Task-Directed Computation of Qualitative Decisions from Sensor Data

Research output: Contribution to journalArticle

Abstract

This article describes a novel approach to sensor - based decision making based on formulating and solving large systems of parametric constraints. The constraints describe both a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique that performs decision-directed model recovery is developed. This method is straightforward to apply, is easily parallelized, and convergence can be demonstrated under very reasonable structural and statistical assumptions. This approach is demonstrated on several different decision-making problems involving manipulation and categorization of objects observed with a range scanner. The experiments indicate that simultaneous solution of both model constraints and decision criteria can lead to efficient and effective decision making, even when the observed data does not strongly determine a data model.

Original languageEnglish (US)
Pages (from-to)415-429
Number of pages15
JournalIEEE Transactions on Robotics and Automation
Volume10
Issue number4
DOIs
StatePublished - 1994
Externally publishedYes

Fingerprint

Decision making
Sensors
Data structures
Recovery
Experiments

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Task-Directed Computation of Qualitative Decisions from Sensor Data. / Hager, Gregory.

In: IEEE Transactions on Robotics and Automation, Vol. 10, No. 4, 1994, p. 415-429.

Research output: Contribution to journalArticle

@article{f087850943b04e5483e4215c455cc192,
title = "Task-Directed Computation of Qualitative Decisions from Sensor Data",
abstract = "This article describes a novel approach to sensor - based decision making based on formulating and solving large systems of parametric constraints. The constraints describe both a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique that performs decision-directed model recovery is developed. This method is straightforward to apply, is easily parallelized, and convergence can be demonstrated under very reasonable structural and statistical assumptions. This approach is demonstrated on several different decision-making problems involving manipulation and categorization of objects observed with a range scanner. The experiments indicate that simultaneous solution of both model constraints and decision criteria can lead to efficient and effective decision making, even when the observed data does not strongly determine a data model.",
author = "Gregory Hager",
year = "1994",
doi = "10.1109/70.313093",
language = "English (US)",
volume = "10",
pages = "415--429",
journal = "IEEE Transactions on Robotics and Automation",
issn = "1042-296X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Task-Directed Computation of Qualitative Decisions from Sensor Data

AU - Hager, Gregory

PY - 1994

Y1 - 1994

N2 - This article describes a novel approach to sensor - based decision making based on formulating and solving large systems of parametric constraints. The constraints describe both a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique that performs decision-directed model recovery is developed. This method is straightforward to apply, is easily parallelized, and convergence can be demonstrated under very reasonable structural and statistical assumptions. This approach is demonstrated on several different decision-making problems involving manipulation and categorization of objects observed with a range scanner. The experiments indicate that simultaneous solution of both model constraints and decision criteria can lead to efficient and effective decision making, even when the observed data does not strongly determine a data model.

AB - This article describes a novel approach to sensor - based decision making based on formulating and solving large systems of parametric constraints. The constraints describe both a model for sensor data and the criteria for correct decisions about the data. An incremental constraint solving technique that performs decision-directed model recovery is developed. This method is straightforward to apply, is easily parallelized, and convergence can be demonstrated under very reasonable structural and statistical assumptions. This approach is demonstrated on several different decision-making problems involving manipulation and categorization of objects observed with a range scanner. The experiments indicate that simultaneous solution of both model constraints and decision criteria can lead to efficient and effective decision making, even when the observed data does not strongly determine a data model.

UR - http://www.scopus.com/inward/record.url?scp=0028485514&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028485514&partnerID=8YFLogxK

U2 - 10.1109/70.313093

DO - 10.1109/70.313093

M3 - Article

AN - SCOPUS:0028485514

VL - 10

SP - 415

EP - 429

JO - IEEE Transactions on Robotics and Automation

JF - IEEE Transactions on Robotics and Automation

SN - 1042-296X

IS - 4

ER -