Fox, Maria

A Compilation of the Full PDDL+ Language into SMT

AAAI Conferences

Planning in hybrid systems is important for dealing with real-world applications. PDDL+ supports this representation of domains with mixed discrete and continuous dynamics, and supports events and processes modelling exogenous change. Motivated by numerous SAT-based planning approaches, we propose an approach to PDDL+ planning through SMT, describing an SMT encoding that captures all the features of the PDDL+ problem as published by Fox and Long. The encoding can be applied on domains with nonlinear continuous change. We apply this encoding in a simple planning algorithm, demonstrating excellent results on a set of benchmark problems.

Leveraging Probabilistic Reasoning in Deterministic Planning for Large-Scale Autonomous Search-and-Tracking

AAAI Conferences

Search-And-Tracking (SaT) is the problem of searching for a mobile target and tracking it once it is found. Since SaT platforms face many sources of uncertainty and operational constraints, progress in the field has been restricted to simple and unrealistic scenarios. In this paper, we propose a new hybrid approach to SaT that allows us to successfully address large-scale and complex SaT missions. The probabilistic structure of SaT is compiled into a deterministic planning model and Bayesian inference is directly incorporated in the planning mechanism. Thanks to this tight integration between automated planning and probabilistic reasoning, we are able to exploit the power of both approaches. Planning provides the tools to efficiently explore big search spaces, while Bayesian inference, by readily combining prior knowledge with observable data, allows the planner to make more informed and effective decisions. We offer experimental evidence of the potential of our approach.

Solving Realistic Unit Commitment Problems Using Temporal Planning: Challenges and Solutions

AAAI Conferences

When facing real world planning problems, standard planners are often inadequate and enhancement of the current techniques are required. In this paper we present the challenges that we have faced in solving the Unit Commitment (UC) problem, a well-known problem in the electrical power industry for which current best methods are based on Mixed Integer Programming (MIP). Typical UC instances involve hundreds or even thousands of generating units, pushing the scalability of state of the art planners beyond their limits. Furthermore, UC is characterised by state-dependent action costs, a feature that not many domain independent planners can efficiently handle. In this paper we focus on the challenge of making domain-independent planning competitive with the MIP method on realistic-sized UC instances. We present the results of our investigation into modelling the UC problem as a temporal planning problem, and show how we scaled up from handling fewer than 10 generating units to more than 400, obtaining solutions almost as high quality as those generated by MIP. We conclude by discussing future directions for temporal planning in this domain, that lie beyond what can be modelled and solved using MIP methods.

Heuristic Evaluation Based on Lifted Relaxed Planning Graphs

AAAI Conferences

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.

Planning the Behaviour of Low-Cost Quadcopters for Surveillance Missions

AAAI Conferences

Micro Aerial Vehicles (MAVs) are increasingly regarded as a valid low-cost alternative to UAVs and ground robots in surveillance missions and a number of other civil and military applications. Research on autonomous MAVs is still in its infancy and has focused almost exclusively on integrating control and computer vision techniques to achieve reliable autonomous flight. In this paper, we describe our approach to using automated planning in order to elicit high-level intelligent behaviour from autonomous MAVs engaged in surveillance applications. Planning offers effective tools to handle the unique challenges faced by MAVs that relate to their fast and unstable dynamics as well as their low endurance and small payload capabilities. We demonstrate our approach by focusing on the "Parrot AR.Drone2.0" quadcopter and Search-and-Tracking missions, which involve searching for a mobile target and tracking it after it is found.

Autonomous Search and Tracking via Temporal Planning

AAAI Conferences

Search And Tracking (SAT) is the problem of searching for a mobile target and tracking it after it is found. As this problem has important applications in search-and-rescue and surveillance operations, recently there has been increasing interest in equipping unmanned aerial vehicles (UAVs) with autonomous SAT capabilities. State-of-the-art approaches to SAT rely on estimating the probability density function of the target's state and solving the search control problem in a greedy fashion over a short planning horizon (typically, a one-step lookahead). These techniques suffer high computational cost, making them unsuitable for complex problems. In this paper, we propose a novel approach to SAT, which allows us to handle big geographical areas, complex target motion models and long-term operations. Our solution is to track the target reactively while it is in view and to plan a recovery strategy that relocates the target every time it is lost, using a high-performing automated planning tool. The planning problem consists of deciding where to search and which search patterns to use in order to maximise the likelihood of recovering the target. We show experimental results demonstrating the potential of our approach.

Partially Grounded Planning as Quantified Boolean Formula

AAAI Conferences

This paper describes a technique for translating bounded propositional reachability problems, such as Planning, into Quantified Boolean Formulae (QBF). The key feature of this translation is that the problem, and the resultant encoding is only partially grounded. The technique is applicable to other SAT or QBF encodings as an additional improvement, potentially reducing the size of the resulting formula by an exponential amount. We present experimental results showing that the approach applied to a simple SAT translation greatly improves the time taken to encode and solve problems in which there are many objects of a single type, even solving some problems that cannot be reasonably encoded as SAT.

Introducing Worldwide AI

AI Magazine

We are pleased to introduce Worldwide AI — a new column in AI Magazine that we hope will keep AAAI members informed about AI efforts around the world.

Extending the Use of Inference in Temporal Planning as Forwards Search

AAAI Conferences

PDDL 2.1 supports modelling of complex temporal planning domains in which solutions must exploit concurrency. Few existing temporal planners can solve problems that require concurrency and those that do typically pay a performance price to deploy reasoning machinery that is not always required. In this paper we show how to improve the performance of forward-search planners that attempt to solve the full temporal planning problem, both by narrowing the use of the concurrency machinery to situations that demand it and also by improving the power of inference to prune redundant branches of the search space for common patterns of interaction in temporal domains that do require concurrency. Results illustrate the effectiveness of our ideas in improving the efficiency of a temporal planner that can solve problems with required concurrency, both in domains that exploit this ability and in those that do not.