Showing papers by "Derek Long published in 2006"

Plan Constraints and Preferences in PDDL3

Abstract: We propose an extension to the PDDL language, called PDDL3.0, that aims at a better characterization of plan quality by allowing the user to express strong and soft constraints about the structure of the desired plans, as well as strong and soft problem goals. PDDL3.0 was the reference language of the 5th International Planning competition (IPC-5). This paper contains most of the document about PDDL3.0 that was discussed by the Consulting Committee of IPC-5, and then distributed to the IPC-5 competitors. Introduction The notion of plan quality in automated planning is a practically very important issue. In many real-world planning domains, we have to address problems with a large set of solutions, or with a set of goals that cannot all be achieved. In these problems, it is important to generate plans of good or optimal quality achieving all problem goals (if possible) or some subset of them. In the previous International planning competitions, the plan generation CPU-time played a central role in the evaluation of the competing planners. In the fifth International planning competition (IPC-5), while considering the CPUtime, we would like to give greater emphasis to the importance of plan quality. The versions of PDDL used in the previous two competitions (PDDL2.1 and PDDL2.2) allow us to express some criteria for plan quality, such as the number of plan actions or parallel steps, and relatively complex plan metrics involving plan makespan and numerical quantities. These are powerful and expressive in domains that include metric fluents, but plan quality can still only be measured by plan size in the case of propositional planning. We believe that these criteria are insufficient, and we propose to extend PDDL with new constructs increasing its expressive power about the plan quality specification. The proposed extended language allows us to express strong and soft constraints on plan trajectories (i.e. constraints over possible actions in the plan and intermediate states reached by the plan), as well as strong and soft problem goals (i.e. goals that must be achieved in any valid plan, and goals that we desire to achieve, but that do not have to be necessarily achieved). Strong constraints and goals must be satisfied by any valid plan, while soft constraints and goals express desired constraints and goals, some of which may be more preferred than others. Informally, in planning with soft constraints and goals, the best quality plan should satisfy “as much as possible” the soft constraints and goals according to the specified preference relation distinguishing alternative feasible plans (satisfying all strong constraints and goals). While soft constraints have been extensively studied in the CSP literature, only very recently has the planning community started to investigate them (Brafman & Chernyavsky 2005; Briel et al. 2004; Delgrande, Schaub, & Tompits 2005; Miguel, Jarvis, & Shen 2001; Smith 2004; Son & Pontelli 2004), and we believe that they deserve more research efforts. The following are some informal examples of plan trajectory constraints and soft goals. Additional formal examples will be given in the next section. Examples in a blocksworld domain: a fragile block can never have something above it, or it can have at most one block on it; we would like that the blocks forming the same tower always have the same colour; in some state of the plan, all blocks should be on the table. Examples in a transportation domain: we would like that every airplane is used (instead of using only a few airplanes, because it is better to distribute the workload among the available resources and limit heavy usage); whenever a ship is ready at a port to load the containers it has to transport, all such containers should be ready at that port; we would like that at the end of the plan all trucks are clean and at their source location; we would like no truck to visit any destination more than once. When we have soft constraints and goals, it can be useful to give different priorities to them, and this should be taken into account in the plan quality evaluation. While there is more than one way to specify the importance of a soft constraint or goal, as a first attempt to tackle this issue, for IPC5 we have chosen a simple quantitative approach: each soft constraint and goal is associated with a numerical weight representing the cost of its violation in a plan (and hence also its relative importance with respect the other specified soft constraints and goals). Weighted soft constraints and goals are part of the plan metric expression, and the best quality plans are those optimising such an expression (more details are given in the next sections). Using this approach we can express that certain plans are more preferred than others. Some examples are (other formalised examples are given in the next sections):1 I prefer a plan where every airplane is used, rather than a plan using 100 units of fuel less, which could be expressed by weighting a failure to use all the planes by a number 100 times bigger than the weight associated with the fuel use in the plan metric; I prefer a plan where each city is visited at most once, rather than a plan with a shorter makespan, which could be expressed by using constraint violation costs penalising a failure to visit each city at most once very heavily; I prefer a plan where at the end each truck is at its start location, rather than a plan where every city is visited by at most one truck, which could be expressed by using goal costs penalising a goal failure of having every truck at its start location more heavily than a failure of having in the plan every city visited by at most one truck. We also observe that the rich additional expressive power we propose to add for goal specifications allows the expression of constraints that are actually derivable necessary properties of optimal plans. By adding them as goal conditions, we have a way to express constraints that we know will lead to the planner finding optimal plans. Similarly, one can express constraints that prevent a planner from exploring parts of the plan space that are known to lead to inefficient performance. In the next sections, we outline some extensions to PDDL2.2 that we propose for IPC-5. We call the extended language PDDL3.0. It should be noted that this is a preliminary version of the extended language, and that a more detailed description will be prepared in the future. Moreover, given that the proposed extensions are relatively new in the planning community, and that the teams participating in IPC-5 will have limited time to develop their systems, we impose some simplifying restrictions to make the language more accessible. State Trajectory Constraints Syntax and Intended Meaning State trajectory constraints assert conditions that must be met by the entire sequence of states visited during the execution of a plan. They are expressed through temporal modal operators over first order formulae involving state predicates. We recognise that there would be value in also allowing propositions asserting the occurrence of action instances in a plan, rather than simply describing properties of the states visited during execution of the plan, but we choose to restrict ourselves to state predicates in this extension of the language. The use of the extensions described here imply a new requirements flag, :constraints. The basic modal operators we propose to use in IPC-5 are: always, sometime, at-most-once, and atend (for goal state conditions). We use a special default assumption that unadorned conditions in the goal specification are automatically taken to be “at end” conditions. This assumption The benchmark domains and problems of IPC-5 contain many additional examples; some samples of them are described in (Gerevini & Long 2006). is made in order to preserve the standard meaning for existing goal specifications, despite the fact that in a standard semantics for an LTL formula an unadorned proposition would be interpreted according to the current state. We add within which can be used to express deadlines. In addition, rather than allowing arbitrary nesting of modal operators, we introduce some specific operators that offer some limited nesting. We have sometime-before, sometime-after, always-within. Other modalities could be added, but we believe that these are sufficiently powerful for an initial level of the sublanguage modelling constraints. It should be noted that, by combining these modalities with timed initial literals (defined in PDDL2.2), we can express further goal constraints. In particular, one can specify the interval of time when a goal should hold, or the lower bound on the time when it should hold. Since these are interesting and useful constraints, we introduce two modal operators as “syntactic sugar” of the basic language: hold-during and hold-after. Trajectory constraints are specified in the planning problem file in a new field, called :constraints that will usually appear after the goal. In addition, we allow constraints to be specified in the action domain file on the grounds that some constraints might be seen as safety conditions, or operating conditions, that are not physical limitations, but are nevertheless constraints that must always be respected in any valid plan for the domain (say legal constraints or operating procedures that must be respected). This also uses a section labelled (:constraints ...). The interpretation of (:constraints ...) in the conjunction of a domain and a problem file is that it is equivalent to having all the constraints added to the goals. The use of trajectory constraints (in the domain file or in the goal specification) implies the need for the :constraints flag in the :requirements list. Note that no temporal modal operator is allowed in preconditions of actions. That is, all action preconditions are with respect to a state (or time interval, in the case of overall action c

