2002 ModelingOntologiesForRoboticEnvironments
- (Chella et al., 2002) ⇒ Antonio Chella, Massimo Cossentino, Roberto Pirrone, and Andrea Ruisi. (2002). “Modeling Ontologies for Robotic Environments.” In: Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering (SEKE 2002). doi:10.1145/568760.568775
Subject Headings: Ontology, Robotics.
Notes
Quotes
Author Keywords
Multi Agent Systems, Ontologies, Robotics.
Abstract
On the basis of a multiple abstraction levels specification process, we developed a representational model for environmental robotic knowledge through the definition of a set of ontologies using a multi perspective approach. A general ontological model for typical indoor environments has been first developed, followed by its specialization using an implementation perspective. Actual software implementation of the ontology has been obtained via a XML-based markup language, used to build a repository for robotic environmental knowledge.
1. Introduction
An ontology can be defined as a formally specified model of bodies of knowledge defining the concepts used to describe a domain and the relations that hold between them [4]. In the context of Artificial Intelligence, an ontology deals with what categories of real entities can be identified and how they are related. Knowledge-based system refer to entities and relations in the real world; to build such systems, a well-formalized global ontology is needed to specify what kinds of things exist, what their general properties are, and the interactions among them.
References
- 1. Thrun, S. and Bucken, A. Integrating grid-based and topological maps for mobile robot navigation. In: Proceedings of the 13th Conference on Artificial Intelligence (Portland, Oregon, August 1996)
- 2. D. Maio and S. Rizzi, "Knowledge architecture for environment representation in autonomous agents", Proceedings of ISCIS VIII, Istanbul, 1993.
- 3. D. Maio and S. Rizzi. A Multi-Agent Approach to Environment Exploration. International Journ. Cooperative Information Systems, 5(2-3):213-250, 1996.
- 4. S. Cranefield and M. Purvis. UML as an ontology modelling language. In: Proceedings of the Workshop on Intelligent Information Integration, 16th International Joint Conference on Artificial Intelligence (IJCAI-99), 1999
- 5. T. Duckett, A. Saffiotti, Building globally consistent gridmaps from topologies, in: Proceedings of the Sixth International IFAC Symposium on Robot Control (SYROCO), Wien, Austria, 2000
- 6. Fabrizi, E. and A. Saffiotti (2000). Extracting topology-based maps from gridmaps. In: IEEE Intl. Conference on Robotics and Automation (ICRA). San Francisco, CA.
- 7. Sebastian Thrun, Jens-Steffen Gutmann, Dieter Fox, Wolfram Burgard, Benjamin J. Kuipers, Integrating topological and metroc maps for mobile robot navigation: a statistical approach, Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, p.989-995, July 1998, Madison, Wisconsin, United States
- 8. Benjamin Kuipers, The spatial semantic hierarchy, Artificial Intelligence, v.119 n.1-2, p.191-233, May 2000 doi:10.1016/S0004-3702(00)00017-5
- 9. G. Booch et al. UML for XML Schema Mapping Specification.. Rational Software white paper, 1999
- 10. Migrating from xml dtd to xml schema using uml. Rational Software White Paper, 2000
- 11. Fox D., Burgard W., Thrun S. Probabilistic methods for mobile robot mapping. In: Proceedings of the IJCAI-99 Workshop on Adaptive Spatial Representations of Dynamic Environments, 1999
- 12. Popov, D. Using XML as the core language for Knowledge representation in AI. Proceedings of the 2nd International workshop on Computer Science and Information Technologies (CSIT'2000). Ufa, Russia, 2000,