Understanding and Using Context
Personal and Ubiquitous Computing
Network Engineering for Agile Belief Network Models
IEEE Transactions on Knowledge and Data Engineering
Decision Making and Uncertainty Management in a 3D Reconstruction System
IEEE Transactions on Pattern Analysis and Machine Intelligence
SenSay: A Context-Aware Mobile Phone
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Building large-scale Bayesian networks
The Knowledge Engineering Review
Social Serendipity: Mobilizing Social Software
IEEE Pervasive Computing
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
Context-Aware Composition of Mobile Services
IT Professional
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
Cognitive vision: The case for embodied perception
Image and Vision Computing
MOPET: A context-aware and user-adaptive wearable system for fitness training
Artificial Intelligence in Medicine
Context-aware selection of politeness level for polite mobile service in Korea
Expert Systems with Applications: An International Journal
An Embodied Cognition Approach to Mindreading Skills for Socially Intelligent Robots
International Journal of Robotics Research
A Comparative Study of Mobile-Based Landmark Recognition Techniques
IEEE Intelligent Systems
Enforcing Network Connectivity in Robot Team Missions
International Journal of Robotics Research
Functional object class detection based on learned affordance cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
CMRadar: a personal assistant agent for calendar management
AOIS'04 Proceedings of the 6th international conference on Agent-Oriented Information Systems II
Mixed-Initiative Human–Robot Interaction Using Hierarchical Bayesian Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 12.05 |
Recently, demand for service robots increases, and, particularly, one for personal service robots, which requires robot intelligence, will be expected to increase more. Accordingly, studies on intelligent robots are spreading all over the world. In this situation, we attempt to realize context-awareness for home robot while previous robot research focused on image processing, control and low-level context recognition. This paper uses probabilistic modeling for service robots to provide users with high-level context-aware services required in home environment, and proposes a systematic modeling approach for modeling a number of Bayesian networks. The proposed approach supplements uncertain sensor input using Bayesian network modeling and enhances the efficiency in modeling and reasoning processes using modular design based on domain knowledge. We verify the proposed method is useful as measuring the performance of context-aware module and conducting subjective test.