Vector-space Analysis of Belief-state Approximation for POMDPs

01/10/2013
by   Pascal Poupart, et al.
0

We propose a new approach to value-directed belief state approximation for POMDPs. The value-directed model allows one to choose approximation methods for belief state monitoring that have a small impact on decision quality. Using a vector space analysis of the problem, we devise two new search procedures for selecting an approximation scheme that have much better computational properties than existing methods. Though these provide looser error bounds, we show empirically that they have a similar impact on decision quality in practice, and run up to two orders of magnitude more quickly.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8

research
01/16/2013

Value-Directed Belief State Approximation for POMDPs

We consider the problem belief-state monitoring for the purposes of impl...
research
01/30/2013

Tractable Inference for Complex Stochastic Processes

The monitoring and control of any dynamic system depends crucially on th...
research
06/30/2011

Finding Approximate POMDP solutions Through Belief Compression

Standard value function approaches to finding policies for Partially Obs...
research
04/05/2021

Another Approximation of the First-Passage Time Densities for the Ratcliff Diffusion Decision Model

We present a novel method for approximating the probability density func...
research
01/10/2013

Value-Directed Sampling Methods for POMDPs

We consider the problem of approximate belief-state monitoring using par...
research
12/01/2015

Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing

Existing methods for retrieving k-nearest neighbours suffer from the cur...
research
03/27/2013

Approximations in Bayesian Belief Universe for Knowledge Based Systems

When expert systems based on causal probabilistic networks (CPNs) reach ...

Please sign up or login with your details

Forgot password? Click here to reset