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

The Artificial Reaction Network (ARN) is an artificial chemistry inspired by Cell Signalling Networks. Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. 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 recognising 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.

[1]  J. Ross,et al.  Computational functions in biochemical reaction networks. , 1994, Biophysical journal.

[2]  John A. W. McCall,et al.  Artificial Reaction Network Agents , 2013, ECAL.

[3]  Upinder S Bhalla,et al.  Understanding complex signaling networks through models and metaphors. , 2003, Progress in biophysics and molecular biology.

[4]  Werner Dilger,et al.  AIS Based Robot Navigation in a Rescue Scenario , 2004, ICARIS.

[5]  Wolfgang Banzhaf,et al.  Artificial ChemistriesA Review , 2001, Artificial Life.

[6]  Pablo A Iglesias,et al.  Chemoattractant signaling in dictyostelium discoideum. , 2004, Annual review of cell and developmental biology.

[7]  Gérard Berry,et al.  The chemical abstract machine , 1989, POPL '90.

[8]  John A. W. McCall,et al.  Temporal Patterns in Artificial Reaction Networks , 2012, ICANN.

[9]  Wolfgang Banzhaf,et al.  Evolving a "nose" for a robot , 2000 .

[10]  George M. Coghill,et al.  Artificial reaction networks , 2011 .

[11]  B. Kholodenko Cell-signalling dynamics in time and space , 2006, Nature Reviews Molecular Cell Biology.

[12]  R. R. Goldberg,et al.  Effects of chemoattractant pteridines upon speed of D. discoideum vegetative amoebae. , 2006, Cell motility and the cytoskeleton.

[13]  J. Strassmann,et al.  Primitive agriculture in a social amoeba , 2011, Nature.

[14]  Thomas Schmickl,et al.  A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning , 2010, IEEE Congress on Evolutionary Computation.

[15]  Yaochu Jin,et al.  A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network , 2009, Biosyst..

[16]  John A. W. McCall,et al.  Exploring aspects of cell intelligence with artificial reaction networks , 2013, Soft Computing.

[17]  John J Tyson,et al.  Functional motifs in biochemical reaction networks. , 2010, Annual review of physical chemistry.

[18]  Vinod Patidar,et al.  A Pseudo Random Bit Generator Based on Chaotic Logistic Map and its Statistical Testing , 2009, Informatica.

[19]  A. Griffin,et al.  The Social Lives of Microbes , 2007 .

[20]  Brian J. Ford,et al.  On Intelligence in Cells: The Case for Whole Cell Biology , 2009 .

[21]  Borys Wróbel,et al.  Controlling development and chemotaxis of soft-bodied multicellular animats with the same gene regulatory network , 2013, ECAL.

[22]  P. Devreotes Dictyostelium discoideum: a model system for cell-cell interactions in development. , 1989, Science.

[23]  Matthias Becker,et al.  Simulation Model For The Whole Life Cycle Of The Slime Mold Dictyostelium Discoideum , 2010, ECMS.

[24]  Colin McCann,et al.  Cell speed, persistence and information transmission during signal relay and collective migration , 2010, Journal of Cell Science.

[25]  Peter A. J. Hilbers,et al.  Computing with Feedforward Networks of Artificial Biochemical Neurons , 2007, IWNC.

[26]  Phatak,et al.  Logistic map: A possible random-number generator. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[27]  Yiannis Ventikos,et al.  Robotic swarm concept for efficient oil spill confrontation. , 2008, Journal of hazardous materials.

[28]  D. Bray Protein molecules as computational elements in living cells , 1995, Nature.

[29]  D. Cotter,et al.  Patterning of development in Dictyostelium discoideum: factors regulating growth, differentiation, spore dormancy, and germination. , 1992, Biochemistry and cell biology = Biochimie et biologie cellulaire.

[30]  BanzhafWolfgang,et al.  Artificial chemistriesa review , 2001 .

[31]  H. Othmer,et al.  A discrete cell model with adaptive signalling for aggregation of Dictyostelium discoideum. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[32]  Natalie Andrew,et al.  Chemotaxis in shallow gradients is mediated independently of PtdIns 3-kinase by biased choices between random protrusions , 2007, Nature Cell Biology.

[33]  Wei-Min Shen,et al.  Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms , 2004, Auton. Robots.

[34]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[35]  John A. W. McCall,et al.  Adaptive Dynamic Control of Quadrupedal Robotic Gaits with Artificial Reaction Networks , 2012, ICONIP.