Ajit V. Rajwade

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Recent research on point-based approximation algorithms for POMDPs demonstrated that good solutions to POMDP problems can be obtained without considering the entire belief simplex. For instance, the Point Based Value Iteration (PBVI) algorithm [Pineau et al., 2003] computes the value function only for a small set of belief states and iteratively adds more(More)
My main research centers around sequential decision making under uncertainty. In a complex dynamical system useful abstractions of knowledge can be essential to an autonomous agent for efficient decision making. Predictive State Representation, PSR, has been developed to provide a maintainable, self-verifiable and learnable representation of the knowledge(More)
Hidden Markov Models have emerged as a very popular technique of classification of patterns and sequences, and have been utilized for various applications such as recognition of shapes, speech and other signals. In this report, we study the basic functionality of HMM and explore its limits by testing the model on synthetically generated data belonging to(More)
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