1,721,093 research outputs found
Numerical Kernels for Monitoring and Repairing Plans Involving Continuous and Consumable Resources
No full textPlan Repair for Resource Constrained Tasks via Numeric Macro Actions
No full textThe paper addresses the problem of plan repair for tasks involving mandatory constraints on consumable and continuous resources, modeled as numeric fluents. The approach starts by proposing a new notion of numeric macro actions allowing to handle - as an extension to the classical macro action formulation - conditions and operations not only on the propositional fragment, but also on the numeric one. By reasoning directly on the current plan, the paper shows two techniques for selecting useful macro actions. Such macro actions, toghether with the original actions model, are then used by an off-the-shelf numeric planner for a faster plan repair task. To evaluate the techniques and the contribution of numeric macro actions, we experimented the approach on several numeric planning domains using Metric-FF as off-the-shelf planner.Results show that both strategies enhance the performance of the same planning system without macro actions. Even, one of the strategies turns out to be very competitive also with the specialized plan repair system LPG-ADAPT, both in terms of cpu-time, and stability of the repaired plan
Numeric Kernel for Reasoning about Plans Involving Numeric Fluents
Full text linkThe paper proposes the notion of numeric kernel as a means for reasoning about plans involving numeric state variables, i.e. numeric fluents. A numeric kernel identifies the sufficient and necessary conditions that allow to directly - without any search and any propagation - assess whether a plan is valid in a specific world state. The notion generalizes the propositional kernels defined for the STRIPS language, to support domains involving numeric information as well. A regression method to build such kernels is reported, and its correctness is theoretically proved. To evaluate the numeric kernels contribution, we report two possible repair strategies that can be employed as a direct application of the numeric kernel properties. Results show the promise of the approach both from the computational point of view and in terms of plan quality
CPCES: A planning framework to solve conformant planning problems through a counterexample guided refinement
No full textReCon: An Online Task ReConfiguration Approach for Robust Plan Execution
No full textThe paper presents an approach for the robust plan execution in presence of consumable and continuous resources. Plan execution is a critical activity since a number of unexpected situations could prevent the feasibility of tasks to be accomplished; however, many robotic scenarios (e.g. in space exploration) disallow robotic systems to perform significant deviations from the original plan formulation. In order to both (i) preserve the “stability” of the current plan and (ii) provide the system with a reasonable level of autonomy in handling unexpected situations, an innovative approach based on task reconfiguration is presented. Exploiting an enriched action formulation grounding on the notion of execution modalities, ReCon replaces the replanning mechanism with a novel reconfiguration mechanism, handled by means of a CSP solver. The paper studies the system for a typical planetary rover mission and provides a rich experimental analysis showing that, when the anomalies refer to unexpected resources consumption, the reconfiguration is not only more efficient but also more effective than a plan adaptation mechanism. The experiments are performed by evaluating the recovery performances depending on constraints on computational costs
BLAST:Bit-Blasting Numbers for Classical Planning (Extended Abstract)
Full text linkIt is well known that numeric planning can be made decidable if the domain of all numeric state variables is finite. This bounded formulation can be polynomially compiled into classical planning with Boolean conditions and conditional effects preserving the plan size exactly. However, it remains unclear whether this compilation has any practical utility. To explore this aspect, this work revisits the theoretical compilation framework from a practical perspective, focusing on the fragment of simple numeric planning. Specifically, we introduce three different compilations. The first, called one-hot, aims to systematise the current practice among planning practitioners of modelling numeric planning through classical planning. The other two, termed binary compilations, extend and specialise the logarithmic encoding introduced in previous literature. Our experimental analysis reveals that the overly complex logarithmic encoding can, surprisingly, be made practical with some representational expedients. Among these, the use of axioms is particularly crucial. Furthermore, we identify a class of mildly numeric planning problems where a classical planner, i.e., LAMA, when run on the compiled problem, is highly competitive with state-of-the-art numeric planners
Sampling Strategies for Conformant Planning
No full textWe present a generalisation of CPCES, a conformant planner that uses two procedures: candidate plan generation and sampling of the initial belief state. The new CPCES better distinguishes these two procedures and therefore provides a clearer framework for the resolution of conformant planning problems. We study CPCES theoretically by analysing the sampling phase through the lens of tags, width and basis. The benefit of this new interpretation is twofold: firstly it allows us to bound the maximum number of iterations required by CPCES, and second it allows us to individuate sampling strategies that guarantee the discovery of subsets of minimal bases. An experimental analysis reported in the paper shows that the greedy sampling (the original version of CPCES) is the more effective strategy, coverage wise. However, when either the quality of the plans or the size of the resulting samples is important a more sophisticated sampling is more effective
Temporal Planning with Temporal Metric Trajectory Constraints
No full textIn several industrial applications of planning, complex temporal
metric trajectory constraints are needed to adequately
model the problem at hand. For example, in production
plants, items must be processed following a “recipe” of steps
subject to precise timing constraints. Modeling such domains
is very challenging in existing action-based languages due to
the lack of sufficiently expressive trajectory constraints.
We propose a novel temporal planning formalism allowing
quantified temporal constraints over execution timing of action
instances. We build on top of instantaneous actions borrowed
from classical planning and add expressive temporal
constructs. The paper details the semantics of our new formalism
and presents a solving technique grounded in classical,
heuristic forward search planning. Our experiments prove
the proposed framework superior to alternative state-of-theart
planning approaches on industrial benchmarks, and competitive
with similar solving methods on well known benchmarks
took from the planning competition
Optimising the Stability in Plan Repair via Compilation
No full textPlan repair is the problem of solving a given planning problem by using a solution plan of a similar problem. Plan repair problems can arise in execution contexts, that is, when an agent performing the plan has to deal with some unexpected contingency that makes the given plan invalid. Repairing a plan works often much better than replanning from scratch, and is crucial when plans have to be kept stable. There is no planning system until now that guarantees to find plans at the minimum distance from an input plan. This paper presents the first approach to such a problem; we indeed introduce a simple compilation scheme that converts a classical planning problem into another where optimal plans correspond to plans with the minimum distance from an input plan. Our experiments using a number of planners show that such a simple approach can solve the plan repair problem optimally and more effectively than replanning from scratch for a large number of cases. Last but not least, the approach proves competitive with LPG-ADAPT
Proactive and Reactive Reconfiguration for the Robust Execution of Multi Modality Plans
Full text linkThe paper addresses the problem of executing a plan in a dynamic environment for tasks involving constraints on consumable resources modeled as numeric fluents. In particular, the paper proposes a novel monitoring and adaptation strategy joining reactivity and proactivity in a unified framework. By exploiting the flexibility of a multi modality plan (where each action can be executed in different modalities), reactivity and proactivity are guaranteed by means of a reconfiguration step. The reconfiguration is performed (i) when the plan is no more valid to recovery from the impasse (reactively), or (ii) under the lead of a kernel based strategy to enforce the tolerance to unexpected situations (proactivity). Both mechanisms have been integrated into a continual planning system and experimentally evaluated over three numeric domains, extensions of planning competition domains. Results show that the approach is able to increase the percentage of cases successfully solved while preserving efficiency in most situations
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