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ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Multiple Modalities in Interactive Systems and Robots, Volume 4 Issue 3, October 2014



Section: Special Issue on Multiple Modalities in Interactive Systems and Robots

Introduction to the Special Issue on Machine Learning for Multiple Modalities in Interactive Systems and Robots
Heriberto Cuayáhuitl, Lutz Frommberger, Nina Dethlefs, Antoine Raux, Mathew Marge, Hendrik Zender
Article No.: 12e
DOI: 10.1145/2670539

This special issue highlights research articles that apply machine learning to robots and other systems that interact with users through more than one modality, such as speech, gestures, and vision. For example, a robot may coordinate its speech...

Efficient Interactive Multiclass Learning from Binary Feedback
Hung Ngo, Matthew Luciw, Jawas Nagi, Alexander Forster, Jürgen Schmidhuber, Ngo Anh Vien
Article No.: 12
DOI: 10.1145/2629631

We introduce a novel algorithm called upper confidence-weighted learning (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL...

Interpreting Natural Language Instructions Using Language, Vision, and Behavior
Luciana Benotti, Tessa Lau, Martín Villalba
Article No.: 13
DOI: 10.1145/2629632

We define the problem of automatic instruction interpretation as follows. Given a natural language instruction, can we automatically predict what an instruction follower, such as a robot, should do in the environment to follow that...

Machine Learning for Social Multiparty Human--Robot Interaction
Simon Keizer, Mary Ellen Foster, Zhuoran Wang, Oliver Lemon
Article No.: 14
DOI: 10.1145/2600021

We describe a variety of machine-learning techniques that are being applied to social multiuser human--robot interaction using a robot bartender in our scenario. We first present a data-driven approach to social state recognition based on...

Nonstrict Hierarchical Reinforcement Learning for Interactive Systems and Robots
Heriberto Cuayáhuitl, Ivana Kruijff-Korbayová, Nina Dethlefs
Article No.: 15
DOI: 10.1145/2659003

Conversational systems and robots that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem has been addressed either by using function approximation techniques that...