University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Probabilistic Modeling for Sequential Decision Making under Uncertainty Problems

Probabilistic Modeling for Sequential Decision Making under Uncertainty Problems

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Host: Dr Mohan Sridharan

Abstract: In this talk, I will present my recent research results on planning and learning under uncertainty for sequential decision making problems. In particular, I will show how to tackle both the curse of dimensionality and the curse of history. To tackle those issues in planning under uncertainty, my researches resort to three principled techniques that i) realize and integrate temporal abstraction to scale up planning, ii) use Monte-Carlo simulations for complex and intractable computations, and iii) exploit model-based trajectory optimization to deal with smooth dynamics and differential constraints in (non-linear) dynamical systems. In the second part of the talk, I will show my recent results on model-based learning. In particular, I will describe how to achieve a better data-efficiency and generalization by integrating future predictions and using Bayesian optimization.


This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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