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AR
2011
14 years 7 months ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...
CLIMA
2011
13 years 11 months ago
Verifying Team Formation Protocols with Probabilistic Model Checking
Multi-agent systems are an increasingly important software paradigm and in many of its applications agents cooperate to achieve a particular goal. This requires the design of effi...
Taolue Chen, Marta Z. Kwiatkowska, David Parker, A...
IFM
2007
Springer
15 years 6 months ago
Qualitative Probabilistic Modelling in Event-B
Abstract. Event-B is a notation and method for discrete systems modelling by refinement. We introduce a small but very useful construction: qualitative probabilistic choice. It ex...
Stefan Hallerstede, Thai Son Hoang
AAAI
2011
13 years 12 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
CMPB
2010
97views more  CMPB 2010»
14 years 10 months ago
A ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge
Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. ...
Weiru Liu, Anbu Yue, David J. Timson