Finished Meltdown. Need to write up some notes.
Think about using a CMAC or Deep CMAC for function learning, because NIST. Also, can it be used for multi-dimensional learning?
- Cerebellar model articulation controller
- Adaptive Noise Cancellation Using Deep Cerebellar Model Articulation Controller
- RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology
- A hybrid control system, integrating principal and compensation controllers, is developed for multiple-input-multiple-output (MIMO) uncertain nonlinear systems. This hybrid control system is based on sliding-mode technique and uses a recurrent cerebellar model articulation controller (RCMAC) as an uncertainty observer. The principal controller containing an RCMAC uncertainty observer is the main controller, and the compensation controller is a compensator for the approximation error of the system uncertainty. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. The Taylor linearization technique is employed to increase the learning ability of RCMAC and the adaptive laws of the control system are derived based on Lyapunov stability theorem and Barbalat’s lemma so that the asymptotical stability of the system can be guaranteed. Finally, the proposed design method is applied to control a biped robot. Simulation results demonstrate the effectiveness of the proposed control scheme for the MIMO uncertain nonlinear system
- Github CMAC TF projects
7:00 – 12:00, 2:00 – 5:00 ASRC Research
- Finish up At Home in the Universe notes – done!
- Get started on framing out Antonio’s paper – good progress!
- Basically, Aaron and I think there is a spectrum of interaction that can occur in these systems. At one end is some kind of market, where communication is mediated through price, time, and convenience to the transportation user. At the other is a more top down, control system way of dealing with this. NIST RCS would be an example of this. In between these two extremes are control hierarchies that in turn interact through markets
- Wrote up some early thoughts on how simulation and machine learning can be a thinking fast and slow solution to understandable AI
Listening to We Can’t Talk Anymore? Understanding the Structural Roots of Partisan Polarization and the Decline of Democratic Discourse in 21st Century America. Very Tajfel
- David Peritz
- Political polarization, accompanied by negative partisanship, are striking features of the current political landscape. Perhaps these trends were originally confined to politicians and the media, but we recently reached the point where the majority of Americans report they would consider it more objectionable if their children married across party lines than if they married someone of another faith. Where did this polarization come from? And what it is doing to American democracy, which is housed in institutions that were framed to encourage open deliberation, compromise and consensus formation? In this talk, Professor David Peritz will examine some of the deeper forces in the American economy, the public sphere and media, political institutions, and even moral psychology that best seem to account for the recent rise in popular polarization.
Sent out a Doodle to nail down the time for the PhD review
Went looking for something that talks about the cognitive load for TIT-FOR-TAT in the Iterated Prisoner’s Dilemma and can’t find anything. Did find this though, that is kind of interesting: New tack wins prisoner’s dilemma. It’s a collective intelligence approach:
- Teams could submit multiple strategies, or players, and the Southampton team submitted 60 programs. These, Jennings explained, were all slight variations on a theme and were designed to execute a known series of five to 10 moves by which they could recognize each other. Once two Southampton players recognized each other, they were designed to immediately assume “master and slave” roles – one would sacrifice itself so the other could win repeatedly.
- Nick Jennings
- Professor Jennings is an internationally-recognized authority in the areas of artificial intelligence, autonomous systems, cybersecurity and agent-based computing. His research covers both the science and the engineering of intelligent systems. He has undertaken fundamental research on automated bargaining, mechanism design, trust and reputation, coalition formation, human-agent collectives and crowd sourcing. He has also pioneered the application of multi-agent technology; developing real-world systems in domains such as business process management, smart energy systems, sensor networks, disaster response, telecommunications, citizen science and defence.
- Sarvapali D. (Gopal) Ramchurn
- I am a Professor of Artificial Intelligence in the Agents, Interaction, and Complexity Group (AIC), in the department of Electronics and Computer Science, at the University of Southampton and Chief Scientist for North Star, an AI startup. I am also the director of the newly created Centre for Machine Intelligence. I am interested in the development of autonomous agents and multi-agent systems and their application to Cyber Physical Systems (CPS) such as smart energy systems, the Internet of Things (IoT), and disaster response. My research combines a number of techniques from Machine learning, AI, Game theory, and HCI.
7:00 – 4:30 ASRC MKT
- SASO Travel request
- SASO Hotel – done! Aaaaand I booked for August rather than September. Sent a note to try and fix using their form. If nothing by COB try email.
- Potential DME repair?
- Starting Deep Learning with Keras. Done with chapter one
- Two seedbank lstm text examples:
- Generate Shakespeare using tf.keras
- This notebook demonstrates how to generate text using an RNN with tf.keras and eager execution.This notebook is an end-to-end example. When you run it, it will download a dataset of Shakespeare’s writing. The notebook will then train a model, and use it to generate sample output.
- This notebook will let you input a file containing the text you want your generator to mimic, train your model, see the results, and save it for future use all in one page.