Maria Fox

JAIR Journal 2025 Journal Article

Path-Planning on a Spherical Surface with Disturbances and Exclusion Zones

• Jonathan Smith
• Samuel Hall
• George Coombs
• Harrison Abbot
• Ayat Fekry
• Michael A. S. Thorne
• Derek Long
• Maria Fox

An algorithm is presented for path-planning in a non-uniform spheroid mesh containing exclusion zones, vector and scalar fields. The mesh models physical environments such as ocean regions, together with a variety of environmental phenomena such as wind, current and ice conditions which impact on routing decisions. The path-planning method can be used to optimise the travel time of journeys between points in the mesh. We provide the algorithmic details and the mathematical foundations of the algorithms. To demonstrate that the method has basic desirable properties, we show that long paths in unconstrained regions of the mesh closely approximate great circle arcs. We go on to show that the method path-plans efficiently in environments with complex interacting conditions.

AAAI Conference 2017 Conference Paper

Deterministic versus Probabilistic Methods for Searching for an Evasive Target

• Sara Bernardini
• Maria Fox
• Derek Long
• Chiara Piacentini

Several advanced applications of autonomous aerial vehicles in civilian and military contexts involve a searching agent with imperfect sensors that seeks to locate a mobile target in a given region. Effectively managing uncertainty is key to solving the related search problem, which is why all methods devised so far hinge on a probabilistic formulation of the problem and solve it through branch-and-bound algorithms, Bayesian filtering or POMDP solvers. In this paper, we consider a class of hard search tasks involving a target that exhibits an intentional evasive behaviour and moves over a large geographical area, i. e. , a target that is particularly difficult to track down and uncertain to locate. We show that, even for such a complex problem, it is advantageous to compile its probabilistic structure into a deterministic model and use standard deterministic solvers to find solutions. In particular, we formulate the search problem for our uncooperative target both as a deterministic automated planning task and as a constraint programming task and show that in both cases our solution outperforms POMDPs methods.

AAAI Conference 2016 Conference Paper

Heuristic Planning for Hybrid Systems

• Wiktor Piotrowski
• Maria Fox
• Derek Long
• Daniele Magazzeni
• Fabio Mercorio

Planning in hybrid systems has been gaining research interest in the Artificial Intelligence community in recent years. Hybrid systems allow for a more accurate representation of real world problems, though solving them is very challenging due to complex system dynamics and a large model feature set. We developed DiNo, a new planner designed to tackle problems set in hybrid domains. DiNo is based on the discretise and validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic.

IJCAI Conference 2016 Conference Paper

Heuristic Planning for PDDL+ Domains

• Wiktor Piotrowski
• Maria Fox
• Derek Long
• Daniele Magazzeni
• Fabio Mercorio

Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of complex system dynamics, including non-linear processes and events. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamcs governing the continuous evolution of states. DiNo is based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper. Although several planners have been developed to work with subsets of PDDL+ features, or restricted forms of processes, DiNo is currently the only heuristic planner capable of handling non-linear system dynamics combined with the full PDDL+ feature set.

AIJ Journal 2015 Journal Article

An extension of metric temporal planning with application to AC voltage control

• Chiara Piacentini
• Varvara Alimisis
• Maria Fox
• Derek Long

In this paper we explore the deployment of planning techniques to solve a new class of metric temporal planning problems, characterised by the need to manage both plan trajectory constraints and uncontrollable numeric events. This combination gives rise to challenges not previously solved in state-of-the-art planners. We introduce new planning methods to handle these challenges, and demonstrate our approach using a real application domain: voltage control in Alternating Current (AC) electrical networks. Embedding electricity networks in a domain description presents important modelling challenges. We introduce an encapsulated type, Network, the implementation of which is hidden from the planner. The effects of actions trigger complex updates to the state of the network. We distinguish between the direct effects of planned actions, and the indirect effects triggered by them, and we propose a method for integrating a specialised external AC power equation solver with a planner. We consider a new heuristic function that takes into account the next uncontrollable event, and its interaction with active trajectory constraints, when determining the actions that are helpful in a state. This lookahead heuristic also exploits an abstraction of the encapsulated Network type to obtain more informative distance estimates. We conduct experiments to evaluate the benefits of the lookahead heuristic, showing that our approach scales very well with the size of the network and the number of controllable components of the network.

AAAI Conference 2015 Conference Paper

Planning with Numeric Timed Initial Fluents

• Chiara Piacentini
• Maria Fox
• Derek Long

Numeric Timed Initial Fluents represent a new feature in PDDL that extends the concept of Timed Initial Literals to numeric fluents. They are particularly useful to model independent functions that change through time and influence the actions to be applied. Although they are very useful to model real world problems, they are not systematically defined in the family of PDDL languages and they are not implemented in any generic PDDL planner, except for POPF2 and UPMurphi. In this paper we present an extension of the planner POPF2 (POPF-TIF) to handle problems with numeric Timed Initial Fluents. We propose and evaluate two contributions: the first is based on improvements of the heuristic evaluation, while the second considers alternative search algorithms based on a mixture of Enforced Hill Climbing and Best First Search.

