Symmetries in planning problems

Symmetries arise in planning in a variety of ways. This paper describes the ways that symmetry aises most naturally in planning problems and reviews the approaches that have been applied to exploitation of symmetry in order to reduce search for plans. It then introduces some extensions to the use of symmetry in planning before moving on to consider how the exploitation of symmetry in planning might be generalised to offer new approaches to exploitation of symmetry in other combinatorial search problems

STAN4 : a hybrid planning strategy based on subproblem abstraction

Planning domains often feature subproblems such as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Using two such subsolvers our hybrid planner, stan4, participated successfully in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning competition

Utilizing automatically inferred invariants in graph construction and search

In this paper we explore the relative importance of persistent and non-persistent mutex relations in the performance of Graphplan- based planners. We also show the advantages of pre-compiling persistent mutex relations. Using TIM we are able to generate, during a pre-processing analysis, all of the persistent binary mutex relations that would be inferred by Graphplan during each graph construction. We show how the efficient storgae of, and access to, these pre-processed persistent mutexes yields a modest improvement in graph construction performance. We further demonstrate that the process by which these persistent mutexes are identified can, in certain kinds of domain, allow the exploitation of binary mutex relations which are inaccessible to Graphplan. We present The Island of Sodor, a simple planning domain characterizing a class of domains in which certain persistent mutexes are present but are not detectable by Graphplan during graph construction. We show that the exploitation of these hidden binary mutexes makes problems in this kind of domain trivially solvable by STAN, where they are intractable for other Graphplan-based planners

Automatic synthesis and use of generic types in planning

This work is concerned with the automatic inference of generic types from STRIPS planning domain descriptions. Generic types are higher order types allowing the partition of domains (and components of domains) into different domain classes, including the commonly occurring transportation domains class. We show how the generic type structure of domains can be exploited to increase planner efficiency. We have focussed so far on the generic types of typical of transportation domains, but instead to go on to characterise, and identify examples of, other domain classes such as construction domains. One of the most interesting properties of the work described here is that domains which would not be recognised, by the human, as transportation domains can turn out to have an underlying transportation character which can be exploited by the application of heuristics suited to standard transportation domains. We illustrate this by considering both standard transportation domains (such as Logistics) and non-standard ones (the PaintWall domains presented in this paper). The analyses described here are completely planner-independent and contribute to an increasing collection of pre-planning analysis tools which help to increase performance of planners by decomposing and understanding the structures of planning problems before planners are applied

Validating plans with continuous effects

A critical element in the use of PDDL2.1, the modelling language developed for the International Planning Competition series, has been the common understanding of the semantics of the language. The fact that this has been implemented in plan validation software was vital to the progress of the competition. However, the validation of plans using actions with continuous effects presents new challenges (that precede the challenges presented by planning with those effects). In this paper we review the need for continuous effects, their semantics and the problems that arise in validation of plans that include them. We report our progress in implementing the semantics in an extended version of the plan validation software

The International planning competition series and empirical evaluation of AI planning systems

In this paper we consider the role of the International Planning Competition series in the evaluation of planners, both directly through the events themselves, and indirectly through the creation of resources and infrastructure. We also consider the problem of evaluation based on data collected both in the competitions and otherwise and examine some of the issues that arise in attempting to formulate and test hypotheses around the data

Plan validation and mixed-initiative planning in space operations

Bringing artificial intelligence planning and scheduling applications into the real world is a hard task that is receiving more attention every day by researchers and practitioners from many fields. In many cases, it requires the integration of several underlying techniques like planning, scheduling, constraint satisfaction, mixed-initiative planning and scheduling, temporal reasoning, knowledge representation, formal models and languages, and technological issues. Most papers included in this book are clear examples on how to integrate several of these techniques. Furthermore, the book also covers many interesting approaches in application areas ranging from industrial job shop to electronic tourism, environmental problems, virtual teaching or space missions. This book also provides powerful techniques that allow to build fully deployable applications to solve real problems and an updated review of many of the most interesting areas of application of these technologies, showing how powerful these technologies are to overcome the expresiveness and efficiency problems of real world problems

Explainable Planning

As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems cooperate in problem-solving, but also it is a necessary step in the process of building trust as humans migrate greater responsibility to such systems. The challenge is to find effective ways to communicate the foundations of AI-driven behaviour, when the algorithms that drive it are far from transparent to humans. In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and common basis for communication with users, while acknowledging the gap between planning algorithms and human problem-solving.Comment: Presented at the IJCAI-17 workshop on Explainable AI (http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/). Melbourne, August 201

Detecting execution failures using learned action models

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

Modelling Mixed Discrete-Continuous Domains for Planning

In this paper we present pddl+, a planning domain description language for modelling mixed discrete-continuous planning domains. We describe the syntax and modelling style of pddl+, showing that the language makes convenient the modelling of complex time-dependent effects. We provide a formal semantics for pddl+ by mapping planning instances into constructs of hybrid automata. Using the syntax of HAs as our semantic model we construct a semantic mapping to labelled transition systems to complete the formal interpretation of pddl+ planning instances. An advantage of building a mapping from pddl+ to HA theory is that it forms a bridge between the Planning and Real Time Systems research communities. One consequence is that we can expect to make use of some of the theoretical properties of HAs. For example, for a restricted class of HAs the Reachability problem (which is equivalent to Plan Existence) is decidable. pddl+ provides an alternative to the continuous durative action model of pddl2.1, adding a more flexible and robust model of time-dependent behaviour