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Workshop: Sergio Gonzales and Nikos Gekas, Chair: Peggy Series

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What
  • ANC Workshop Talk
When May 21, 2013
from 11:00 AM to 12:00 PM
Where IF 4.31/4.33
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Sergio Gonzales

Agent-Based Control for Collaborative Domestic Demand Response in Future Electricity Networks

Abstract:  A key stability constraint of electricity networks is that supply and demand must be in balance all the time. This requirement is being challenged by: (1) the paradigm shift from few massive carbon-intensive power plants to many distributed small-scale intermittent renewable generators (e.g., solar PVs and wind turbines); (2) the increase in demand by the electrification of heat and transport; and (3) the inability to economically store electricity in large quantities. This research project aims at creating novel agent-based control mechanisms to coordinate domestic electricity consumption. A real-time price signal is used to reflect the supply availability. Smart houses receive this signal and use it along with consumers’ preferences to adapt their electricity consumption, in order to minimise the electricity bill and satisfy the global stability constraint in future scenarios.


Nikos Gekas

Complexity and specificity of experimentally induced expectations in motion perception
 
A growing body of work suggests that perception is akin to Bayesian Inference [1]. In this context, it is critical to understand how priors are formed, what complexity of the prior distributions can be learned, and whether learned priors transfer to similar stimuli. Only a handful of studies have started to explore these issues (e.g. [2], [3]).
 
In [4], we had shown that the statistics of past visual motion stimuli can powerfully modulate the perception of new motion directions. We manipulated subjects’ expectations by using a bimodal distribution of motion directions such that two directions were more frequently presented than the others. Subjects (i) perceived motion directions as being more similar to the most frequent directions than they really were, and (ii) in the absence of stimuli, the most frequent stimuli were frequently perceived.
 
Here, we modify this paradigm to explore whether subjects can learn multiple stimulus distributions simultaneously.  We interleaved moving dot displays of two different colours with different motion direction distributions. When one distribution was uniform and the other bimodal, subjects learned the statistics of the combined (bimodal) distribution. When one distribution was bimodal and the other its complementary so that the combined distribution was uniform, subjects tried to learn the statistics of each distribution but did not clearly apply that knowledge only to the appropriate condition.
 
Our findings suggest that it is possible to learn the joint statistics of the stimuli but only under specific conditions. Our results can be modeled using a Bayesian framework and discussed in terms of a sub-optimality of the statistical learning process under some conditions. Understanding the limitations of statistical learning for complex stimuli may help understanding how expectations are learned at the neural level.

1. Fiser et al. Trends Cogn Sci 2010, 14:119-130.
2. Turk-Browne et al. J Exp Psychol Learn Mem Cogn 2008, 34: 399-407.
3. Turk-Browne & Scholl J Exp Psychol Hum Percept Perform 2009, 35: 195-202.
4. Chalk et al. J Vision 2010, 10: 1-18.