USE OF REINFORCEMENT LEARNING (RL) FOR PLAN GENERATION IN BELIEF-DESIRE-INTENTION (BDI) AGENT SYSTEMS

Abstract

The Belief-Desire-Intention (BDI) agent framework is a reactive agent framework based on the idea of intentionality. A known weaknesses of BDI is its lack of learning capabilities resulting from its dependence on an a-priori library of plans. BDI plans are designed by human experts on the domain to which BDI is being applied and are fixed. Any situation the BDI agent encounters which does not have a matching plan can result in erroneous agent operation and even agent failure. Researchers have augmented the BDI framework with various learning frameworks including decision trees, self-organizing neural networks, hybrid-architectures using low level learners, and metaplans for plan hypothesis abduction and plan modifications. Other relevant research tackled the use of a-priori knowledge, previously learned knowledge and the learning of plans without a-priori knowledge on planning systems, and the integration of learning, planning and execution. These studies were, however, not investigated in relation to BDI systems. This study explores the successful use of Reinforcement Learning (RL), a computational learning framework based on the idea of learning from repeated interactions with the environment, to generate plans in BDI systems without relying on a-priori knowledge.