Paper Info
Title
Knowledge Representation for Cognitive Robotic Systems
Abstract
Cognitive robotics are autonomous systems capable of artificial reasoning. Such systems can be achieved with a logical approach, but still AI struggles to connect the abstract logic with real-world meanings. Knowledge representation and reasoning help to resolve this problem and to establish the vital connection between knowledge, perception, and action of a robot. Cognitive robots must use their knowledge against the perception of their world and generate appropriate actions in that world in compliance with some goals and beliefs. This paper presents an approach to multi-tier knowledge representation for cognitive robots, where ontologies are integrated with rules and Bayesian networks. The approach allows for efficient and comprehensive knowledge structuring and awareness based on logical and statistical reasoning.
Year
DOI
Venue
2012
10.1109/ISORCW.2012.36
ISORC Workshops
Keywords
Field
DocType
cognitive robot,abstract logic,statistical reasoning,bayesian network,knowledge representation,logical approach,artificial reasoning,cognitive robotic systems,reasoning help,cognitive robotics,comprehensive knowledge structure,bayesian networks,cognition,statistical analysis,ontologies,reasoning,autonomous systems,robotics,knowledge representation and reasoning,logical reasoning
Cognitive robotics,Procedural knowledge,Body of knowledge,Knowledge representation and reasoning,Computer science,Model-based reasoning,Knowledge-based systems,Human–computer interaction,Artificial intelligence,Reasoning system,Distributed computing,Open Knowledge Base Connectivity
Conference
Citations 
PageRank 
References 
6
0.76
2
Authors
2
Name
Order
Citations
PageRank
Emil Vassev126341.81
Mike Hinchey249451.89