Met with Heera to discuss her work. I’m going to set up a GitHub project and add a parser that reads in an xml config file that then parses csv files into:
Spreadsheet for evaluation
Split-up csv files for analysis
Pick up air filter and oil change kit for the bike
Ran the random binary network code and generated figures for the text
Remind all the players about the run tomorrow – done
The goal of this tutorial is to promote interest in the study of random Boolean networks (RBNs). These can be very interesting models, since one does not have to assume any functionality or particular connectivity of the networks to study their generic properties. Like this, RBNs have been used for exploring the configurations where life could emerge. The fact that RBNs are a generalization of cellular automata makes their research a very important topic. The tutorial, intended for a broad audience, presents the state of the art in RBNs, spanning over several lines of research carried out by different groups. We focus on research done within artificial life, as we cannot exhaust the abundant research done over the decades related to RBNs.
I can add a display that shows this:
Got that working
Rewrote so that there is an evolve without a fitness test. Trying to set up transition patterns like this:
The thing is, I don’t see how the K part works here…
An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.
Tried making it so K can be higher than N with resampling and I still can’t keep the system from converging, which makes me think that there is something wrong with the code.
Still working on getting the algorithm right. Fitness is calculated by get_value(), but the history needs to be the genome
In mathematics, a finite field or Galois field (so-named in honor of Évariste Galois) is a field that contains a finite number of elements. As with any field, a finite field is a set on which the operations of multiplication, addition, subtraction and division are defined and satisfy certain basic rules. The most common examples of finite fields are given by the integers mod p when p is a prime number.
Send reviews to Antonio – done
Back to work on the physics model. Make sure to include a data dictionary mapping system to support Bruce’s concept
Generally, fixed a lot of bugs in the code, but I think I understand the NK model thing. I do want to try and find how they did the traveling salesman problem
AI/ML Meeting
NASA? Air Force(?) are putting together a reinforcement learning model for autonomous spacecraft control, that requires a simulator.
As an aside, I think that an NK model can be modified to use backpropagation rather than mutation. That could be interesting.
Ok, here’s everything working the way I think it should work, but I’m not sure it’s right….
Need to get back to Antonio about authorship and roles. I think that it makes sense if he can get a sense of what – done
Discovered the trumptwitterarchive, which is downloadable. Would like to build a network of the retweets and tagging by sentiment, gender and race.
Code review with Chris. Unfortunately, it was more like an interrogation than a tour. My sense is that he was expecting us to ask questions and we were expecting a presentation.
Talked to David about his experience in the dungeon and with the map on the way down to Skyline Drive yesterday. It didn’t work for him, and I asked hip to write up his thoughts, particularly:
How you approach problems (quiet, listen, if the point bets mentioned by someone else, that’s sufficient)
A brief description of your experience (learning that you don’t talk to goblins, etc)
A paragraph that talks about your reaction to the map during gameplay. Why you think it didn’t work, and the observations of how the other players seemed to use it based on questionable assumptions?
Dissertation. Finished up the first pass at Bacharach
Started Kaufmann. Tried to get Mathpix to work, but NVIDIA is grabbing the ctrl-alt-m and it seems I can’t stop that
Working on adding network visualization to timeseriesML, which we’ll need for visualizing cluster membership changes, and I need it for NK models
Writing up some code that will produce visualizable NK Models, which doesn’t seem to be out there?
That map laid the groundwork for her various M. C. Escher-esque Temples of Time, which connect spacial and temporal history. They give readers a chance to walk into a room, moving through time and space as if entering a virtual reality:
More Bones in a Hut. Found the online version of the Hi-Lo chapter from BIC. Explaining why Hi-Lo is different from IPD.
More reaction wheel modeling
Get flight and hotel for July 30 trip – Done
So this is how you should install Panda3d and examples:
First, make sure that you have Python 3.7 (64 bit! The default is 32 bit). Make sure that your path environment points to this, and not any other 3.x versions that you’re hoarding on your machine.
pip install panda3d==1.10.3
Then download the installer and DON’T select the python 3.7 support:
Finish the install and verify that the demos run (e.g. python \Panda3D-1.10.3-x64\samples\asteroids\main.py):
That’s it!
Discussed DARPA solicitation HR001119S0053 with Aaron. We know the field of study – logistics and supply chain, but BD has totally dropped the ball on deadlines and setting up any kind of relationship with the points of contact. We countered that we could write a paper and present at a venue to gain access and credibility that way.
There is a weekly XML from the FBO. Downloading this week’s to see if it’s easy to parse and search
BP&S is “on hold” in ArXiv. Hoping that it’s overlap with DfS. I took the mapping text out of the DfS paper and resubmitted. Once that’s done I can send Antonio a link and get advice.
Code review with Chris
Contact David and see if he’s ok with July 23 – Nope. Trying Aaron M. as a replacement
More dissertation. Folded in most of the BP&S paper
Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11,12,13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure–property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.
Well, this is looking a lot like the way I would have written it
You can convert a NodePath into a “regular” pointer at any time by calling nodePath.node(). However, there is no unambiguous way to convert back. That’s important: sometimes you need a NodePath, sometimes you need a node pointer. Because of this, it is recommended that you store NodePaths, not node pointers. When you pass parameters, you should probably pass NodePaths, not node pointers. The callee can always convert the NodePath to a node pointer if it needs to.
