented explicitly, and analyze and extend existing methods for relaxed planning in the presence of conditional effects. The resulting heuristics are empirically evaluated, and shown to be sometimes much more informative than standard delete-relaxation heuristics.

We present PROST, a probabilistic planning system that is based on the UCT algorithm by Kocsis and Szepesvari (2006), which has been applied successfully to many areas of planning and acting under uncertainty. The objective of this paper is to show the application of UCT to domain- independent probabilistic planning, an area it had not been applied to before. We furthermore present several enhance- ments to the algorithm, including a method that is able to drastically reduce the branching factor by identifying super- fluous actions. We show how search depth limitation leads to a more thoroughly investigated search space in parts that are influential on the quality of a policy, and present a sound and polynomially computable detection of reward locks, states that correspond to, e.g., dead ends or goals. We describe a general Q-value initialization for unvisited nodes in the search tree that circumvents the initial random walks inher- ent to UCT, and leads to a faster convergence on average. We demonstrate the significant influence of the enhancements by providing a comparison on the IPPC benchmark domains.

Existing ship scheduling approaches either ignore constraints on ship draft (distance between the waterline and the keel), or model these in very simple ways, such as a constant draft limit that does not change with time. However, in most ports the draft restriction changes over time due to variation in environmental conditions. More accurate consideration of draft constraints would allow more cargo to be scheduled for transport on the same set of ships. We present constraint programming (CP) and mixed integer programming (MIP) models for the problem of scheduling ships at a port with time-varying draft constraints so as to optimise cargo throughput at the port. We also investigate the effect of several variations to the CP model, including a model containing sequence variables, and a model with ordered inputs. Our model allows us to solve realistic instances of the problem to optimality in a very short time, and produces better schedules than both scheduling with constant draft, and manual scheduling approaches used in practice at ports.

Merge-and-shrink abstraction (M&S) is an approach for constructing admissible heuristic functions for cost-optimal planning. It enables the targeted design of abstractions, by allowing to choose individual pairs of (abstract) states to aggregate into one. A key question is how to actually make these choices, so as to obtain an informed heuristic at reasonable computational cost. Recent work has addressed this via the well-known notion of bisimulation. When aggregating only bisimilar states -- essentially, states whose behavior is identical under every planning operator -- M&S yields a perfect heuristic. However, bisimulations are typically exponentially large. Thus we must relax the bisimulation criterion, so that it applies to more state pairs, and yields smaller abstractions. We herein devise a fine-grained method for doing so. We restrict the bisimulation criterion to consider only a subset K of the planning operators. We show that, if K is chosen appropriately, then M&S still yields a perfect heuristic, while abstraction size may decrease exponentially. Designing practical approximations for K, we obtain M&S heuristics that are competitive with the state of the art.

Considerable effort has been spent extending the scope of planning beyond propositional domains to include, for example, time and numbers. Each extension has been designed as a separate specific semantic enrichment of the underlying planning model, with its own syntax and customised integration into a planning algorithm. Inspired by work on SAT Modulo Theories (SMT) in the SAT community, we develop a modelling language and planner that treat arbitrary first order theories as parameters. We call the approach Planning Modulo Theories (PMT). We introduce a modular language to represent PMT problems and demonstrate its benefits over PDDL in expressivity and compactness. We present a generalisation of the $h_{max}$ heuristic that allows our planner, PMTPlan, to automatically reason about arbitrary theories added as modules. Over several new and existing benchmarks, exploiting different theories, we show that PMTPlan can significantly out-perform an existing planner using PDDL models.

Delete-free planning underlies many popular relaxation (h+) based heuristics used in state-of-the-art planners; it provides a simpler setting for exploring new pruning methods and other ideas; and a number of interesting recent planning domains are naturally delete-free. In this paper we explore new pruning methods for planning in delete-free planning domains. First, we observe that optimal delete-free plans can be composed from contiguous sub-plans that focus on one fact landmark at a time. Thus, instead of attempting to achieve the goal, the planner can focus on more easily achievable landmarks at each stage. Then, we suggest a number of complementary pruning techniques that are made more powerful with this observation. To carry out these pruning techniques efficiently, we make heavy use of an And/Or graph depicting the planning problem. We empirically evaluate these ideas using the FD framework, and show that they lead to clear improvements.

This work describes an approach that automatically extracts standard metadata information from e-learning contents, combines it with the student preferences/goals and creates PDDL planning domains+problems.These PDDL problems can be solved by current planners, although we motivate the use and benefits of case-based planning techniques, to obtain fully tailored learning routes that significantly enhance the learning process. During the execution of a given route, a monitoring phase is used to detect discrepancies, i.e. flaws that prevent the student from continuing with the original plan. In such a situation, an adaptation mechanism becomes necessary to fix the flaws, while also trying to minimise the differences between the original and the new route. We have integrated this approach on top of Moodle and experimented with 100 benchmark problems to evaluate the quality, scalability and viability of the system.

We present several new contributions in sampling-based coverage path planning, the task of finding feasible paths that give 100% sensor coverage of complex structures in obstaclefilled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Second, we introduce a new algorithm for the iterative improvement of a feasible coverage path; this relies on a samplingbased subroutine that makes asymptotically optimal local improvements to a feasible coverage path based on a strong generalization of the RRT* algorithm. We then apply the algorithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunction with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with unprecedented efficiency.