Efficient use of multiple batteries is a practical problem with wide and growing application. The problem can be cast as a planning problem under uncertainty. We describe the approach we have adopted to modelling and solving this problem, seen as a Markov Decision Problem, building effective policies for battery switching in the face of stochastic load profiles. Our solution exploits and adapts several existing techniques: planning for deterministic mixed discrete-continuous problems and Monte Carlo sampling for policy learning. The paper describes the development of planning techniques to allow solution of the non-linear continuous dynamic models capturing the battery behaviours. This approach depends on carefully handled discretisation of the temporal dimension. The construction of policies is performed using a classification approach and this idea offers opportunities for wider exploitation in other problems. The approach and its generality are described in the paper. Application of the approach leads to construction of policies that, in simulation, significantly outperform those that are currently in use and the best published solutions to the battery management problem. We achieve solutions that achieve more than 99% efficiency in simulation compared with the theoretical limit and do so with far fewer battery switches than existing policies. Behaviour of physical batteries does not exactly match the simulated models for many reasons, so to confirm that our theoretical results can lead to real measured improvements in performance we also conduct and report experiments using a physical test system. These results demonstrate that we can obtain 5%-15% improvement in lifetimes in the case of a two battery system.

In this paper we introduce a framework for option model composition. Option models are temporal abstractions that, like macro-operators in classical planning, jump directly from a start state to an end state. Prior work has focused on constructing option models from primitive actions, by intra-option model learning; or on using option models to construct a value function, by inter-option planning. We present a unified view of intra- and inter-option model learning, based on a major generalisation of the Bellman equation. Our fundamental operation is the recursive composition of option models into other option models. This key idea enables compositional planning over many levels of abstraction. We illustrate our framework using a dynamic programming algorithm that simultaneously constructs optimal option models for multiple subgoals, and also searches over those option models to provide rapid progress towards other subgoals.

Answer Set Programming (ASP) is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since its first informal editions, ASP systems have been compared in the now well-established ASP Competition. The Third (Open) ASP Competition, as the sequel to the ASP Competitions Series held at the University of Potsdam in Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took place at the University of Calabria (Italy) in the first half of 2011. Participants competed on a pre-selected collection of benchmark problems, taken from a variety of domains as well as real world applications. The Competition ran on two tracks: the Model and Solve (M&S) Track, based on an open problem encoding, and open language, and open to any kind of system based on a declarative specification paradigm; and the System Track, run on the basis of fixed, public problem encodings, written in a standard ASP language. This paper discusses the format of the Competition and the rationale behind it, then reports the results for both tracks. Comparison with the second ASP competition and state-of-the-art solutions for some of the benchmark domains is eventually discussed. To appear in Theory and Practice of Logic Programming (TPLP).

The need to economize limited resources, such as fuel or money, is aubiquitous feature of planning problems. If the resources cannot bereplenished, the planner must make do with the initial supply. It isthen of paramount importance how constrained the problem is,i.e., whether and to which extent the initial resource supply exceedsthe minimum need. While there is a large body of literature on numericplanning and planning with resources, such resource constrainednesshas only been scantily investigated. We herein start to address thisin more detail. We generalize the previous notion of resourceconstrainedness, characterized through a numeric problem feature C≥1 , to the case of multiple resources. We implement an extendedbenchmark suite controlling C . We conduct a large-scale study of thecurrent state of the art as a function of C , highlighting whichtechniques contribute to success. We introduce two new techniques ontop of a recent Monte Carlo Random Walk method, resulting in a plannerthat, in these benchmarks, outperforms previous planners whenresources are scarce ( C close to 1 ). We investigate the parametersinfluencing the performance of that planner, and we show that one ofthe two new techniques works well also on the regular IPC benchmarks.

Optimal plans of delete-free planning tasks are interesting both in domains that have no delete effects and as the relaxation heuristic h+ in general planning. Many heuristics for optimal and satisficing planning approximate the h+ heuristic, which is well-informed and admissible but intractable to compute. In this work, branch-and-bound and IDA* search are used in a search space tailored to delete-free planning together with an incrementally computed version of the LM-cut heuristic. The resulting algorithm for optimal delete-free planning exceeds the performance of A* with the LM-cut heuristic in the state-of-the-art planner Fast Downward.

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.

We address the problem of computing a policy for fully observable non-deterministic (FOND) planning problems. By focusing on the relevant aspects of the state of the world, we introduce a series of improvements to the previous state of the art and extend the applicability of our planner, PRP, to work in an online setting. The use of state relevance allows our policy to be exponentially more succinct in representing a solution to a FOND problem for some domains. Through the introduction of new techniques for avoiding deadends and determining sufficient validity conditions, PRP has the potential to compute a policy up to several orders of magnitude faster than previous approaches. We also find dramatic improvements over the state of the art in online replanning when we treat suitable probabilistic domains as FOND domains.

We present a simple and efficient algorithm to solve delete-free planning problems optimally and calculate the h+ heuristic. The algorithm efficiently computes a minimum-cost hitting set for a complete set of disjunctive action landmarks generated on the fly. Unlike other recent approaches, the landmarks it generates are guaranteed to be set-inclusion minimal. In almost all delete-relaxed IPC domains, this leads to a significant coverage and runtime improvement.

Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.

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.