Integrating Learning into a BDI Agent for Environments with Changing Dynamics


We propose a framework that adds learning for improving plan selection in the popular BDI agent programming paradigm. In contrast with previous proposals, the approach given here is able to scale up well with the complexity of the agent's plan library. Technically, we develop a novel confidence measure which allows the agent to adjust its reliance on the learning dynamically, facilitating in principle infinitely many (re)learning phases. We demonstrate the benefits of the approach in an example controller for energy management

Similar works

This paper was published in RMIT Research Repository.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.