Abstract Hidden Markov Models for Online Probabilistic Plan Recognition

A General Model for Online Probabilistic Plan Recognition

We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHMEM can represent a richer class of probabilistic plans, and at the same time derive...

Policy Recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent’s behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent’s plan. Our contributions in this paper are two fold. In terms of...

Efficient Approximate Inference for Online Probabilistic Plan Recognition

We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHM M). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHM M can represent a richer class of probabilistic plans, and at the same time derive...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words