Back to Search
Start Over
Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
- Source :
- Frontiers in Robotics and AI, Vol 8 (2021), Frontiers in Robotics and AI
- Publication Year :
- 2021
- Publisher :
- Frontiers Media S.A., 2021.
-
Abstract
- Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.
- Subjects :
- bias
evolvable morphologies
Computer science
02 engineering and technology
Machine learning
computer.software_genre
03 medical and health sciences
locality
Artificial Intelligence
Encoding (memory)
behavioral traits
0202 electrical engineering, electronic engineering, information engineering
TJ1-1570
Mechanical engineering and machinery
Control (linguistics)
evolvable robots
Original Research
030304 developmental biology
Robotics and AI
0303 health sciences
business.industry
Locality
Principal (computer security)
QA75.5-76.95
encoding
Computer Science Applications
Bounded function
Electronic computers. Computer science
Robot
020201 artificial intelligence & image processing
phenotypic traits
Artificial intelligence
Behavior-based robotics
business
computer
Generative grammar
Subjects
Details
- Language :
- English
- ISSN :
- 22969144
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Frontiers in Robotics and AI
- Accession number :
- edsair.doi.dedup.....025cf83906d195b6fcc1f7533dc59860