Decentralised Autonomic Computing: Analysing Self-Organising Emergent Behaviour using Advanced Numerical Methods

When designing decentralised autonomic computing systems, a fundamental engineering issue is to assess system-wide behaviour. Such decentralised systems are characterised by the lack of global control, typically consist of autonomous cooperating entities, and often rely on self-organised emergent behaviour to achieve the requirements. A well-founded and practically feasible approach to study overall system behaviour is a prerequisite for successful deployment. On one hand, formal proofs of correct behaviour and even predictions of the exact system-wide behaviour are practically infeasible due to the complex, dynamic, and often nondeterministic nature of self-organising emergent systems. On the other hand, simple simulations give no convincing arguments for guaranteeing system-wide properties. We describe an alternative approach that allows to analyse and assess trends in system-wide behaviour, based on so-called "equation-free" macroscopic analysis. This technique yields more reliable results about the system-wide behaviour, compared to mere observation of simulation results, at an affordable computational cost. Numerical algorithms act at the system-wide level and steer the simulations. This allows to limit the amount of simulations considerably. We illustrate the approach by studying a particular system-wide property of a decentralised control system for automated guided vehicles and we outline a road map towards a general methodology for studying decentralised autonomic computing systems

[1]  C. W. Gear,et al.  Equation-Free, Coarse-Grained Multiscale Computation: Enabling Mocroscopic Simulators to Perform System-Level Analysis , 2003 .

[2]  H. Van Dyke Parunak,et al.  Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide , 1998, MABS.

[3]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[4]  Roger G. Ghanem,et al.  A Multiscale Data Assimilation with the Ensemble Kalman Filter , 2005, Multiscale Model. Simul..

[5]  Ioannis G. Kevrekidis,et al.  Equation-free: The computer-aided analysis of complex multiscale systems , 2004 .

[6]  Wayne L. Winston.  Operations research: applications and algorithms / Wayne L. Winston , 2004 .

[7]  Tucker R. Balch,et al.  Communication in reactive multiagent robotic systems , 1995, Auton. Robots.

[8]  Ioannis G. Kevrekidis,et al.  Projective Methods for Stiff Differential Equations: Problems with Gaps in Their Eigenvalue Spectrum , 2002, SIAM J. Sci. Comput..

[9]  T. Wolf,et al.  Emergence and self-organisation: a statement of similarities and differences , 2004 .

[10]  Daniel Kunkle,et al.  Emergence of constraint in self-organizing systems. , 2004, Nonlinear dynamics, psychology, and life sciences.

[11]  Tucker R. Balch,et al.  Hierarchic Social Entropy: An Information Theoretic Measure of Robot Group Diversity , 2000, Auton. Robots.

[12]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[13]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[14]  Ulrich Nehmzow,et al.  Quantitative analysis of robot-environment interaction - towards "scientific mobile robotics" , 2003, Robotics Auton. Syst..

[15]  Richard John Anthony,et al.  Emergence: a paradigm for robust and scalable distributed applications , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[16]  H. Van Dyke Parunak,et al.  Entropy and self-organization in multi-agent systems , 2001, AGENTS '01.

[17]  Markus P. J. Fromherz,et al.  Distributed Adaptive Constrained Optimization for Smart Matter Systems , 2002 .