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2018


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018, Falk Lieder and Frederick Callaway contributed equally to this publication. (inproceedings)

Abstract
How should we think and decide, and how can we learn to make better decisions? To address these questions we formalize the discovery of cognitive strategies as a metacognitive reinforcement learning problem. This formulation leads to a computational method for deriving optimal cognitive strategies and a feedback mechanism for accelerating the process by which people learn how to make better decisions. As a proof of concept, we apply our approach to develop an intelligent system that teaches people optimal planning stratgies. Our training program combines a novel process-tracing paradigm that makes peoples latent planning strategies observable with an intelligent system that gives people feedback on how their planning strategy could be improved. The pedagogy of our intelligent tutor is based on the theory that people discover their cognitive strategies through metacognitive reinforcement learning. Concretely, the tutor’s feedback is designed to maximally accelerate people’s metacognitive reinforcement learning towards the optimal cognitive strategy. A series of four experiments confirmed that training with the cognitive tutor significantly improved people’s decision-making competency: Experiment 1 demonstrated that the cognitive tutor’s feedback accelerates participants’ metacognitive learning. Experiment 2 found that this training effect transfers to more difficult planning problems in more complex environments. Experiment 3 found that these transfer effects are retained for at least 24 hours after the training. Finally, Experiment 4 found that practicing with the cognitive tutor conveys additional benefits above and beyond verbal description of the optimal planning strategy. The results suggest that promoting metacognitive reinforcement learning with optimal feedback is a promising approach to improving the human mind.

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link (url) Project Page [BibTex]

2018


link (url) Project Page [BibTex]


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Discovering Rational Heuristics for Risky Choice

Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L., Lieder, F.

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

Abstract
How should we think and decide to make the best possible use of our precious time and limited cognitive resources? And how do people’s cognitive strategies compare to this ideal? We study these questions in the domain of multi-alternative risky choice using the methodology of resource-rational analysis. To answer the first question, we leverage a new meta-level reinforcement learning algorithm to derive optimal heuristics for four different risky choice environments. We find that our method rediscovers two fast-and-frugal heuristics that people are known to use, namely Take-The-Best and choosing randomly, as resource-rational strategies for specific environments. Our method also discovered a novel heuristic that combines elements of Take-The-Best and Satisficing. To answer the second question, we use the Mouselab paradigm to measure how people’s decision strategies compare to the predictions of our resource-rational analysis. We found that our resource-rational analysis correctly predicted which strategies people use and under which conditions they use them. While people generally tend to make rational use of their limited resources overall, their strategy choices do not always fully exploit the structure of each decision problem. Overall, people’s decision operations were about 88% as resource-rational as they could possibly be. A formal model comparison confirmed that our resource-rational model explained people’s decision strategies significantly better than the Directed Cognition model of Gabaix et al. (2006). Our study is a proof-of-concept that optimal cognitive strategies can be automatically derived from the principle of resource-rationality. Our results suggest that resource-rational analysis is a promising approach for uncovering people’s cognitive strategies and revisiting the debate about human rationality with a more realistic normative standard.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to Select Computations

Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L., Lieder, F.

In Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, August 2018, Frederick Callaway and Sayan Gul and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Assessment Of Atypical Motor Development In Infants Through Toy-Stimulated Play And Center Of Pressure Analysis

Zhao, S., Mohan, M., Torres, W. O., Bogen, D. K., Shofer, F. S., Prosser, L., Loeb, H., Johnson, M. J.

In Proceedings of the Annual Rehabilitation Engineering and Assistive Technology Society of North America (RESNA) Conference, Arlington, USA, July 2018 (inproceedings)

Abstract
There is a need to identify measures and create systems to assess motor development at an early stage. Center of Pressure (CoP) is a quantifiable metric that has been used to investigate postural control in healthy young children [6], children with CP [7], and infants just beginning to sit [8]. It was found that infants born prematurely exhibit different patterns of CoP movement than infants born full-term when assessing development impairments relating to postural control [9]. Preterm infants exhibited greater CoP excursions but had greater variability in their movements than fullterm infants. Our solution, the Play And Neuro-Development Assessment (PANDA) Gym, is a sensorized environment that aims to provide early diagnosis of neuromotor disorder in infants and improve current screening processes by providing quantitative measures rather than subjective ones, and promoting natural play with the stimulus of toys. Previous studies have documented stages in motor development in infants [10, 11], and developmental delays could become more apparent through toy interactions. This study examines the sensitivity of the pressure-sensitive mat subsystem to detect differences in CoP movement patterns for preterm and fullterm infants less than 6 months of age, with varying risk levels. This study aims to distinguish between typical and atypical motor development through assessment of the CoP data of infants in a natural play environment, in conditions where movement may be further stimulated with the presence of a toy.

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link (url) [BibTex]

link (url) [BibTex]


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Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform

Ma, L., Stueckler, J., Wu, T., Cremers, D.

arxiv, 2018, arXiv:1808.01834 (techreport)

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[BibTex]

[BibTex]

2013


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Governance of Humanoid Robot Using Master Exoskeleton

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the IEEE International Symposium on Robotics (ISR), Seoul, South Korea, October 2013 (inproceedings)

Abstract
Dexto:Eka: is an adult-size humanoid robot being developed with the aim of achieving tele-presence. The paper sheds light on the control of this robot using a Master Exoskeleton which comprises of an Exo-Frame, a Control Column and a Graphical User Interface. It further illuminates the processes and algorithms that have been utilized to make an efficient system that would effectively emulate a tele-operator.

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DOI [BibTex]

2013


DOI [BibTex]


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Design and development part 2 of Dexto:Eka: - The humanoid robot

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, August 2013 (inproceedings)

Abstract
Through this paper, we elucidate the second phase of the design and development of the tele-operated humanoid robot Dexto:Eka:. Phase one comprised of the development of a 6 DoF left anthropomorphic arm and left exo-frame. Here, we illustrate the development of the right arm, right exo-frame, torso, backbone, human machine interface and omni-directional locomotion system. Dexto:Eka: will be able to communicate with a remote user through Wi-Fi. An exo-frame capacitates it to emulate human arms and its locomotion is controlled by joystick. A Graphical User Interface monitors and helps in controlling the system.

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DOI [BibTex]

DOI [BibTex]