Application of Confidence Intervals to the Autonomous Acquisition of High-level Spatial Knowledge

03/27/2013
by   Lambert E. Wixson, et al.
0

Objects in the world usually appear in context, participating in spatial relationships and interactions that are predictable and expected. Knowledge of these contexts can be used in the task of using a mobile camera to search for a specified object in a room. We call this the object search task. This paper is concerned with representing this knowledge in a manner facilitating its application to object search while at the same time lending itself to autonomous learning by a robot. The ability for the robot to learn such knowledge without supervision is crucial due to the vast number of possible relationships that can exist for any given set of objects. Moreover, since a robot will not have an infinite amount of time to learn, it must be able to determine an order in which to look for possible relationships so as to maximize the rate at which new knowledge is gained. In effect, there must be a "focus of interest" operator that allows the robot to choose which examples are likely to convey the most new information and should be examined first. This paper demonstrates how a representation based on statistical confidence intervals allows the construction of a system that achieves the above goals. An algorithm, based on the Highest Impact First heuristic, is presented as a means for providing a "focus of interest" with which to control the learning process, and examples are given.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 7

page 8

research
09/12/2021

Optimal hypergeometric confidence sets are (almost) always intervals

We present an efficient method of calculating exact confidence intervals...
research
03/05/2018

Expected length of post-model-selection confidence intervals conditional on polyhedral constraints

Valid inference after model selection is currently a very active area of...
research
06/24/2020

AutoNCP: Automated pipelines for accurate confidence intervals

Successful application of machine learning models to real-world predicti...
research
09/20/2019

Novel algorithm for confidence sub-contour box estimation: an alternative to traditional confidence intervals

The factor estimation process is a really challenging task for non-linea...
research
01/30/2015

Confidence intervals for AB-test

AB-testing is a very popular technique in web companies since it makes i...
research
04/25/2022

Relevance Models Based on the Knowledge Gap

Search systems are increasingly used for gaining knowledge through acces...

Please sign up or login with your details

Forgot password? Click here to reset