Computer Science and Biology 361
This isn't particularly good or even described well, but it does reflect my current thoughts on my final project. Perhaps with your critique and commentary, I can refine my thoughts and put together a nice project.
--A series of simple NetLogo models that illustrate an emergent explanation for complicated social behavior--
Some potential models include:
#1. Antagonism between 2 races, with one racial group having superiority in number and resources over the other.
- For most individuals of the superior race (red), they would rather live almost entirely with people like themselves (the exact proportion can be on a slider)
If you'll recall, we had a discussion yesterday in class about the "saddle" pattern seen in testing a range of input values (x & y each from 0 to 1) for the xor problem. There was some question as to why the saddle always ran like "/" rather than "\" Basically, the center range of x & y insisted on returning high values, even when Doug added training data for (0.5,0.5) to return 0.
My intuition was that it had something to do with the calculations involved in adjusting the network's weights during back-propagation. Math isn't my forte (unfortunately, for a comp sci major!), but I'm pretty sure I've confirmed that the backprop algorithm is biased for answers of 1 over 0. Let me know if there's a hole in my logic, here.
A long time ago, in a galaxy not too far from here, Lindsay Gold brought up the game of "Spore" which is to be released by Electronic Arts in the fourth quarter of this year. I thought that I'd continue that conversation with a new post and a couple of new links.
A quick review: Spore is a computer game modeled on evolution, except that there is human interaction. You guide a "spore" (yes, I know that the term is inaccurate) through evolution. As you go along, you can add features to yourself, and your technology progresses as well. You can even acheive space flight!
So for those of you interested, here are a couple links:
During Monday's lecture
Professor Blank told us about a split into two separate approaches to studying artificial intelligence. The split is between the rational models and emergent models.
Professor Blank writes that "These two paradigms, in my opinion, have little to do with one another. That is, emergent models can certainly show rational, rule-like behavior. But the implementation of emergent models have nothing to do with how rational models operate."
I don't understand how this dichotomy is possible! I agree that emergent models can show logical "rational" behavior. We need only look to Langton's Ant and look at its rule like behavior when building its road to believe it.
Although, as Doug pointed out in class, there are important differences between connectionist networks on computers and neurons in the brain, I still think that the name "Neural Networks" is justified by the fundamental similarity between the two.
This fundamental similarity, which I think is very important and which also implies many promising possibilities for simulated neural nets--based on phenomena in the brain--that have not yet been explored in AI research, ...this fundamental similarity is the way in which information is manipulated through destroying some info and then copying the result and distributing it to create new info. That is, when the activations of input nodes in a computer network are summed in a node, which node each of those signals came from is lost. But the information that remains--the combined strength of those inputs--is then copied and distributed to nodes in the next layer, where the process is repeated. I think that this method of information manipulation is an incredibly important concept in and of itself; therefore, we should acknowledge connectionist networks' debt to the structure of the brain. Furthermore, I think there is (or will eventually be) a lot more we can do with neural nets based on observations of how the brain works.
I know this class is about emergence, but since we're frequently dabbling in the area of artificial intelligence, I feel like I can't pass up the opportunity to post this. I came across this site last year and recently rediscovered it today. It's an interesting chess-playing AI applet called the Thinking Machine 4
. When it's the computer's move, it searches the board for the best move; what makes this interesting is that while the computer is "deciding" it's move, it actually maps the possible moves and counter-moves with colored lines. It reminded me of the idea of how an agent should determine how its actions change the world around it--which is something we discussed in Intro to AI. As the gameplay progresses, it's evident that the program makes more specific moves. I don't know if I would quantify this as intelligence though. Could it be that the search tree the program is traversing is becoming smaller? The method by which a typical AI program uses to quantify a 'good' move doesn't usually change during a game--to my knowledge at least. With that said, it's hard for me to say if the moves are becoming 'intelligent' or if the 'good' moves are just a product of the current state of the board. On a more technical note, the programmers used something the quiescence search method
in conjunction with alpha-beta pruning
in order to cut down on the size of the search tree. It's pretty neat, so check it out. Enjoy!
Today, one of my favorite biologist bloggers pontificates on Conway's Game of Life, agency, and the biologist's job of finding patterns.
Pharyngula explores the development of complexity in Chance and regularity in the development of the fly eye
. When Pharyngula (PZ Myers) argues against a magical interpretation of the unfolding of events, he is talking to the "intelligent design" proponents who would point to this development and claim that it must be the act of a designer.
I thought I would link you all to the website I demonstrated Monday in my report about Blink
This is the Project Implicit
website, where you, too, can spend time freaking out about how prejudiced you are, probably about groups that include yourself. (For example, I happen to "fail" the Gender-Career test miserably.)
In computer graphics demos there is a fairly common method of rendering water ripples based on a CA; each cell has a height value, and every frame its height value is set to the damped inverse of its previous height value + the average height of its neighbors. This results in fairly realistic-looking ripples that propogate outwards as expected.
A tutorial for achieving this effect can be found here
In my game I am implementing a fairly similar effect for water ripples
Only because Pi has come up so frequently in class, I thought this article
on the history of Pi might be of interest.