Plan Recovery in Reactive HTNs Using Symbolic Planning

Ouali, Lydia Ould; Rich, Charles

doi:10.1007/978-3-319-21365-1_33

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2016

2024

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“…Symbolic architectures are usually good for planning, but realization of reactive behavior within them is awkward, while emergent architectures are usually better suited for reactive control. Thus, hybrid solutions are developed to solve the problem of plan recovery [1].…”

Section: Introductionmentioning

confidence: 99%

Real-Time GA-Based Probabilistic Programming in Application to Robot Control

Potapov

Rodionov

Potapova

2016

Artificial General Intelligence

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Abstract. Possibility to solve the problem of planning and plan recovery for robots using probabilistic programming with optimization queries, which is being developed as a framework for AGI and cognitive architectures, is considered. Planning can be done directly by introducing a generative model for plans and optimizing an objective function calculated via plan simulation. Plan recovery is achieved almost without modifying optimization queries. These queries are simply executed in parallel with plan execution by a robot meaning that they continuously optimize dynamically varying objective functions tracking their optima. Experiments with the NAO robot showed that replanning can be naturally done within this approach without developing special plan recovery methods.

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Section: Introductionmentioning

confidence: 99%