Academic Journal
Behavioural Specialisation in Embodied Evolutionary Robotics: Why so Difficult?
| Τίτλος: | Behavioural Specialisation in Embodied Evolutionary Robotics: Why so Difficult? |
|---|---|
| Συγγραφείς: | Montanier, Jean-Marc, Carrignon, Simon, Bredeche, Nicolas |
| Συνεισφορές: | Barcelona Supercomputing Center |
| Στοιχεία εκδότη: | Frontiers Media |
| Έτος έκδοσης: | 2016 |
| Συλλογή: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| Θεματικοί όροι: | Àrees temàtiques de la UPC::Enginyeria electrònica, Evolutionary robotics--Computer simulation, Behavioral modeling, Algorithms--Data processing, Embodied evolution, Evolutionary robotics, Behavioral specialization, Division of labor, Distributed online learning, Collective behavior, Robots--Programació, Robòtica evolutiva |
| Περιγραφή: | Embodied evolutionary robotics is an on-line distributed learning method used in collective robotics where robots are facing open environments. This paper focuses on learning behavioral specialization, as defined by robots being able to demonstrate different kind of behaviors at the same time (e.g., division of labor). Using a foraging task with two resources available in limited quantities, we show that behavioral specialization is unlikely to evolve in the general case, unless very specific conditions are met regarding interactions between robots (a very sparse communication network is required) and the expected outcome of specialization (specialization into groups of similar sizes is easier to achieve). We also show that the population size (the larger the better) as well as the selection scheme used (favoring exploration over exploitation) both play important – though not always mandatory – roles. This research sheds light on why existing embodied evolution algorithms are limited with respect to learning efficient division of labor in the general case, i.e., where it is not possible to guess before deployment if behavioral specialization is required or not, and gives directions to overcome current limitations. ; This work is supported by the European Unions Horizon 2020 research and innovation programme under grant agreement No 640891, and the ERC Advanced Grant EPNet (340828). Part of the experiments presented in this paper were carried out using the Grid’5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER, and several Universities as well as other funding bodies (see https://www.grid5000.fr). The other parts of the simulations have been done in the supercomputer MareNostrum at Barcelona Supercomputing Center – Centro Nacional de Supercomputacion (The Spanish National Supercomputing Center). ; Peer Reviewed ; Postprint (published version) |
| Τύπος εγγράφου: | article in journal/newspaper |
| Περιγραφή αρχείου: | 11 p.; application/pdf |
| Γλώσσα: | English |
| Relation: | http://journal.frontiersin.org/article/10.3389/frobt.2016.00038/full; info:eu-repo/grantAgreement/EC/H2020/640891/EU/Deferred Restructuring of Experience in Autonomous Machines/DREAM; info:eu-repo/grantAgreement/EC/FP7/340828/EU/Production and distribution of food during the Roman Empire: Economics and political dynamics./EPNET; https://hdl.handle.net/2117/88475 |
| DOI: | 10.3389/frobt.2016.00038 |
| Διαθεσιμότητα: | https://hdl.handle.net/2117/88475 https://doi.org/10.3389/frobt.2016.00038 |
| Rights: | http://creativecommons.org/licenses/by/4.0/ ; Open Access ; Attribution 4.0 International License |
| Αριθμός Καταχώρησης: | edsbas.E8ED0F1F |
| Βάση Δεδομένων: | BASE |
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