Plan acquisition in a BDI agent framework through intentional learning

Inspired by the theory of practical reasoning, Belief-Desire-Intention (BDI) agents are perhaps the most well-known type and architecture of cognitive agents. Such agents can reason about their environment and perform complex plans to bring about their objectives and goals. Within the context of ever-changing environments though, one desirable feature for agents is that of learning, implemented in BDI agents as Intentional Learning, a framework focused on the monitoring of the mental states to include learning as part of the agent goals. In this paper, we consider and develop intentional learning within the Jason BDI framework for agents focused on a plan acquisition strategy addressing the cases of learning plans composed of one action, sequences or a repetition of actions that allow an agent to improve its behaviour at run-time. This is done at the pure BDI agent level, the repertoire of plans is directly updated without using external planning tools. We take as a testbed the simple vacuum cleaning environment and how new plans are acquired for accomplishing tasks of different level of complexity: escape from tunnel-like paths and wall-following. Furthermore, we integrate in a novel way the use of NetLogo as an environment to locate Jason agents, maintaining a clear delineation between decision making and action in the environment with the decision-making firmly anchored within the BDI agent’s reasoning cycle.