Microsoft Researchers Introduce a Theoretical Framework Using Variational Bayesian Theory Incorporating a Bayesian Intention Variable
Practical Solutions and Value
In decision-making, habitual behavior and goal-directed behavior have been traditionally seen as separate. Microsoft researchers introduce a framework to unify these behaviors, enhancing decision-making efficiency and adaptability in both biological and artificial agents.
The Bayesian behavior framework addresses the traditional division between habitual and goal-directed behaviors, leveraging variational Bayesian methods to synergize these two behavior types. This allows for a seamless transition and interaction between the two behavior types, enhancing decision-making processes.
The framework was tested in vision-based sensorimotor tasks within a T-maze environment, yielding significant observations such as the transition from goal-directed to habitual behavior, resilience in habitual behaviors, and zero-shot goal-directed planning.
This innovative approach not only bridges the gap between habitual and goal-directed behaviors but also enhances the efficiency and adaptability of decision-making processes in both biological and artificial agents.
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