Knowledge Representation and Ontologies for Autonomous Systems
Papers from the AAAI Spring Symposium
Craig Schlenoff and Michael Uschold, Cochairs
For an autonomous system to behave appropriately in an uncertain environment, many researchers feel that the system must have an internal representation (world model) of entities, events, and situations that it perceives in the world. The term "autonomous systems" in this context refers to embodied intelligent systems that can operate for extended periods of time without human supervision. A major challenge for these systems is maintaining an accurate internal representation of pertinent information about the environment.
A large body of work exists in various knowledge representation, ontology, and data fusion areas, yet relatively little has been applied to real-time world modeling in autonomous systems. This symposium's objective was to bring together colleagues in the autonomous systems, knowledge representation, ontology, and data fusion communities to explore leveraging existing knowledge technologies to benefit autonomous systems.