Machine learning in agent-based stochastic simulation: Inferential theory and evaluation in transportation logistics

Abstract

Multiagent-based simulation is an approach to realize stochastic simulation where both the behavior of the modeled multiagent system and dynamic aspects of its environment are implemented with autonomous agents. Such simulation provides an ideal environment for intelligent agents to learn to perform their tasks before being deployed in a real-world environment. The presented research investigates theoretical and practical aspects of learning by autonomous agents within stochastic agent-based simulation. The theoretical work is based on the Inferential Theory of Learning, which describes learning processes from the perspective of a learner’s goal as a search through knowledge space. The theory is extended for approximate and probabilistic learning to account for the situations encountered when learning in stochastic environments. Practical aspects are exemplified by two use cases in autonomous logistics: learning predictive models for environment conditions in the future, and learning in the context of evolutionary plan optimization.