Enhancing virtual reality with artificial life: Reconstructing a flooded European Mesolithic landscape
Abstract
The fusion of Virtual Reality and Artificial Life technologies has opened up a valuable and effective technique for research in the field of dynamic archaeological reconstruction. This paper describes early evaluations of simulated vegetation and environmental models using decentralized Artificial Life entities. The results demonstrate a strong feasibility for the application of integrated VR and Artificial Life in solving a 10,000 year old mystery shrouding a submerged landscape in the Southern North Sea, off the east coast of the United Kingdom. Three experimental scenarios with dynamic, “artificial” vegetation are observed to grow, reproduce, and react to virtual environmental parameters in a way that mimics their physical counterparts. Through further experimentation and refinement of the Artificial Life rules, plus the integration of additional knowledge from subject matter experts in related scientific fields, a credible reconstruction of the ancient and, today, inaccessible landscape may be within our reach.Citation
Ch'ng, E., and Stone, R. 'Enhancing Virtual Reality with Artificial Life: Reconstructing a flooded European Mesolithic Landscape', Presence: Teleoperators and Virtual Environments, 15(3) pp. 341-352.Publisher
MIT PressJournal
Presence: Teleoperators and Virtual EnvironmentsAdditional Links
https://www.mitpressjournals.org/doi/10.1162/pres.15.3.341Type
Journal articleLanguage
enDescription
Earlier research to visualise the submerged Shotton River by Ch’ng, Arvanitis and Stone (2004) provide a foundation for the realisation that the manual placement of plants based on geo-archaeology and paleobotany did not represent an accurate reconstruction. This paper extended previous work using VR visualisation, adding artificial life software to simulate the growth of ‘virtual’ vegetation. The relationship with the Institute of Archaeology and Antiquity at the University of Birmingham continued.ISSN
1054-74601531-3263
ae974a485f413a2113503eed53cd6c53
10.1162/pres.15.3.341