Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T22:17:57.610Z Has data issue: false hasContentIssue false
  • English
  • Français

Robotic machine learning of anaphora

Published online by Cambridge University Press:  01 July 1998

Patrick Suppes  and
Michael Böttner
Patrick Suppes
Affiliation:
Ventura Hall, Stanford University, Stanford CA 94305-4115, USA. E-mail: suppes@csli.stanford.edu.
Michael Böttner
Affiliation:
Ventura Hall, Stanford University, Stanford CA 94305-4115, USA. E-mail: suppes@csli.stanford.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Our contribution tackles the problem of learning to understand anaphoric references in the context of robotic machine learning; e.g. Get the large screw. Put it in the left hole. Our solution assumes the probabilistic theory of learning spelt out in earlier publications. Associations are formed probabilistically between constituents of the verbal command and constituents of a presupposed internal representation. The theory is extended, as a first step, to anaphora by learning how to distinguish between incorrect surface depth and the correct tree-structure depth of the anaphoric references.

Keywords

AnaphoraMachine learningRobots
Type
Research Article
Information
Robotica , Volume 16 , Issue 4 , July 1998 , pp. 425 - 431
Copyright
© 1998 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)