Huh. It looks like there is no support for procedurally generated primitives. Well, I know what I’m going to be doing…
Origin – done
Grid
Cube (x, y, z size), color (texture?), Boolean for endcaps
Cylinder (radius+steps, length), color
Sphere (radius+steps), color
Skybox (texture)
Then try making a satellite from parts
JuryRoom Meeting
A lot of discussion on UI issues – how to vote for/against, the right panel layout, and the questions that should be asked for Chris’ study
Read and commented on Shimei’s proposal. It’s interesting to see how she’s weaving all these smaller threads together into one larger narrative. I find that my natural approach is to start with an encompassing vision and figure out how to break it down into its component parts. Which sure seems like stylistic vs. primordial. Interestingly, this implies that stylistic is more integrative? Transdisciplinary, primordial work, because it has no natural home, is more disruptive. It makes me think of this episode of Shock of the New about Paul Cezanne.
Working on getting BP&S into one file for ArXiv, then back to the dissertation.
Flailed around with some package mismatches, and had a upper/lowercase (.PNG vs. .png) problem. Submitted!
Need to ping Antonio about BP&S potential venues
The Redirect Method uses Adwords targeting tools and curated YouTube videos uploaded by people all around the world to confront online radicalization. It focuses on the slice of ISIS’ audience that is most susceptible to its messaging, and redirects them towards curated YouTube videos debunking ISIS recruiting themes. This open methodology was developed from interviews with ISIS defectors, respects users’ privacy and can be deployed to tackle other types of violent recruiting discourses online.
In order to serve as an antidote to extremist messages, counter-messages (CM) are placed in the same online environment as extremist content. Often, they are even tagged with similar keywords. Given that automated algorithms may define putative relationships between videos based on mutual topics, CM can appear directly linked to extremist content. This poses severe challenges for prevention programs using CM. This study investigates the extent to which algorithms influence the interrelatedness of counter- and extremist messages. By means of two exemplary information network analyses based on YouTube videos of two CM campaigns, we demonstrate that CM are closely—or even directly—connected to extremist content. The results hint at the problematic role of algorithms for prevention campaigns.
Downloaded and ran the installer. It couldn’t tell that I had Python 3.7.x, but otherwise was fine. Maybe that’s because my Python is on my D: drive?
Ran:
pip install panda3d==1.10.3
Which worked just fine
Had to add the D:\Panda3D-1.10.3-x64\bin and D:\Panda3D-1.10.3-x64\panda3d to the path to get all the imports to work right. This could be because I’m using a global, separately installed Python 3.7.x
Hmmm. Getting ModuleNotFoundError: No module named ‘panda3d.core.Point3’; ‘panda3d.core’ is not a package. The IDE can find it though….
In a very odd sequence of events, I tried using
from pandac.PandaModules import Point3, which worked, but gave me a deprecated warning.
Then, while fooling around, I tried the preferred
from panda3d.core import Point3, which now works. No idea what fixed it. Here’s the config that I’m using to run:
Nice performance, too:
And it has bullet in it, so maybe it will work here?
Got a desk reject from JASSS. Finding a home for this is turning out to be hard
Adjust Belief Places and Spaces for a straight ArXiv submission (article, endquote, fix cites). I’m doing this partially out of spite – I don’t want to see JASSS looking back at me in my svn repo. But I also need to get all the parts fixed so that it can be folded into the dissertation. \citep doesn’t play well, and I need to replace all the quotes with \enquote{}.
Start folding BP&S into dissertation
Look for Collective Intelligence venue?
Updated Pandas, which was where I got hung on Tuesday. Now I can use DataFrame.to_numpy() instead of Dataframe.values
Continuing on TimeSeriesNormalizer – done! Below is the original file with the data in columns (left) and the normalized file with the data in rows (right):
Continuing with the ICML 2019 Tutorial: Recent Advances in Population-Based Search for Deep Neural Networks. Wow. Lots of implications for diversity science. They need to read Martindale though.
Quality-Diversity optimization is a new family of optimization algorithms that, instead of searching for a single optimal solution to solving a task, searches for a large collection of solutions that all solve the task in a different way. This approach is particularly promising for learning behavioral repertoires in robotics, as such a diversity of behaviors enables robots to be more versatile and resilient. However, these algorithms require the user to manually define behavioral descriptors, which is used to determine whether two solutions are different or similar. The choice of a behavioral descriptor is crucial, as it completely changes the solution types that the algorithm derives. In this paper, we introduce a new method to automatically define this descriptor by combining Quality-Diversity algorithms with unsupervised dimensionality reduction algorithms. This approach enables robots to autonomously discover the range of their capabilities while interacting with their environment. The results from two experimental scenarios demonstrate that robot can autonomously discover a large range of possible behaviors, without any prior knowledge about their morphology and environment. Furthermore, these behaviors are deemed to be similar to handcrafted solutions that uses domain knowledge and significantly more diverse than when using existing unsupervised methods.
viztales 
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