Download 1 Title: Machine learning/Artificial intelligence for Prediction in

1 Title: Machine learning/Artificial intelligence for Prediction in Robotics (MAPiR)
Robotics and Intelligent Systems group (ROBIN), University of Oslo
Contact: Jim Tørresen, [email protected] and Kyrre Glette, [email protected]
More information: http://www.mn.uio.no/ifi/english/research/groups/robin
The PhD project will be connected to our ongoing Research Council of Norway funded
projects about Robot Companions for Elderly Care and/or Prediction and Coordination for
Robots and Interactive Music. They are both concerned with applying machine learning/
artificial intelligence for analyzing multi-sensor systems to train behavior models of both
robots and human users for predicting future events and actions.
Requirements: Applicants should have strong programming skills, and knowledge and
experience with machine learning/artificial intelligence.
2 Main objective and summary of the project
Humans are superior to computers and robots when it comes to being alert by applying
multimodal sensing together with learned knowledge in predicting future events and
choosing the best actions. Are we able to transfer these skills into intelligent systems and also
apply that knowledge in how the systems interact with human users? The goal of this project
is to design, implement and evaluate systems that can learn to predict and act using
knowledge about human prediction and decision making mechanisms [1].
Computational models providing prediction are the target of this project. However, that
requires an internal model of the system to be involved in the prediction whether it is a
network of sensors, a data set, a robot, a human or a combination of two or more of these.
Thus, we shall work on developing various kinds of internal models that can represent the
behavior to be modeled (which together with perception provide a system with selfawareness capabilities) [5]. These would be relevant for robot control and user interaction.
The applications would be within robotics (e.g. robot companions to be applied within
elderly care or in other application areas) or to enhance the user adaptation of smartphones.
The work would be building on the state-of-the-art knowledge in sensors, behavioral models,
machine learning, and artificial intelligence.
3 Project background and scientific basis
There is currently taking place a transition from systems earlier having a static behavior into
now being able sense its user and being able to adapt to the current needs and preferences of its
given user. This is relevant for many kinds of systems including robots which in the future are
going to appear closer to humans than what we are used to; at home, at work and out in the
society. Our research group is currently involved in several projects funded by the Research
Council of Norway where we target to develop technology with such adaptive features. Thus,
this PhD project is to complement with these and would be collaborating with researchers in
them. The projects include Elderly Care with Robot Companion (MECS, 2016-2019) and
Engineering Predictability with Embodied Cognition (EPEC, 2016–2019). The projects target
to develop computational intelligence models being able to represent behaviour and act with
timely response through prediction capabilities whether it regards the systems itself or its user.
The foundation will be our earlier work undertaken on self-aware and self-expressive
computing systems [3] including with motion studies [4].
4 Research questions and scientific challenges
We would like to focus on the following research challenges in the project:
• Extract knowledge mechanisms of human prediction, intuition and reasoning
• Develop learning methods for predicting future events and actions
• Develop mechanisms for adaptive response from quick and instinctive to slower wellreasoned
• Demonstrate the feasibility of the developed technology in a set of different use cases
• Contribute to the development of a computational reference architecture
5 Scientific method
The project will consist of working with relevant data collected from the selected
applications and later in the project also testing the developed methods in real-time
operation. Different available feature extraction and classification algorithms are to be
compared, combined and extended including recent ones in artificial neural networks
[2]. We will employ quantitative measures of performance, and supplement with
statistical assessments, given the stochastic processes and real-world nature of the
research.
6
Ethics
There are few ethical issues related to the project but if present, approvals from the Regional
Committees for Medical and Health Research Ethics would be collected, informed consent
from all study participants, guaranteeing confidentiality and anonymous processing of data and
acting in accordance with the Guidelines for Research Ethics.
7 Project timeline
Year 1: Literature study. Choose relevant applications. Mandatory courses. Implement
methods and first experiments based on simulation. Publication.
Year 2-3: Results from real world application. More experiments with new or modified
methods. Several publications.
Year 4: Final experiments. An extensive publication. Thesis writing.
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References
[1] Evans, J. St. B. T. and Stanovich, K. E. Dual-process theories of higher cognition. Science, 8:223–
241, 2013.
[2] LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
[3] Lewis, P.R.; Chandra, A.; Funmilade, F.; Glette, K.; Chen, T.; Bahsoon, R.; Torresen, J.; Yao. X.:
Arch. Aspects of Self-Aware and Self-Expressive Computing Syst.: From Psychology to
Engineering, IEEE Computer, vol.48, no. 8, pp. 62-70, Aug. 2015
[4] Nymoen, K.,...Torresen J. Analyzing correspondence between sound objects and body motion. In
ACM Trans. on Applied Perception, 10(2), 2013
[5] Schillaci, G., Hafner, V. V., & Lara, B. Exploration behaviours, body representations and simulations
processes for the development of cognition in artificial agents. Frontiers in Robotics and AI,
3(June), 39, 2016.