Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Why interaction is more powerful than algorithms
Communications of the ACM
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Emergence: A Paradigm for Robust and Scalable Distributed Applications
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Using the experimental method to produce reliable self-organised systems
Engineering Self-Organising Systems
Properties and mechanisms of self-organizing MANET and P2P systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Designing self-organising environments with agents and artefacts: a simulation-driven approach
International Journal of Agent-Oriented Software Engineering
Engineering Systems Which Generate Emergent Functionalities
Engineering Environment-Mediated Multi-Agent Systems
Enhancing self-organising emergent systems design with simulation
ESAW'06 Proceedings of the 7th international conference on Engineering societies in the agents world VII
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The goal of engineering self-organising emergent systems is to acquire a macroscopic system behaviour solely from autonomous local activity and interaction. Due to the non-deterministic nature of such systems, it is hard to guarantee that the required macroscopic behaviour is achieved and maintained. Before even considering a self-organising emergent system in an industrial context, e.g. for Automated Guided Vehicle (AGV) transportation systems, such guarantees are needed. An empirical analysis approach is proposed that combines realistic agent-based simulations with existing scientific numerical algorithms for analysing the macroscopic behaviour. The numerical algorithm itself obtains the analysis results on the fly by steering and accelerating the simulation process according to the algorithm's goal. The approach is feasible, compared to formal proofs, and leads to more reliable and valuable results, compared to mere observation of simulation results. Also, the approach allows to systematically analyse the macroscopic behaviour to acquire macroscopic guarantees and feedback that can be used by an engineering process to iteratively shape a self-organising emergent solution.