NDLI: Which criteria for autonomously shifting between goal-directed and habitual behaviors in robots?

Content Provider	IEEE Xplore Digital Library
Author	Renaudo, E. Girard, B. Chatila, R. Khamassi, M.
Copyright Year	2015
Description	Author affiliation: Inst. des Syst. Intelligents et de Robot., Sorbonne Univ., Paris, France (Renaudo, E.; Girard, B.; Chatila, R.; Khamassi, M.)
Abstract	Research in the fields of Psychology and Neuroscience have provided strong evidence that mammals can adaptively switch between goal-directed behaviors - i.e. deliberative decisions based on costly but flexible planned long-term consequences of actions - and habitual behaviors - i.e. reactive behaviors that are efficient when the environment is stable but inflexible in the case of environmental changes. However, the computational principles underlying this switching ability are not yet understood, and several alternative criteria have been proposed, each tested on specific subsets of experimental datasets. Here we present a neurorobotic implementation and comparison of such type of criteria, plus some new ones imported from the field of ensemble reinforcement learning, with a twofold objective: on the one hand exploring the possible efficiency of such bio-inspired principles to enable robots to have more behavioral flexibility during autonomous development and learning; on the other hand, analyzing whether an asynchronous continuous robotic simulation and comparison of these criteria in a common task can feed current debates in the Psychological and Neuroscience fields. We evaluate these methods in an apparently simple repetitive cube-pushing task on a simulated conveyor belt, but which imposes to the robot constant trade-offs between speed and accuracy and between stability and abrupt changes. Our results show that if overall performance is not improved by using multiple behavioral systems in a stable environment, these methods allow for a better adaptation to environmental changes. The Voting methods and Boltzmann addition, from ensemble reinforcement learning, give the best performance, providing an interesting alternative to Expert selection.
Starting Page	254
Ending Page	260
File Size	558696
Page Count	7
File Format	PDF
e-ISBN	9781467393201
DOI	10.1109/DEVLRN.2015.7346152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Animals Computational modeling Learning (artificial intelligence) Computer architecture Belts Entropy Robots
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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