Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Using Model Trees for Classification
Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Towards developing general models of usability with PARADISE
Natural Language Engineering
Dialog in the open world: platform and applications
Proceedings of the 2009 international conference on Multimodal interfaces
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Learning to predict engagement with a spoken dialog system in open-world settings
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing
IEEE Transactions on Affective Computing
Two people walk into a bar: dynamic multi-party social interaction with a robot agent
Proceedings of the 14th ACM international conference on Multimodal interaction
Detecting Engagement in HRI: An Exploration of Social and Task-Based Context
SOCIALCOM-PASSAT '12 Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust
Attention-based addressee selection for service and social robots to interact with multiple persons
Proceedings of the Workshop at SIGGRAPH Asia
Comparing task-based and socially intelligent behaviour in a robot bartender
Proceedings of the 15th ACM on International conference on multimodal interaction
Comparing task-based and socially intelligent behaviour in a robot bartender
Proceedings of the 15th ACM on International conference on multimodal interaction
Towards action selection under uncertainty for a socially aware robot bartender
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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A robot agent existing in the physical world must be able to understand the social states of the human users it interacts with in order to respond appropriately. We compared two implemented methods for estimating the engagement state of customers for a robot bartender based on low-level sensor data: a rule-based version derived from the analysis of human behaviour in real bars, and a trained version using supervised learning on a labelled multimodal corpus. We first compared the two implementations using cross-validation on real sensor data and found that nearly all classifier types significantly outperformed the rule-based classifier. We also carried out feature selection to see which sensor features were the most informative for the classification task, and found that the position of the head and hands were relevant, but that the torso orientation was not. Finally, we performed a user study comparing the ability of the two classifiers to detect the intended user engagement of actual customers of the robot bartender; this study found that the trained classifier was faster at detecting initial intended user engagement, but that the rule-based classifier was more stable.