IJCAI Conference 2015 Conference Paper

Temporal Planning with Semantic Attachment of Non-Linear Monotonic Continuous Behaviours

• Josef Bajada
• Maria Fox
• Derek Long

Non-linear continuous change is common in realworld problems, especially those that model physical systems. We present an algorithm which builds upon existent temporal planning techniques based on linear programming to approximate non-linear continuous monotonic functions. These are integrated through a semantic attachment mechanism, allowing external libraries or functions that are difficult to model in native PDDL to be evaluated during the planning process. A new planning system implementing this algorithm was developed and evaluated. Results show that the addition of this algorithm to the planning process can enable it to solve a broader set of planning problems.

AAAI Conference 2014 Conference Paper

Symbolic Domain Predictive Control

• Johannes Löhr
• Martin Wehrle
• Maria Fox
• Bernhard Nebel

Planning-based methods to guide switched hybrid systems from an initial state into a desired goal region opens an interesting field for control. The idea of the Domain Predictive Control (DPC) approach is to generate input signals affecting both the numerical states and the modes of the system by stringing together atomic actions to a logically consistent plan. However, the existing DPC approach is restricted in the sense that a discrete and pre-defined input signal is required for each action. In this paper, we extend the approach to deal with symbolic states. This allows for the propagation of reachable regions of the state space emerging from actions with inputs that can be arbitrarily chosen within specified input bounds. This symbolic extension enables the applicability of DPC to systems with bounded inputs sets and increases its robustness due to the implicitly reduced search space. Moreover, precise numeric goal states instead of goal regions become reachable.

IJCAI Conference 2011 Conference Paper

Automatic Construction of Efficient Multiple Battery Usage Policies

• Maria Fox
• Derek Long
• Daniele Magazzeni

There is a huge and growing number of systems that depend on batteries for power supply, ranging from small mobile devices to large high-powered systems such as electrical substations. In most of these systems, there are significant user-benefits or engineering reasons to base the supply on multiple batteries, with load being switched between batteries by a control system. The key to efficient use of multiple batteries lies in the design of effective policies for the management of the switching of load between them. This paper describes work in which we show that automated planning can produce much more effective policies than other approaches to multiple battery load management in the literature.

IJCAI Conference 2009 Conference Paper

• Amanda Coles
• Andrew Coles
• Maria Fox
• Derek Long

We consider the problem of planning in domains with continuous linear numeric change. Such change cannot always be adequately modelled by discretisation and is a key facet of many interesting problems. We show how a forward-chaining temporal planner can be extended to reason with actions with continuous linear effects. We extend a temporal planner to handle numeric values using linear programming. We show how linear continuous change can be integrated into the same linear program and we discuss how a temporal-numeric heuristic can be used to provide the search guidance necessary to underpin continuous planning. We present results to show that the approach can effectively handle duration-dependent change and numeric variables subject to continuous linear change.

AIJ Journal 2009 Journal Article

Advances in automated plan generation

• Maria Fox
• Sylvie Thiébaux

AIJ Journal 2009 Journal Article

Managing concurrency in temporal planning using planner-scheduler interaction

• Andrew Coles
• Maria Fox
• Keith Halsey
• Derek Long
• Amanda Smith

Metric temporal planning involves both selecting and organising actions to satisfy the goals and also assigning to each of these actions its start time and, where necessary, its duration. The assignment of start times to actions is a central concern of scheduling. In pddl2. 1, the widely adopted planning domain description language standard, metric temporal planning problems are described using actions with durations. A large number of planners have been developed to handle this language, but the great majority of them are fundamentally limited in the class of temporal problems they can solve. In this paper, we review the source of this limitation and present an approach to metric temporal planning that is not so restricted. Our approach links planning and scheduling algorithms into a planner, Crikey, that can successfully tackle a wide range of temporal problems. We show how Crikey can be simplified to solve a wide and interesting subset of metric temporal problems, while remaining competitive with other temporal planners that are unable to handle required concurrency. We provide empirical data comparing the performance of this planner, Crikey SHE, our original version, Crikey, and a range of other modern temporal planners. Our contribution is to describe the first competitive planner capable of solving problems that require concurrent actions.

AAAI Conference 2007 Conference Paper

Detecting Execution Failures Using Learned Action Models

• Maria Fox

Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present an approach by which a trace of the execution of a behaviour is monitored by tracking its most likely explanation through a learned model of how the behaviour is normally executed. In this way, possible failures are identified as deviations from common patterns of the execution of the behaviour. We perform an experiment in which we inject errors into the behaviour of a robot performing a particular task, and explore how well a learned model of the task can detect where these errors occur.

AAAI Conference 2007 Conference Paper

Discovering Near Symmetry in Graphs

• Maria Fox

Symmetry is a widespread phenomenon that can offer opportunities for powerful exploitation in areas as diverse as molecular chemistry, pure mathematics, circuit design, biology and architecture. Graphs are an abstract way to represent relational structures. The search for symmetry in many contexts can thus be reduced to the attempt to find graph automorphisms. Brendan McKay’s NAUTY system (McKay 1990) is an example of one of the highly successful products of research into this task. Erdős and Rényi showed that almost all large graphs are asymmetric, but it is readily observed that many graphs representing structures of real interest contain symmetry. Even more graphs are nearly symmetric, in the sense that to each graph there is a closely similar graph that is symmetric. In this paper we explore the problem of finding near symmetries in graphs and describe the techniques we are developing for performing this task.

