Applications and design of cooperative multi-agent ARN-based systems.

Gerrard, Claire E.; McCall, John; Macleod, Christopher; Coghill, George M.

Abstract

The Artificial Reaction Network (ARN) is an Artificial Chemistry inspired by Cell Signalling Networks (CSNs). Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In previous work, the ARN was applied to the simulation of the chemotaxis pathway of E. coli and to the control of quadrupedal robotic gaits. In this paper, the design and application of ARN-based cell-like agents termed Cytobots are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognizing environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics.

Citation

GERRARD, C.E., MCCALL, J., MACLEOD, C. and COGHILL, G.M. 2015. Applications and design of cooperative multi-agent ARN-based systems. Soft computing [online], 19(6), pages 1581-1594. Available from: https://doi.org/10.1007/s00500-014-1330-9

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2014
• Online Publication Date: Jun 21, 2014
• Publication Date: Jun 30, 2015
• Deposit Date: Jun 11, 2015
• Publicly Available Date: Jun 11, 2015
• Journal: Soft computing
• Print ISSN: 1432-7643
• Electronic ISSN: 1433-7479
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 6
• Pages: 1581-1594
• DOI: https://doi.org/10.1007/s00500-014-1330-9
• Keywords: Artificial biochemical network (ABN); Artificial chemistry; Artificial neural network (ANN); Swarm robotics
• Public URL: http://hdl.handle.net/10059/1218