Showing papers by "Derek Long published in 2005"

Validating plans in the context of processes and exogenous events

Abstract: 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.

Exploration of the Robustness of Plans

Abstract: 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.

MADbot: a motivated and goal directed robot

Abstract: In most work in plan generation and execution the assumption has been made that the goals being addressed by the planning system (and executive) are imposed externally and that once a plan has been constructed to achieve these goals the activity of the planner can cease. Similarly, once the plan has been successfully executed and a state satisfying the externally imposed goals has been reached, it has been assumed that the planning and execution behaviours will suspend until a new goal set and consequent plan has been imposed. These assumptions do not hold for fully autonomous systems, which are capable of directing their own behaviour and prioritising their own goals. The problem we are most concerned with is determining how goals arise during the autonomous behaviour of a system. Recent work by Knight et al (Knight et al. 2001) and Chien et al (Chien et al. 2001) relaxed the above assumptions by introducing the idea of continuous planning in the Casper system. Casper is an architecture for an autonomous planning and control system intended for application in space missions involving onboard autonomy. In (Chien et al. 2001), the authors indicate that the system can respond to failures and opportunities during execution of a plan within a given horizon and, in principle, update its goal set in response to environmental factors. The RAX system (Jonsson et al. 2000) demonstrated the integration of planning, execution, failure diagnosis and subsequent plan repair. Our approach to autonomous planning and execution is to address the issues of goal generation and management according to the changing motivations of an autonomous system. Although in general an autonomous system will be created to carry out externally-imposed tasks, any extended period of autonomous behaviour will require a system to react to its environment and to be able to create its own goals. The MADbot project concerns the development of a motivated autonomous system, capable of generating its own goals in accordance with a system of drives and impulses (Coddington & Luck 2004) and monitoring its own behaviour in the execution of plans. Execution monitoring is done with respect to stochastic models of the actions that the robot can execute. At any point during the execution of its plan the robot can estimate its most likely state based on the appropriate model and the current observations of the system. If

Abstraction-based action ordering in planning

Abstract: 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.

Time in planning

Abstract: This collection represents the primary reference work for researchers and students in the area of Temporal Reasoning in Artificial Intelligence. Temporal reasoning has a vital role to play in many areas, particularly Artificial Intelligence. Yet, until now, there has been no single volume collecting together the breadth of work in this area. This collection brings together the leading researchers in a range of relevant areas and provides an coherent description of the breadth of activity concerning temporal reasoning in the field of Artificial Intelligence.

Plan validation and mixed-initiative planning in space operations

Abstract: 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.