AAAI Conference 2006 Conference Paper

Exploration of the Robustness of Plans

• Maria Fox

This paper considers the problem of stochastic robustness testing for plans. As many authors have observed, unforeseen execution-time variations, both in the effects of actions and in the times at which they occur, can result in a plan failing to execute correctly even when it is valid with respect to a domain model. In this paper we contrast the validation of a plan with respect to a domain model, confirming soundness, with the validation with respect to an execution model, which we call robustness. We describe a Monte Carlo probing strategy that takes a hypothesis testing approach to confirming the robustness of a plan. An important contribution of the work is that we draw links between the robustness of plans and the notion of the “fuzzy” robustness of traces through timed hybrid automata, introduced by Gupta et al. We show that robustness depends on the metric used to define the set of plans that are probed, and that the most appropriate metric depends on the capabilities of the executive and the way in which it will interpret and execute the plan.

AIJ Journal 2006 Journal Article

Robot introspection through learned hidden Markov models

• Maria Fox
• Malik Ghallab
• Guillaume Infantes
• Derek Long

In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behavioural models to provide a robot with an introspective capability. We assume that the behaviour of a robot in achieving a task can be modelled as a finite stochastic state transition system. Beginning with data recorded by a robot in the execution of a task, we use unsupervised learning techniques to estimate a hidden Markov model (HMM) that can be used both for predicting and explaining the behaviour of the robot in subsequent executions of the task. We demonstrate that it is feasible to automate the entire process of learning a high quality HMM from the data recorded by the robot during execution of its task. The learned HMM can be used both for monitoring and controlling the behaviour of the robot. The ultimate purpose of our work is to learn models for the full set of tasks associated with a given problem domain, and to integrate these models with a generative task planner. We want to show that these models can be used successfully in controlling the execution of a plan. However, this paper does not develop the planning and control aspects of our work, focussing instead on the learning methodology and the evaluation of a learned model. The essential property of the models we seek to construct is that the most probable trajectory through a model, given the observations made by the robot, accurately diagnoses, or explains, the behaviour that the robot actually performed when making these observations. In the work reported here we consider a navigation task. We explain the learning process, the experimental setup and the structure of the resulting learned behavioural models. We then evaluate the extent to which explanations proposed by the learned models accord with a human observer's interpretation of the behaviour exhibited by the robot in its execution of the task.

IJCAI Conference 2005 Conference Paper

Abstraction-based Action Ordering in Planning

• Maria Fox
• Derek Long
• Julie

Many planning problems contain collections of symmetric objects, actions and structures which render them difficult to solve efficiently. It has been shown that the detection and exploitation of symmetric structure in planning problems can dramatically reduce the size of the search space and the time taken to find a solution. We present the idea of using an abstraction of the problem domain to reveal symmetric structure and guide the navigation of the search space. We show that this is effective even in domains in which there is little accessible symmetric structure available for pruning. Proactive exploitation represents a flexible and powerful alternative to the symmetry-breaking strategies exploited in earlier work in planning and CSPs. The notion of almost symmetry is defined and results are presented showing that proactive exploitation of almost symmetry can improve the performance of a heuristic forward search planner.

AAAI Conference 2005 Conference Paper

Validating Plans in the Context of Processes and Exogenous Events

• Maria Fox

Complex planning domains push the boundaries of the expressive power of planning domain modelling languages. Recent extensions to the standard planning languages have included expressions for temporal, metric and resource structures. Other work has also considered how process models can be incorporated into domain models. In this paper we consider the problem of expressing and validating models containing events which are triggered as a consequence of the action of physical processes. We focus, primarily, on the validation of plans in the context of exogenous events, discussing the modelling, semantic and implementation issues that arise. Events impact not only on plans but on domain models as a whole and we also consider the problems that arise in considering the validation of event structures in domain models.

IJCAI Conference 1999 Conference Paper

The Detection and Exploitation of Symmetry in Planning Problems

• Maria Fox
• Derek Long

Many planning problems exhibit a high degree of symmetry that cannot yet be exploited successfully by modern planning technology. For example, problems in the Gripper domain, in which a robot with two grippers must transfer balls from one room to another, are trivial to the human problem-solver because the high degree of symmetry in the domain means that the order in which pairs of balls are transported is irrelevant to the length of the shortest transportation plan. However, planners typically search all possible orderings giving rise to an exponential explosion of the search space. This paper describes a way of detecting and exploiting symmetry in the solution of problems that demonstrate these characteristics. We have implemented our techniques in STAN, a Graphplan-based planner that uses state analysis techniques in a number of ways to exploit the underlying structures of domains. We have achieved a dramatic improvement in performance in solving problems exhibiting symmetry. We present a range of results and indicate the further developments we are now pursuing.