Diagnostic problem solving using swarm intelligence

Abstract

Swarm intelligence can be viewed as the emergent collective intelligence of a group of agents, emphasizing direct or indirect local interactions among relatively simple agents. Swarm methods have been widely used for low-dimensional problems such as modeling collective movements in physical space (computer-generated animation, multi-robot teams, etc.), but they have been less studied in higher dimensional problems, mostly in the form of numerical optimization. In this work, we take a step toward applying these kind of systems to diagnostic problem-solving using causal networks. In our model, simple agents move in an abstract high-dimensional space, and based only on local interactions, generate a solution as a result of their collective behavior. Computational experiments show that this model can approximate the best diagnostic solutions (i.e., Bayesian optimal) in reasonably sized problems.