Simulating Biological Intelligence: Active Inference with Experiment-Informed Generative Model
This work addresses the need for explainable and biologically plausible AI models for researchers in AI and neuroscience, though it is incremental as it builds on existing active inference theory.
The authors tackled the problem of modeling purposeful behavior in autonomous agents by proposing an active inference framework with experiment-informed generative models, simulating decision-making in a game-play environment and demonstrating learning with insights into memory-based learning and predictive planning.
With recent and rapid advancements in artificial intelligence (AI), understanding the foundation of purposeful behaviour in autonomous agents is crucial for developing safe and efficient systems. While artificial neural networks have dominated the path to AI, recent studies are exploring the potential of biologically based systems, such as networks of living biological neuronal networks. Along with promises of high power and data efficiency, these systems may also inform more explainable and biologically plausible models. In this work, we propose a framework rooted in active inference, a general theory of behaviour, to model decision-making in embodied agents. Using experiment-informed generative models, we simulate decision-making processes in a simulated game-play environment, mirroring experimental setups that use biological neurons. Our results demonstrate learning in these agents, providing insights into the role of memory-based learning and predictive planning in intelligent decision-making. This work contributes to the growing field of explainable AI by offering a biologically grounded and scalable approach to understanding purposeful behaviour in agents.