Mastering Complex Causal Systems
Tue 16 May - Fri 16 June 2006
The talk focuses on a novel distributed methodology for complex causal systems. Here, a complex system represents a system whose global behaviour, which emerges from the interactions between its usually large number of basic components, is difficult to be accurately described via state-of-the-art modeling techniques. The methodology imitates the manner humans perform reasoning when confronted with mastering complex systems. The monitored complex system is partitioned into minimally separated and causally independent parts that can be separately modeled and understood.
The fact that each region is causally independent from the rest of the model, allows allocating a dedicated local agent to each region, that will be able to perform the required task at local level. However, the local agents exchange information between them via partition's borders. The methodology provides minimal complexity for the communication process between agents. The research performed so far indicates that the methodology can be successfully applied for a variety of purposes on complex systems, which feature causal relationships between their basic components. During presentation, application examples from Fault Diagnosis, Particle Swarm Optimization and Autonomous Agents areas will be provided.