We present a scalable fully-automated forward-chaining planner capable of reasoning with PDDL+ events and linear processes.  Processes and events model (respectively) continuous and discrete exogenous activity in the environment, occurring when certain conditions hold.  We discuss the significant challenges posed in creating a planner that can reason with these, and present novel state-progression and consistency enforcing techniques that allow us to meet these challenges.  We present results showing that our new planner, using PDDL+ models, is able to solve realistic expressive problems more efficiently than the state-of-the-art alternative: a compiled PDDL 2.1 representation with continuous numeric effects.

The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine’s capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer’s time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domainindependent automated planning reduce machine downtime by 10.6% and the uncertainty of measurement by 59%, it is also possible to optimise both metrics reaching a compromise that is on average 9% worse that the best-known solution for each individual metric.

We present a novel approach to fully-observable nondeterministic planning (FOND) that attempts to bridge the gap between symbolic fix-point computation and recent approaches based on forward heuristic search. Concretely, we formalize the relationship between symbolic and dynamic programming nondeterministic planners, and then exploit such connection to propose a novel familyof planning algorithms that reasons over symbolic policies in a directed manner. By doing so, our proposal reasons over sets of states and executions in a succinct way (as done by symbolic planners) while biasing the reasoning with respect to the initial and goal states of the specific planning problem at hand (as done by heuristic planners). We show empirical results that prove this approach promising in settings where there is an intrinsic tension between plan efficiency and plan "robustness," a feature to be expected in nondeterministic domains.

Linking online planning for MDPs with their special case of stochastic multi-armed bandit problems, we analyze three state-of-the-art Monte-Carlo tree search al-gorithms: UCT, BRUE, and MaxUCT. Using the outcome, we (i) introduce two new MCTS algorithms,MaxBRUE, which combines uniform sampling with Bellman backups, and MpaUCT, which combines UCB1with a novel backup procedure, (ii) analyze them formally and empirically, and (iii) show how MCTS algorithms can be further stratified by an exploration control mechanism that improves their empirical performance without harming the formal guarantees.

In previous work we have shown that grounding, while used by most (if not all) modern state-of-the-art planners, is not necessary and is sometimes even undesirable. In this paper we extend this work and present a novel forward-chaining planner that does not require grounding and can solve problem instances that are too large for current planners to handle. We achieve this by exploiting equivalence relationships between objects whist constructing a lifted version of the relaxed planning graph (RPG) and extracting a relaxed plan. We compare our planner to FF and show that our approach consumes far less memory whist still being competitive. In addition we show that by not having to ground the domain we can solve much larger problem instances.

We investigate the use of a simple, discriminative reranking approach to plan recognition in an abductive setting. In contrast to recent work, which attempts to model abductive plan recognition using various formalisms that integrate logic and graphical models (such as Markov Logic Networks or Bayesian Logic Programs), we instead advocate a simpler, more flexible approach in which plans found through an abductive beam-search are discriminatively scored based on arbitrary features. We show that this approach performs well even with relatively few positive training examples, and we obtain state-of-the-art results on two abductive plan recognition datasets, outperforming more complicated systems.

This paper is concerned with temporal planning relying on CSP-based functional representations. These powerful representations are today mostly restricted to the use of piecewise constant functions ranging over finite domains. We are proposing here an extension that brings a significant enhancement in the expressiveness of the representation, towards handling continuous change. This extension consists mainly in allowing piecewise linear functions over continuous domains. We have studied this extension and are currently implementing it in the IXTET planner. However this extended representation is not specific to IXTET; it can be useful to most temporal planners. We show how IXTET syntax, planning algorithm and control can be simply extended to this class of functions. We then consider the more significant modifications required in the two constraints managers for handling temporal and atemporal CSPs.

List of program committee members for the Twenty-Fourth International Conference on Automated Planning and Scheduling.

We study a daily mine planning problem where, given a set of blocks we wishto mine, our task is to generate a mining sequence for the excavators suchthat blending resource constraints are met at various stages of thesequence. Such time-oriented resource constraintsare not traditionally handled well by automated planners. On the other hand,the remaining problem involves finding node-disjoint sequences withstate-dependent travel times on the arcs, which are highly challenging for a Mixed-Integer Program (MIP).In this paper, we address the problem of finding feasible sequences using a combined MIP and planning based decomposition approach. The MIP takes care of the resource constraints, and the planner solves the remaining sequence problem. We extend the notion of finding feasible sequences to finding good feasible sequences, by devising a heuristic objective function in the MIP, which improves the resulting search space for the planner.We empirically analyse the scalability of our approach on a benchmark data set, before demonstrating its effectiveness on a real world case study provided by our industry partner. These results demonstrate that by using a heuristic MIP, it is possible to obtain better makespan results with a suboptimal planner than by using an optimal planner with an uninformed MIP.

This paper describes the Dynamic Execution Order Scheduling (DEOS) system that has been developed to handle highly dynamic and interactive scheduling domains. Unlike typical scheduling problems which have a static task list, DEOS is able to handle dynamic task lists in which tasks are added, deleted and modified “on the fly" DEOS is also able to handle tasks with uncertain and/or probabilistic outcomes. DEOS extends the current scheduling paradigm to allow tasking in dynamic and uncertain environments by viewing the planning and scheduling tasks as being integrated and evolving entities. DEOS has been successfully applied to the domains of Air Campaign Planning (ACP) and Intelligence, Surveillance and Reconnaissance (ISR) management. The paper provides an overview of the dynamic task model and the “penalty box" scheduling algorithm which was developed to provide robust solutions to over constrained scheduling problems. The basic algorithm is described together with extensions to handle flexible time constraints.