Maybe all those great minds will rub
off on you -- just sitting here.
Charlie smiles. Then... a thought sparks --
FLASH CHARLIE VISION
PHOTOS of EINSTEIN, HEISENBERG, SHRODINGER, CURIE, etc. (with
an overlay of NAMES and IMAGES OF THEIR ACCOMPLISHMENTS --
i.e., E = mc^2, the Uncertainty Principle, Shrodinger's Cat, etc.)
float through space and come to rest in a FAMOUS 1927 PHOTO
of them gathered and seated for a portrait, as we MATCH INTO:
Charlie -- pulling the SAME HISTORIC PHOTO from his box of stuff --
...That's it, Dad. I've been dancing
around the idea of neural networks.
Maybe what I'm looking for has been
here in front of me the whole time --
a historical neural network.
"Great minds think alike."
Einstein's general relativity tells us that matter and energy bend spacetime. One of the best examples of this can be
seen with "gravitational lensing." Lensing occurs when the path of light from a source (or in this case a grid
of point sources) travels near a massive or energetic object, called a "lens," and bends due to the
mass/energy of the lens. In this Demonstration the massive object is an infinitely small point of variable mass, marked
with a brown dot.
So what've you got?
(off his blank stare)
You haven't even started yet?
(thinking on his feet)
In terms of analyzing their M.O.,
classic game theory -- maybe a "Payoff
Matrix" -- should do the trick.
In conventional non-cooperative game theory, each player can see and can instantaneously select any element of its
strategy set in response to the other players' strategy selections. In real settings, however, the strategies available
to a player at any given time will often be a function of the strategy it selected at a prior time. For example,
changing only one aspect of a strategy at a time may be possible. Sometimes these constraints on the dynamics of
strategy selection may be the result of external circumstances or cognitive limitations on the part of the player; other
times they may be deliberately engineered by the player itself.
Even if we make a key, that still
doesn't tell us where the box is.
There are jewelry wholesalers all
over the city, Charlie.
Path Minimization should help us there.
...analyzing the most efficient
routes from point A to point B.
(off Charlie's look)
Hey, some of this stuff sticks.
You are a traveling salesman. Your task: visit the cities (represented as dots on the gameboard) one by one by clicking
them. Start anywhere you like. You will trace out a route as you proceed. You must visit every city once and then return
to your starting point. The goal is to find the shortest possible route that accomplishes this. Your total distance is
recorded at the bottom of the panel, along with the total distance of the best route that Mathematica can find.
I thought Path Minimization would be
the easy part.
What's the problem?
I'm using Djikstra's algorithm,
overlaying it with CalTrans data on
traffic flow... It's not working.
A government wants to construct a road network connecting many towns. Suppose each road must connect two towns and be
straight. Kruskal's algorithm gives the least expensive tree of roads. Allowing nodes that are not towns leads to a
different problem involving soap bubble theory.