Modelling mixed discrete-continuous domains for planning

Abstract: 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.

Plan stability: replanning versus plan repair

Abstract: The ultimate objective in planning is to construct plans for execution. However, when a plan is executed in a real environment it can encounter differences between the expected and actual context of execution. These differences can manifest as divergences between the expected and observed states of the world, or as a change in the goals to be achieved by the plan. In both cases, the old plan must be replaced with a new one. In replacing the plan an important consideration is plan stability. We compare two alternative strategies for achieving the stable repair of a plan: one is simply to replan from scratch and the other is to adapt the existing plan to the new context. We present arguments to support the claim that plan stability is a valuable property. We then propose an implementation, based on LPG, of a plan repair strategy that adapts a plan to its new context. We demonstrate empirically that our plan repair strategy achieves more stability than replanning and can produce repaired plans more efficiently than replanning.

Preferences and soft constraints in PDDL3

Abstract: The notion of plan qualityin automated planning is a practically very important issue. In many real-world planning domains, we have to address problems with a large set of solutions, or with a set of goals that cannot all be achieved. In these problems, it is important to generate plans of good or optimal quality achieving all problem goals (if possible) or some subset of them. In the previous International planning competitions, the plan generation CPU-time played a central role in the evaluation of the competing planners. In the fifth International planning competition (IPC-5), while considering the CPU-time, we would like to give greater emphasis to the importance of plan quality. The versions of PDDL used in the previous two competitions (PDDL2.1 and PDDL2.2) allow us to express some criteria for plan quality, such as the number of plan actions or parallel steps, and relatively complex plan metrics involving plan makespan and numerical quantities. These are powerful and expressive in domains that include metric fluents, but plan quality can still only be measured by plan size in the case of propositional planning. We believe that these criteria are insufficient, and we propose to extend PDDL with new constructs increasing its expressive power about the plan quality specification.

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.

On-board timeline validation and repair : a feasibility study

Abstract: We report on the progress and outcome of a recent ESAfunded project (MMOPS) designed to explore the feasibility of on-board reasoning about payload timelines. The project sought to examine the role of on-board timeline reasoning and the operational context into which it would fit. We framed a specification for an on-board service that fits with existing practices and represents a plausible advance within sensible constraints on the progress of operations planning. We have implemented a prototype to demonstrate the feasibility of such a system and have used it to show how science gathering operations might be improved by its deployment.

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

Abstract: 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.

Using learned action models in execution monitoring

Abstract: 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 further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour.

