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Belief modelling for situation awareness in human-robot interaction
2010
19th International Symposium in Robot and Human Interactive Communication
To interact naturally with humans, robots need to be aware of their own surroundings. This awareness is usually encoded in some implicit or explicit representation of the situated context. In this paper, we present a new framework for constructing rich belief models of the robot's environment. Key to our approach is the use of Markov Logic as a unified framework for inference over these beliefs. Markov Logic is a combination of first-order logic and probabilistic graphical models. Its
doi:10.1109/roman.2010.5598723
dblp:conf/ro-man/LisonEK10
fatcat:ou7d6kp4svgwjpawpn7ls3ruwu