This dissertation addresses the goals of robust, adaptive, scalable, dynamic and distributed agent systems and their application in manufacturing control. A synthesis of methods from dynamical systems theory, computation theory and inductive inference has been developed that suggests a constructive approach to address the problem of emergent computation. Most applications of multi-agent systems in manufacturing control use the term emergence in a rather vague fashion, describing a process where macro-level behavior arises from the interaction of some micro-level components. The actual cause for this emergent phenomenon - the intrinsic computation of the process - has not yet been taken into account.
In this context, the multi-agent system is understood in the sense of an agent based model where each thing is represented as an agent in a population of distinctive agents. Agents observe their environment and reconstruct a model based on patterns that reflect the structure in which the process stores and transforms information. This unique model - it is minimal in complexity and maximal in predictive power - is used in forecasting the manufacturing process. This pattern that allows for a large reduction in complexity a small reduction in accuracy is the emergent phenomenon in forecasting.