Uncertainty, Action & Interaction: in Pursuit of Cognitive Information Processing

我的研究愿景旨在为现实生活环境中增强人类认知的深刻认知信息处理（CIP）系统的设计和实现做出贡献。CIP涉及感知，学习，推理和面对不确定性和噪音的强大互动能力。我计划在我以前的工作的基础上，详细阐述有效的算法，以解决不同的认知水平，从低到高层次的持续CIP系统：（i）推理不确定的知识在静态世界中;（ii）推理随着时间的推移在一个不确定的环境中;（iii）决定做什么在不确定的环境中;（iv）决定做什么在一个不确定的和多智能体的环境。在（i）的上下文中，我们建议构建我们自己的先验，以便可以将有向无环图结构覆盖为信念网络和潜在树。我们特别研究了隐藏单元流行度的后验分布的表示是最好的。在（ii）的背景下，我们的工作将主要集中在贝叶斯视角下建模和过滤之间的相互作用。一个方向的目的是应用灵活的贝叶斯非参数建模方法来学习未知的概率模型的隐藏状态估计。另一个方向是设计一个递归贝叶斯过滤框架，以有效地更新贝叶斯非参数建模性能的在线方式。对于水平（iii），我们将首先研究贝叶斯部分可观察马尔可夫决策过程（POMDP）背景下的迁移学习情况，特别是（a）当多任务和生活学习用于学习POMDP时，以及（B）当来自单个任务的训练数据不足时。其次，我们研究了一个更具投机性的途径，包括在结构贝叶斯POMDP的形式的动态决策网络（DDN），即DBN完成的奖励，然后将其转换为一个推理问题。最后和（四），我们将集中在几个方面的合作控制，特别是代理之间的共识，可以制定在“潜在的游戏”，以及如何这些特定的游戏可以涉及到分布式资源分配以及本地和分布式控制法。

My research vision aims to contribute to the design and implementation of profound cognitive information processing (CIP) systems for augmented human cognition in real-life environments. CIP involves the ability to perceive, learn, reason and interact robustly in face of uncertainty and noise. I plan to build on my previous work to elaborate efficient algorithms to tackle different cognitive levels from low to high-level sustaining CIP systems: (i) reasoning on uncertain knowledge in static worlds; (ii) reasoning over time in an uncertain environment; (iii) deciding what to do in uncertain environment; (iv) deciding what to do in an uncertain and multiagent environment. In the context of (i), we propose to construct our own priors so that one can cover directed acyclic graph structures as belief networks and latent trees. We particularly investigate which representation of the posterior distribution over hidden units' popularity is the best. In the context of (ii), our work will mainly focus on the interaction between modelling and filtering in a Bayesian perspective. One direction aims at applying flexible Bayesian nonparametric modelling methods to learn unknown probabilistic models for hidden state estimation. The other direction is to design a recursive Bayesian filtering framework to efficiently update Bayesian nonparametric modelling performance in an online manner. For level (iii), we will first investigate the case of transfer learning in the context of Bayesian partially observable Markov decision process (POMDP), particularly (a) when multi-task and life-learning are used for learning a POMDP and (b) when the training data from single task are not sufficient. Second, we investigate a more speculative avenue which consists in structuring Bayesian POMDP in the form of dynamic decision network (DDN), that is, a DBN completed by rewards, and then translate it to an inference problem. Finally and for (iv), we will focus on how several aspects of cooperative control, particularly consensus between agents, can be formulated in ``potential games'' and how these specific games can be pertained to distributed resource allocation as well as to local and distributed control laws.