Herself’s Artificial Intelligence

Sparse distributed memory first appeared in 1998 as a model of long term memory in humans. The main idea is that distances between concepts in our brains can be represented as distances between points in a high dimension world. Since distances between points are far apart in many dimensions, the distance between concepts is large. The large distance between concepts means I only have to come closer to it than another idea for a match to be made.

For example if I give each letter of the alphabet its own dimension then map it by position in a word then ‘aeple’ is closer to ‘apple’ than ‘ample’ and I can guess that the correct word for the mistyped word. Or a four legged creature that is tall and is spotted is closer to a giraffe than a short legged spotted leopard.

This type of storage of data means you can store far fewer examples you need to match allowing you to store more information in a much smaller memory footprint.

All input is represented in binary form in sparse distributed memory. The algorithm works by calculating the ‘Hamming distance’ between data input and existing examples in memory.

Books:
Reinforcement Learning: An Introduction (pdf download )

More information:
Sparse Distributed Memory
Kevin Kelly ‘Out of Control’ Chapter 2
Sparse Distributed Memory and Related Models Chapter 3 (pdf)

Papers:
Kanerva’s Sparse Distributed Memory An associative memory algorithm well suited to the connection machine. (pdf) ( exellent starting point )
Kanerva’s Sparse Distributed Memory, Multiple Hamming Thresholds ( pdf )

All games and all competitions can be represented by trees. Each node represents a place to make a decision, each edge represents a decision that can be made from that node. One of the simplest games we all know is tic-tac-toe. The game tree for tic-tac-toe has an root node with 9 edges. Each edge represents one of the 9 positions you can play if you are player one. The second level of nodes each have 8 edges. Each edge represents one of the remaining 8 positions open to player 2. The paths through the game tree for tic-tac-toe = 9! or 362,880 possible ways to play the game through to completion. So while tic-tac-toe is with in the realm of games the computer can fully solve while playing most games are not. One method used in artificially intelligent games to solve this problem is backward induction.

For example: You get a job transfer to Mars for 10 years. There are two possible jobs on Mars open to your spouse. One job pays $60/year, one job pays $100/year. You know that at the end of 10 years you will both be shipped back to Earth.

A lottery is held every year for the job openings, 1/2 are for the $60/year job, half are for the $100 a year job. Once you commit, that is your job until the trip home. At which point in time should your spouse stop holding out for the $100/year job and take the $60/job?

In year 10 the possibilities are $100/$60/$0 so the $60 job should be accepted.

In year 9 the possibilities are $200 for the $100/year job; $120 for the $60 year job. But if the spouse holds out there is a 50% chance of either. So 50%( $100 + $60 ) = $80. We have then $200/$120/$80. Take the bad job.

In year 8 we have possible earnings of $300/$180 or ( 50% * ($200 + $120 ) = $160 ). The bad job is a better choice than holding out.

In year 7 we have possible earnings of $400/$240 or ( 50% ( $300 + $180 ) = $320 ). Since the $320 is higher than the $240 we should hold out.

In year 6 and earlier it will be better to hold out and hope to do better in next years job lottery, in year 8 on it is better to take either job.

Now there are some big assumptions here. One is that everyone has all the information needed to make the decision. And two that your spouse won’t go stir crazy sitting on Mars with nothing to do. Another assumption which can lead to trouble is that backward induction assumes the players do not collude with each other.

In games you would use the final payoffs for each player to do your backward induction. In business backward induction is commonly used to guess what the competition will do in response to your moves in the market. While backward induction has been very successful in AI and in politics it won’t solve all game tree problems because we don’t always have all the information and there’s more to life than a paycheck.

Papers:
Is good algorithm for computer players also good for human players?

Synthetic psychology is a field where biological behavior is synthesized rather than analyzed. The father of such behavior Valentino Braitenberg ( home page ) did some interesting work with toy vehicles in the 1986 book “Vehicles: Experiments in Synthetic Psychology“.

The Braitenberg vehicles were simple toy cars with light sensors as headlights. In some cars the wire for each headlight went to the real wheel directly behind it, in some the wires went to the opposite back wheel. The headlight receptors were aimed slightly off to the outside. The more light received by a receptor the faster the wheel wired to that receptor would turn.

Each vehicle exhibited realistic behavior when placed in a plane with several randomly placed light sources. A vehicle wired straight back when placed near a light source will then rush towards the light veering off as it gets closer to the light. As it gets more distant from the light sources the vehicle slows down. The reason is the wheel receiving the most light spins fastest turning the car away from the light source.

The vehicle with the crossed wires will turn and drive towards the brightest light in its field of view. The closer it gets, the faster it goes eventually running into the light.

Pretty interesting behavior from a very simple machine. But what if we add in a neurode to each wire and instead of a plain wire we use a wire that inhibits signals? Neurodes are set to only fire if they receive signals over a certain threshold. In this case zero is to be our threshold. So now our cars send signals to the wheels if there is no light, and do not send a signal if there is a light. Now the vehicle with the wires straight back moves toward a light and slows as it approaches, facing the light. The second vehicle now avoids light sources, speeding off to the darkest corner it can find.

So what has this all to do with current artificial intelligence? Some of our best stuff right now came from earlier work that was done and stopped. Some of our best mathematical algorithms come from extremely early math. And to remind you ( and me ) that simple rules can create very complex behavior in game characters and artificial life forms.

Four neurode versions of these vehicles have been built and they will exhibit more complex, non-linear behavior. “The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” (Edsger Dijkstra) At what point does the behavior become realist enough to be considered a life form?

Source Code:
Java simulation of Braitenberg Vehicles

More information:
Braitenberg Vehicles ( Java and Lisp simulators here )
Another simulation and pseudocode code here

Papers:
Robotics as an educational tool ( CiteSeer )
Swarm modelling. The use of Swarm Intelligence to generate architectural form. ( pdf)