Toward Rough-Granular Computing
Identifieur interne : 000D51 ( Main/Exploration ); précédent : 000D50; suivant : 000D52Toward Rough-Granular Computing
Auteurs : Andrzej Jankowski [Pologne] ; Andrzej Skowron [Pologne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2007.
Abstract
Abstract: Developing methods for approximation of compound concepts expressing the result of perception belongs to the main challenges of Perception Based Computing (PBC) [70]. The perceived concepts are expressed in natural language. We discuss the rough-granular approach to approximation of such concepts from sensory data and domain knowledge. This additional knowledge, represented by ontology of concepts, is used to make it feasible searching for features (condition attributes) relevant for the approximation of concepts on different levels of the concept hierarchy defined by a given ontology. We report several experiments of the proposed methodology for approximation of compound concepts from sensory data and domain knowledge. The approach is illustrated by examples relative to interactions of agents, ontology approximation, adaptive hierarchical learning of compound concepts and skills, behavioral pattern identification, planning, conflict analysis and negotiations, and perception-based reasoning. The presented results seem to justify the following claim of Lotfi A. Zadeh: “In coming years, granular computing is likely to play an increasingly important role in scientific theories-especially in human-centric theories in which human judgement, perception and emotions are of pivotal importance”. The question of how ontologies of concepts can be discovered from sensory data remains as one of the greatest challenges for many interdisciplinary projects on learning of concepts.
Url:
DOI: 10.1007/978-3-540-72530-5_1
Affiliations:
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The perceived concepts are expressed in natural language. We discuss the rough-granular approach to approximation of such concepts from sensory data and domain knowledge. This additional knowledge, represented by ontology of concepts, is used to make it feasible searching for features (condition attributes) relevant for the approximation of concepts on different levels of the concept hierarchy defined by a given ontology. We report several experiments of the proposed methodology for approximation of compound concepts from sensory data and domain knowledge. The approach is illustrated by examples relative to interactions of agents, ontology approximation, adaptive hierarchical learning of compound concepts and skills, behavioral pattern identification, planning, conflict analysis and negotiations, and perception-based reasoning. The presented results seem to justify the following claim of Lotfi A. Zadeh: “In coming years, granular computing is likely to play an increasingly important role in scientific theories-especially in human-centric theories in which human judgement, perception and emotions are of pivotal importance”. The question of how ontologies of concepts can be discovered from sensory data remains as one of the greatest challenges for many interdisciplinary projects on learning of concepts.</div></front></TEI><affiliations><list><country><li>Pologne</li></country></list><tree><country name="Pologne"><noRegion><name sortKey="Jankowski, Andrzej" sort="Jankowski, Andrzej" uniqKey="Jankowski A" first="Andrzej" last="Jankowski">Andrzej Jankowski</name></noRegion><name sortKey="Jankowski, Andrzej" sort="Jankowski, Andrzej" uniqKey="Jankowski A" first="Andrzej" last="Jankowski">Andrzej Jankowski</name><name sortKey="Skowron, Andrzej" sort="Skowron, Andrzej" uniqKey="Skowron A" first="Andrzej" last="Skowron">Andrzej Skowron</name><name sortKey="Skowron, Andrzej" sort="Skowron, Andrzej" uniqKey="Skowron A" first="Andrzej" last="Skowron">Andrzej Skowron</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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