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Search Publications by: Craig I. Schlenoff (Fed)

Displaying 226 - 250 of 300

How Task Analysis Can Be Used to Derive and Organize the Knowledge for the Control of Autonomous Vehicles

March 24, 2004

Author(s)

Tony Barbera, James S. Albus, Elena R. Messina, Craig I. Schlenoff, John A. Horst

The Real-time Control System (RCS) Methodology has evolved over a number of years as a technique to capture task knowledge and organize it in a framework conducive to implementation in computer control systems. The fundamental premise of this methodology

How Task Analysis Can Be Used to Derive and Organize the Knowledge for the Control of Autonomous Vehicles

March 1, 2004

Author(s)

Anthony J. Barbera, James S. Albus, Elena R. Messina, Craig I. Schlenoff, John A. Horst

Developing World Model Data Specifications as Metrics for Sensory Processing for On-Road Driving Taks

January 1, 2004

Author(s)

Tony Barbera, John A. Horst, Craig I. Schlenoff, Evan K. Wallace, David Aha

The building of knowledge-intensive real-time intelligent control systems is one of the most difficult tasks humans attempt. It is motivated by the desire to create an artificial reasoning system that is capable of intelligent behavior, i.e., replicating

Knowledge Engineering for Real Time Intelligent Control

December 31, 2003

Author(s)

Elena R. Messina, James S. Albus, Craig I. Schlenoff, J L. Evans

The key to real-time intelligent control lies in the knowledge models that the system contains. We argue that there needs to be a more rigorous approach to engineering the knowledge within intelligent controllers. Three main classes of knowledge are

Achieving Intelligent Performance in Autonomous Driving

October 31, 2003

Author(s)

James S. Albus, John Evans, Craig I. Schlenoff, Tony Barbera, Elena R. Messina, Stephen B. Balakirsky

Achieving Intelligent Performance in Autonomous Driving

October 1, 2003

Author(s)

James S. Albus, John Evans, Craig I. Schlenoff, Anthony J. Barbera, Elena R. Messina, Stephen B. Balakirsky

Developing World Model Data Specifications as Metrics for Sensory Processing for On-Road Driving Tasks

October 1, 2003

Author(s)

Anthony J. Barbera, John A. Horst, Craig I. Schlenoff, Evan K. Wallace, David Aha

Using Ontologies to Aid Navigation Planning in Autonomous Vehicles

September 1, 2003

Author(s)

Craig I. Schlenoff, Stephen B. Balakirsky, M Uschold, R Provine, S J. Smith

This paper describes a system whose overall goal is to utilize ontologies to enhance the capabilities and performance of autonomous vehicles, particularly in the area of navigation planning. Our approach is to develop an ontology of objects based upon a

Moving Object Prediction for Off-Road Autonomous Navigation

August 25, 2003

Author(s)

Rajmohan Madhavan, Craig I. Schlenoff

The realization of on- and o.-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requiresreal-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully planpaths and to navigate in an unstructured

Developing World Model Data Specifications as Metrics for Sensory Processing for On-Road Driving Tasks

August 18, 2003

Author(s)

Tony Barbera, John A. Horst, Craig I. Schlenoff, Evan K. Wallace, David Aha

Building knowledge-intensive real-time ntelligent control systems is one of the most difficult tasks that humans attempt. It is motivated by the desire to create an artificial reasoning system that displays intelligent behavior (i.e. that can act on the

Performance Evaluation of Tools and Techniques for Representing Cost-Based Decision Criteria for On-Road Autonomous Navigation

August 18, 2003

Author(s)

N H. Zimmerman, Craig I. Schlenoff, Stephen B. Balakirsky

The purpose of the work described in this paper is to analyze rule-based and functional tools to determine which are best suited for cost generation in an autonomous vehicular system. In this context, costs are numeric values that represent a penalty the

An Approach to Predicting the Location of Moving Objects During On-Road Navigation

August 15, 2003

Author(s)

Craig I. Schlenoff, Rajmohan Madhavan, Stephen B. Balakirsky

For an autonomous vehicle to navigate in real-time within a dynamic environment, it must be able to respond to moving objects. In particular, it must be able to predict, with appropriate levels of confidence, where those objects are expected to be at times

Moving Object Prediction for Off-road Autonomous Navigation

April 25, 2003

Author(s)

Rajmohan Madhavan, Craig I. Schlenoff

The realization of on- and off-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured