EditorialKnowledge engineering and ontologies for autonomous systems: 2004 AAAI Spring Symposium
Section snippets
Problem statement
Many researchers feel that an autonomous system must have an internal representation of entities, events, and situations that it perceives in the world in order for it to behave appropriately in uncertain environments. It must have an internal model that captures the richness of what it knows and learns, and a mechanism for computing values and priorities that enables it to choose effective actions [1]. The term “autonomous system” in this context refers to an embodied intelligent system that
The symposium
The Knowledge Representation and Ontologies for Autonomous Systems Symposium was motivated by the desire to bring together experts in the autonomous systems, knowledge representation, ontology, and data fusion communities to explore leveraging existing knowledge technologies to benefit autonomous systems. It was held during March 22–24, 2004 at the Stanford Campus in Palo Alto, CA as part of the 2004 American Association for Artificial Intelligence (AAAI) Spring Symposium Series. The symposium
Results and future direction
This symposium was intended to be the first in a series of workshops that address the general area of applying knowledge representation techniques towards the area of autonomous systems. As mentioned earlier in the article, our primary objectives were to bring the two communities together to explore the potential benefits of leveraging knowledge representation technologies to meet challenges in autonomous systems and set up collaborations. To a large extent, we succeeded in these goals. There
Synopsis of this special issue
This special issue has been organized to ensure that the significant results presented at the symposium reach a wider audience. We have asked selected authors from the symposium to submit updated and extended versions of their paper for inclusion in this issue of the journal.
The nine articles in this journal issue represent a good sampling of the types of knowledge representation approaches that are currently being applied to autonomous systems. The first set of three articles explores
Acknowledgements
We would like to thank the symposium's organizing committee who helped to make the event a success by providing useful technical insights on the topics for the symposium and in assisting in reviewing the papers that were submitted. The organizing committee consisted of:
- (1)
Craig Schlenoff (chair), National Institute of Standards and Technology (NIST), USA
- (2)
Michael Uschold (co-chair), Boeing, USA
- (3)
James Albus, NIST, USA
- (4)
Stephen Balakirsky, NIST, USA
- (5)
James Crawford, NASA Ames Research Center, USA
- (6)
Hugh
Craig Schlenoff received his Bachelors degree in mechanical engineering from the University of Maryland, College Park and his Masters degree in mechanical engineering from Rensselaer Polytechnic Institute. He is a researcher in the Intelligent Systems Division at the National Institute of Standards and Technology. His research interests include knowledge representation, ontologies, and process specification, primarily applied to autonomous systems and manufacturing. He has recently served as
Reference (1)
- et al.
Engineering of Mind
(2001)
Cited by (0)
Craig Schlenoff received his Bachelors degree in mechanical engineering from the University of Maryland, College Park and his Masters degree in mechanical engineering from Rensselaer Polytechnic Institute. He is a researcher in the Intelligent Systems Division at the National Institute of Standards and Technology. His research interests include knowledge representation, ontologies, and process specification, primarily applied to autonomous systems and manufacturing. He has recently served as the program manager for the Process Engineering Program at NIST as well as the Director of Ontologies and Domain Knowledge at VerticalNet, Inc. He has served on the organizing and program committee of numerous knowledge representation and ontology related conferences and workshops, and has published over 35 papers in related areas.
Mike Uschold is a research scientist at Boeing Phantom Works, the advanced research and development organization of The Boeing Company. His interests center around the field concerned with the development and application of ontologies. This includes the emerging Semantic Web, semantic integration, knowledge management, and more recently, in the area of world modeling for autonomous vehicle navigation. For over two decades, Mike has been involved in a wide range of activities in these areas, including research, applications and teaching. Dr Uschold is on the industrial advisory boards of various projects and initiatives related to the Semantic Web and other knowledge technologies. He is very active in organizing and participating in workshops and conferences on these topics. He received his BS in mathematics and physics at Canisis College in Buffalo, N.Y in 1977, a Masters in computer science from Rutgers University in 1982, and a PhD in Artificial Intelligence from The University of Edinburgh in 1991. Before arriving at the Boeing Company in 1997, Dr Uschold was a senior member of technical staff in the Artificial Intelligence Applications Institute (AIAI) at the University of Edinburgh. He has also been a lecturer and a research associate at the Department of AI at the University of Edinburgh.