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Maze navigation and memory with physical reservoir computers
conference contribution
posted on 2023-06-09, 15:32 authored by Chris JohnsonChris Johnson, Andy PhilippidesAndy Philippides, Phil HusbandsPhil HusbandsThe extent to which an organism’s morphology may shape its behaviour is increasingly studied, but still not well understood (McGeer, 1990; Pfeifer and Bongard, 2007; Nakajima et al., 2015; Caluwaerts et al., 2012; Zhao et al., 2013). Hauser et al. (2011, 2012) introduced mass-spring-damper (MSD) reservoir networks as morphologically computing abstracted bodies. As these networks are abstracted from biological bodies, the two will share some properties and capabilities, and studying the former may give us useful clues about the latter. We have previously applied small MSD network pairs to the production of reactive behaviour often referred to as ‘minimally cognitive’ (Johnson et al., 2014, 2015). Here we go on to use similar controllers to solve a target-seeking problem for a mobile agent in a maze, which necessitates memory, over a finite but extended period. If MSD networks with relatively few elements but still high dynamic complexity can solve navigation problems requiring this kind of short term memory, then we may speculate that simple organisms can also.
History
Publication status
- Published
File Version
- Accepted version
Journal
Late Breaking Proceedings of the European Conference on Artificial Life 2015Publisher
MIT PressPublisher URL
Page range
19-22Event name
European Conference on Artificial Life 2015Event location
York, UKEvent type
conferenceEvent date
20-24 July 2015Department affiliated with
- Informatics Publications
Research groups affiliated with
- Centre for Computational Neuroscience and Robotics Publications
Full text available
- No
Peer reviewed?
- Yes
Editors
Paul Andrews, Simon Hickinbotham, Joe Timmis, Tim Taylor, René Doursat, Susan Stepney, Leo Caves, Fiona PolackLegacy Posted Date
2018-10-17First Compliant Deposit (FCD) Date
2018-10-16Usage metrics
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