Scientists have proposed a new AI method in which systems of habitual and goal-directed behaviors learn to help each other.
Simplicity versus adaptability: Understanding the balance between habitual and goal-directed behaviors Through computer simulations that mimicked the exploration of a maze, the method quickly adapts to changing environments and also reproduced the behavior of humans and animals after they had been accustomed to a certain environment for a long time.
The scientists derived a model that integrates habitual and goal-directed systems for learning behavior in AI agents that perform reinforcement learning, a method of learning based on rewards and punishments, based on the theory of"active inference," which has been the focus of much attention recently. In the paper, they created a computer simulation mimicking a task in which mice explore a maze based on visual cues and are rewarded with food when they reach the goal.
For a new goal that AI has not trained for, it uses an internal model of the environment to plan its actions. It does not need to consider all possible actions but uses a combination of its habitual behaviors, which makes planning more efficient. This challenges traditional AI approaches which require all possible goals to be explicitly included in training for them to be achieved.
"We are exploring the computational principles by which multiple systems in the brain work together. We have also seen that neuromodulators such as dopamine and serotonin play a crucial role in this process," Prof. Kenji Doya, head of the Neural Computation Unit explained."AI systems developed with inspiration from the brain and proven capable of solving practical problems can serve as valuable tools in understanding what is happening in the brains of humans and animals.
Psychology Social Psychology Intelligence Computer Modeling Video Games Neural Interfaces Mathematics
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