Formal modeling of robot behavior with learning

Kirwan, R., Miller, A. , Porr, B. and Di Prodi, P. (2013) Formal modeling of robot behavior with learning. Neural Computation, 25(11), pp. 2976-3019. (doi: 10.1162/NECO_a_00493)

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We present formal specification and verification of a robot moving in a complex network, using temporal sequence learning to avoid obstacles. Our aim is to demonstrate the benefit of using a formal approach to analyze such a system as a complementary approach to simulation. We first describe a classical closed-loop simulation of the system and compare this approach to one in which the system is analyzed using formal verification. We show that the formal verification has some advantages over classical simulation and finds deficiencies our classical simulation did not identify. Specifically we present a formal specification of the system, defined in the Promela modeling language and show how the associated model is verified using the Spin model checker. We then introduce an abstract model that is suitable for verifying the same properties for any environment with obstacles under a given set of assumptions. We outline how we can prove that our abstraction is sound: any property that holds for the abstracted model will hold in the original (unabstracted) model.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Di Prodi, Mr Paolo and Kirwan, Dr Ryan Fraser and Porr, Dr Bernd and Miller, Professor Alice
Authors: Kirwan, R., Miller, A., Porr, B., and Di Prodi, P.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Engineering > Biomedical Engineering
Journal Name:Neural Computation
Publisher:MIT Press
ISSN (Online):1530-888X
Published Online:07 October 2013

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