Motivation, Planning and Interaction

Abstract: This chapter deals with three key issues related to designing and building intelligent agents. Firstly, determination of aims and objectives at any given time of an intelligent agent, followed by planning a series of actions to achieve these aims and objectives. And finally agent's interaction with humans and other software agents to complete the objectives. The above issues are discussed under the heading of “motivation,” “planning” and “interaction.” Motivation as an area of study is important in understanding human and animal behavior. Also, from a computer science perspective, it could offer a substantially higher level of control than is currently available. Furthermore, planning is a sub-field of AI research which plays a vital role in the development of intelligent autonomous systems and in many application areas of cognitive systems. This chapter further classifies interactions into three types. The first is machine-machine interactions, including networking and information understanding. The second is people-machine interaction, often referred to as Human Computer Interaction (HCI). Finally, impacts of technology on person-to-person communication.

On-Board Planning and Scheduling for the ExoMars Mission

Abstract: Deep space exploration with surface based robotic elements presents a number of significant challenges, not least of which is the lack of real-time, high bandwidth communication between a surface element and mission control. The lack of timely data combined with the unstructured and potentially hazardous nature of the terrain creates a difficult mission planning environment. Given the paucity of up-to-date data it is difficult to construct robust timelines at the task level. As a consequence such missions can be characterised by recurrent safe-modes, significant downtime and reduced science return. Lag in the communications cycle exacerbates this problem by increasing operational workload and delaying return to active science operations. It is worth noting that even in mid 2005 with a reduced and mature approach for the NASA MER rovers, operations costs are in the region of $2.5M/month. A conservative planning approach is often adopted to improve timeline robustness but inevitably science return is lower as a result. The use of greater on-board autonomy is therefore a critical consideration for such a mission. In particular it is essential to determine just what level of autonomy is appropriate, whether a suitably mature technology implementation exists and to validate these conclusions in a representative way. The current suite of Packet Utilisation Services (PUS) as defined in ECCS-E-70-41 [1] provides an appropriate starting point for the development of an operational autonomy concept for the forthcoming ExoMars mission. Although PUS services such as event monitoring, event action coupling and OBCP’s provide the backbone implementation for FDIR and operational autonomy concept, additional functionality is required to meet the requirements of deep space operations. In particular there is a need to accommodate timeline management from a global perspective in response to variability (during execution) and uncertainty (at mission planning time) with regard to on-board resources such as memory and power. For example the actual power or memory used during payload activity will often be different from that predicted by mission planners. In the pessimistic case this will halt the timeline as there will be insufficient resources to carry out the planned tasks which remain. Alternatively execution may result in a relative abundance of resources if estimates were overly conservative. These could of course be used to carry out additional science. Greater on-board autonomy, which extends the level currently supported by existing PUS services, has been proposed as a means of alleviating this inefficiency. Autonomy in this sense is defined as having software element which can modify or create a rover timeline. The motivation being that an on-board element, with access to actual on-board state is

Developing an autonomous timeline management capability for a robotic mars mission

Abstract: Prsentation on the development of an autonomous timeline management capability for a robotic mars mission.

MMOPS: Assessing the Impact of On-Board Autonomy for Planetary Exploration Missions

Abstract: On-board autonomy has been proposed as a means of alleviating some of the inefficiencies associated with remote operations for planetary exploration missions. The ongoing development of ESA’s ExoMars programme has identified the need for both the provision of a practical approach to on-board autonomy and an assessment of its capability. This paper reports on the development of an advanced, autonomous timeline management prototype based on AI planning and scheduling technology and its assessment using a dedicated framework built from major elements of the Beagle 2 mission such as the on-board software and mission control system. The paper outlines a practical strategy for the deployment of on-board autonomy which is both compatible with existing operational needs andupwardly compatible with future developments. The assessment concludes that the approach significantly improves the payload utilization on an ExoMars like mission and is sufficiently mature to proceed to the next stage i.e. development of a version which is executable on flight representative hardware.

Learning macro-actions genetically from plans

Abstract: Despite recent progress in planning, many complex domains and even larger problems in simple domains remain hard and challenging. One way to achieve further improvement is to utilise knowledge acquired for the planner from the domain. Macro-actions are a promising means by which to convey such knowledge. A macro-action, or macro in short, is a group of actions selected for application as a single choice. Most existing works on macros utilise knowledge explicitly specific to the planners and the domains. But presumably any particular properties are not likely to be common with different planners or wider range of domains. Therefore, a macro learning system that does not exploit any structural knowledge about planners and domains explicitly is of immense interest. This paper presents an offline system capable of learning macros genetically from plans. Given a planner, a domain, and necessary problems, our system generates macros, lifting from plans of some problems, under guidance from a genetic algorithm. It represents macros like regular actions, evaluates them individually by solving other problems, and suggests the best macro to be added to the domain permanently. Genetic algorithms are automatic learning methods that can learn properties of a system using no explicit knowledge about it. Our system thus does not strive to discover or utilise any structural properties specific to a planner or a domain.