Intelligent Supervision for Robust Plan Execution

Acting in real world may be a difficult task for an agent, either software or robotic, because unexpected contingencies may arise at any step of the execution. Previous approaches to robust plan execution consider propositional goals to be achieved and time constraints to be satisfied. However, realistic plans must obey to constraints on continuous/consumable resources, too.To face the complexity in handling these resources, the paper proposes the notion of Multi Modality Action (MMA). The model allows to explicitly express the multiple execution modalities in which a given action can be executed; each execution modality models requirements/consequences on the involved consumable resources when that modality is selected. Relying on the MMA notion, the paper presents how the repair problem can be seen as a problem of reconfiguring actions modalities, and how it can be solved by exploiting a CSP encoding.The MMAs are employed by a new continual planner, FLEX-RR, which, exploiting the synergy from the reconfiguration and a numeric planning mechanism can efficiently repair on the fly the plan keeping it rather stable. An empirical analysis performed on three numeric planning domains, confirms the large benefits of FLEX-RR in terms of competence, efficiency and stability of the repaired plan.

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“…In our version of the PlanetaryRover domain, presented in [25], the most of the MMAs has two or three execution modalities.…”

Section: Experiments In the Planetaryrover Domainmentioning

confidence: 99%

Robust plan execution via reconfiguration and replanning

Scala

Micalizio

Torasso

2015

AIC

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“…On the other hand, the planning formalism must be able to express and handle temporal constraints. Let us notice that (Micalizio et al, 2011) considers plans with durative actions, although time is not continuous. The objective of this work is to prevent problems rather than repair them : when a temporal behavioral pattern expresses a deviation with respect to the nominal expected behavior, the modality of execution of the current action may be changed.…”

Section: Diagnosis For Planning : Plan Diagnosismentioning

confidence: 99%

Bridging control and artificial intelligence theories for diagnosis: A survey

Travé-Massuyès

2014

Engineering Applications of Artificial Intelligence

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Diagnosis is the process of identifying or determining the nature and root cause of a failure, problem, or disease from the symptoms resulting from selected measurements, checks or tests. The different facets of this problem and the wide spectrum of classes of systems make it interesting to several communities and require bridging several theories. Diagnosis is actually a functional fragment in fault management architectures and it must smoothly interact with other functions. This paper presents diagnosis as it is understood in the Control and Artificial Intelligence fields, and exemplifies how different theories of these fields can be synergistically integrated to provide better diagnostic solutions and to achieve improved fault management in different environments 1 .

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“…For instance the robotic systems described in [5] and in [21] can perform a drive action in fast or slow modes. Reliable transmission to the earth, for example, can be slow and cheap, or fast and expensive, depending on the devices actually used.…”

Section: Motivating Examplementioning

confidence: 99%

ReCon: An Online Task ReConfiguration Approach for Robust Plan Execution

Scala

Micalizio

Torasso

2015

Lecture Notes in Computer Science

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The 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.

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Intelligent Supervision for Robust Plan Execution

Cited by 9 publications

References 4 publications

Robust plan execution via reconfiguration and replanning

Robust plan execution via reconfiguration and replanning

Bridging control and artificial intelligence theories for diagnosis: A survey

ReCon: An Online Task ReConfiguration Approach for Robust Plan Execution

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