Behavioural specialisation in embodied evolutionary robotics: why so difficult?
| Autor: | Nicolas Bredeche, Jean-Marc Montanier, Simon Carrignon |
|---|---|
| Přispěvatelé: | Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Barcelona Supercomputing Center |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Collective behavior division of labor Computer science Robòtica evolutiva Evolutionary robotics 02 engineering and technology behavioral specialization Task (project management) Algorithms--Data processing [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 03 medical and health sciences collective behavior Evolutionary robotics--Computer simulation [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Artificial Intelligence Human–computer interaction Specialization (functional) 0202 electrical engineering electronic engineering information engineering [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] embodied evolution ComputingMilieux_MISCELLANEOUS Robotics and AI Behavioral specialization business.industry Division of labor Enginyeria electrònica [Àrees temàtiques de la UPC] Behavioral modeling Robotics Robots--Programació Computer Science Applications Distributed online learning 030104 developmental biology distributed online learning Embodied cognition [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] Robot Embodied evolution 020201 artificial intelligence & image processing Artificial intelligence business Division of labour evolutionary robotics |
| Zdroj: | Frontiers in Robotics and AI Frontiers in Robotics and AI, Frontiers Media S.A., 2016, 3 (38), pp.1-11. ⟨10.3389/frobt.2016.00038⟩ Recercat. Dipósit de la Recerca de Catalunya instname Frontiers in Robotics and AI, 2016, 3 (38), pp.1-11. ⟨10.3389/frobt.2016.00038⟩ UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 2296-9144 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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