Phil 9.5.18

Marco Barbina – Leonardo SpA – Italy (chair E. Di Nitto, room: FBK – Sala Stringa)

  • Sensors on UAVs
    • Electro-optic
    • Radar-based
    • Hyper-spectral(?)
  • Sensors are not the problem, bandwidth is
  • Selecting the relevant information
  • Attention – which parts of the signal are important
    • Autoencoder NN – it’s like dropout?
    • Split domain stacked cnn. The front half (encoder) NN encodes the data and transmits the “compressed” data from the hidden layer to the back half of the NN.

Goal-aware Team Affiliation in Collectives of Autonomous Robots
Lukas Esterle

  • Teamwork in nature
  • Online multi-task k-assignment
  • stampedes!
  • Observer/follower == explore/exploit
  • They did use the multi-armed bandit framework as a learning system.
  • Real world implementation is intruder detection and response

Optimizing Transitions Between Abstract ABM Demonstrations (video presentation)
Brian Seipp, Karan Budhraja and Tim Oates

  • What is the optimal path that connects two behaviors
  • Traverse a behavior coordinate space
  • Getting from a start image to a goal image. The image can “mean” many things. Agents provide the mapping?
  • Intermediate states are shown
  • Noise added to guarantee convergence?

Self-organized Resource Allocation for Reconfigurable Robot Ensembles
Julian Hanke, Oliver Kosak, Alexander Schiendorfer and Wolfgang Reif

  • Search – determine dangers
  • Continual observe
  • React
  • This is a fitness space analysis based on optimal configuration of robots for a given task. It’s kind of a distributed traveling salesman problem combined with a market and a bidder as a way of merging the distributed solution. I wonder if it is computationally better than a GA solution. It could be a way of looking at hyperparameter tuning?
  • Not clear that it can handle a floating point value, like fuel level
  • The goal is for the allocation to be fast

Panel: The Future of Autonomic Computing and Self-* Systems:

  • Kristie Bellman – some success but in isolated, hard to define areas. Build foundation of principals that can be used to frame development
  • Ada Diaconescu – Efficiency-oriented or creativity-oriented self* systems
  • Simon Dobson – We have turned a lot of fields into computation. But – AI told us nothing about how people play Go. We’re trying to build, not describe
  • Xiaohui (Helen) Gu – AIOps, bridging the gap between academia and industry
  • Arif Merchant – (Google) Missing holistic, end-to-end solutions. First and follow-on principals.
  • Danny Weyns – Be less ad-hoc and more scientific. Address threats to validity. Solid foundations.
  • I talked a bit about the continuum from the low-hanging fruit of autonomic systems to the difficult task of creating self-aware systems.

A Macro-Level Order Metric for Self-Organizing Adaptive Systems
David King and Gilbert Peterson

  • critical and stable states, based on local and global entropy
  • I think that he is talking about “personalized” entropy rather than “local” entropy.

A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People
Nicolas Verstaevel, Carole Bernon, Jean-Pierre Georgé and Marie-Pierre Gleizes

  • Real time detection of abnormal behaviors by using feedback from the medical staff.
  • Detect anomalies through a linear regression of disparity values
  • Is this another hyperparameter system? It could be looking for sensitivity of the system to hyperparameter

Risk-based Testing of Self-Adaptive Systems using Run-Time Predictions
André Reichstaller and Alexander Knapp

  • Uses machine learning to explore the space that includes errors. It is a way of finding major(?) features in the state space

Play with Generated Adversarial Networks (GANs) in your browser!

  • First, we’re not visualizing anything as complex as generating realistic images. Instead, we’re showing a GAN that learns a distribution of points in just two dimensions. There’s no real application of something this simple, but it’s much easier to show the system’s mechanics. For one thing, probability distributions in plain old 2D (x,y) space are much easier to visualize than distributions in the space of high-resolution images.