Exploring generation of a genetic robot's personality through neural and evolutionary means
- Authors
- Lee, Kang-Hee
- Issue Date
- Nov-2011
- Publisher
- ELSEVIER SCIENCE BV
- Keywords
- Optimization; Genetic robot; Artificial creature; Software robot; Artificial life; Emotional robot; Robot genome; Robot personality; Neural network; Evolutionary algorithm; Mobile phone
- Citation
- DATA & KNOWLEDGE ENGINEERING, v.70, no.11, pp.923 - 954
- Journal Title
- DATA & KNOWLEDGE ENGINEERING
- Volume
- 70
- Number
- 11
- Start Page
- 923
- End Page
- 954
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/13545
- DOI
- 10.1016/j.datak.2011.06.002
- ISSN
- 0169-023X
- Abstract
- This paper proposes a way to generate a robot genome that contributes to defining the personality of a software robot or an artificial life in a mobile phone. The personality should be both complex and feature-rich, but still plausible by human standards for an emotional life form. However, it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot's personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes a neural network algorithm for a genetic robot's personality (NNGRP) and an upgraded version of a previously introduced evolutionary algorithm for a genetic robot's personality (EAGRP). The robot genomes for heterogeneous personalities are demonstrably generated via the NNGRP and the EAGRP and compared. The implementation is embedded into genetic robots in a mobile phone to verify the feasibility and effectiveness of each algorithm. (C) 2011 Elsevier B.V. All rights reserved.
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