On Predictive planning and counterfactual learning in active inference
Aswin Paul Takuya Isomura Adeel Razi
Given the rapid advancement of artificial intelligence, understanding the
foundations of intelligent behaviour is increasingly important. Active
inference, regarded as a general theory of behaviour, offers a principled
approach to probing the basis of sophistication in planning and
decision-making. In this paper, we examine two decision-making schemes in
active inference based on 'planning' and 'learning from experience'.
Furthermore, we also introduce a mixed model that navigates the data-complexity
trade-off between these strategies, leveraging the strengths of both to
facilitate balanced decision-making. We evaluate our proposed model in a
challenging grid-world scenario that requires adaptability from the agent.
Additionally, our model provides the opportunity to analyze the evolution of
various parameters, offering valuable insights and contributing to an
explainable framework for intelligent decision-making.