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

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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.