论文标题
多模式和多因素分支时间活动推断
Multi-Modal and Multi-Factor Branching Time Active Inference
论文作者
论文摘要
主动推断是建模大脑的最新框架，该框架解释了各种机制，例如习惯形成，多巴胺能排放和好奇心。最近，已经开发了基于蒙特卡洛树搜索的两个版本的分支时间活动推理（BTAI），以处理在计算所有可能的策略直至时间范围内计算先前的策略时发生的指数（时空和时间）复杂性类别。但是，这两个版本的BTAI仍然遭受指数复杂性类W.R.T的损失。在本文中，我们首先允许对几种观察的建模来解决此限制，每个观测值都有自己的可能性映射。同样，我们允许每个潜在状态都有自己的过渡映射。然后，推论算法利用了可能性和过渡映射的分解以加速后验的计算。在DSPRITES环境上测试了这两个优化，其中DSPRITES数据集的元数据被用作模型的输入，而不是DSPRITES图像。在此任务上，$ btai_ {vmp} $（Champion等，2022b，a）能够在5.1秒内解决96.9％的任务，而$ btai_ {bf} $（Champion等，2021a）能够解决该任务的98.6 \％的17.5秒。我们的新方法（$ btai_ {3mf} $）通过仅在2.559秒内完成任务（100 \％），超过了其两个前任。最后，$ btai_ {3mf} $已在灵活且易于使用的（Python）软件包中实现，我们开发了一个图形用户界面，以实现对模型信念，计划过程和行为的检查。
Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity. Recently, two versions of branching time active inference (BTAI) based on Monte-Carlo tree search have been developed to handle the exponential (space and time) complexity class that occurs when computing the prior over all possible policies up to the time horizon. However, those two versions of BTAI still suffer from an exponential complexity class w.r.t the number of observed and latent variables being modelled. In the present paper, we resolve this limitation by first allowing the modelling of several observations, each of them having its own likelihood mapping. Similarly, we allow each latent state to have its own transition mapping. The inference algorithm then exploits the factorisation of the likelihood and transition mappings to accelerate the computation of the posterior. Those two optimisations were tested on the dSprites environment in which the metadata of the dSprites dataset was used as input to the model instead of the dSprites images. On this task, $BTAI_{VMP}$ (Champion et al., 2022b,a) was able to solve 96.9\% of the task in 5.1 seconds, and $BTAI_{BF}$ (Champion et al., 2021a) was able to solve 98.6\% of the task in 17.5 seconds. Our new approach ($BTAI_{3MF}$) outperformed both of its predecessors by solving the task completly (100\%) in only 2.559 seconds. Finally, $BTAI_{3MF}$ has been implemented in a flexible and easy to use (python) package, and we developed a graphical user interface to enable the inspection of the model's beliefs, planning process and behaviour.
