Herself’s Artificial Intelligence

By tapping directly into the brain’s electrical signals, scientists at John’s Hopkins University, in Baltimore, are on their way to developing a prosthetic hand more dexterous than ever before. They have demonstrated for the first time that neural activity recorded from a monkey’s brain can control fingers on a robotic hand, making it play several notes on a piano.

“We would hope that eventually, we’ll be able to implant similar arrays permanently in the motor cortex of human subjects,” says Mark Schieber, a neuroscientist at the University of Rochester, in New York, who is working on the project. However, researchers caution that a practical human version of the neural interface is still a long way off. . . .

. . . To make the neural interface, researchers recorded brain-cell activity from monkeys as they moved their fingers in different ways. (A particular part of the motor cortex has previously been shown to control finger movement.) The scientists then created algorithms to decode these brain signals by identifying the specific activity patterns linked to particular movements. When the decoding system was connected to a robotic hand and fed new neural-activity patterns, the fingers on the hand performed the intended movement 95 percent of the time. . .

More information
Playing the piano with a robotic hand

Designing AI Algorithms for Turn-Based Strategy Games

In action games the AI opponent always has the natural advantage: perfect accuracy and lightning fast reflexes, so the challenge in designing the AI for those games is making it act more human and to be beatable.

In turn-based strategy games the tables are turned. Speed and accuracy are no longer important factors and the cunning and intuition of the human player will easily out match any AI opponent. In fact, it’s nearly impossible to design a AI that can beat an experienced player, but that is not really the point anyway.

The challenge is to make the AI’s attack and defense strategy to appear intelligent and thought out, providing a challenge but letting the player win in the end. Once the player has familiarized himself/herself with the tactics of the AI the game rapidly gets boring, so a certain amount of unpredictability is desirable. . . .

I ran across this article while looking into predator prey chases and thought it might be of interest to some of you.

See also:
Simple predator prey chase algorithms
Predator prey chases using potential functions

Poindexter made prediction markets common knowledge when he created his market to predict terror attacks. Prediction markets work like stock exchanges. Lots of people buy stock in what is expected to do well, and sell off what is expected to do poorly. They have been used to predict political and sports event results among other things.But as those of you know who watch the stock market, mob intelligence is often more like lemming behavior than pooled intelligence. That said, many well known, successful companies have been successfully using predictive markets to decide what new products to bring forth; HP, Corning, Intel, MS, Google, WSJ and more.These markets do best when there is actual money or some other incentive to gain or lose.Online examples include:Iowa Electronic MarketsUS News Futures

See also:
Mob Intelligence

More information:
The Prediction Company
The Fizz-dom of Crowds
Google’s Lunchtime Betting Game
Putting crowd wisdom to work, Google
Here’s an Idea: Let Everyone Have Ideas, NYT
Oddhead Blog: Prediction Markets, Gambling, Electronic Commerce, Artificial Intelligence
Predictify

Papers:
Prediction Markets, Journal of Economic Research ( pdf )
Google Prediction Market Paper ( pdf)

Using potential functions for predator prey movement gives a more realistic chase than using line of sight or other line algorithms.

Potential functions are well known in physics and describe attraction and repulsion due to electricity, gravity etc.

Potential_Energy = – Attraction/distance^j + Repulsion/distance^k

A strong attraction will cause the predator to chase the prey, a strong repulsion will cause the prey to flee the predator. Either of these can be set to zero. Predators may only be attracted to prey, prey may only be repulsed by predators. Various prey and predators can be given slightly different numbers for attraction, repulsion, j, k allowing them to behave differently.

To chase or run a character using potential functions use this algorithm:

Move_Predator (){

double attraction = some_constant; // experiment with these, the size of your game
double repulsion = some_constant; // area will determine good choices
double j = some_constant; // try something between 1 to 3 for these numbers
double k = some_constant; // higher number mean smaller results
double x_predator;
double y_predator;
double x_prey;
double y_prey;

//distance between predator and prey
double distance_x = x_predator – x_prey;
double distance_y = y_predator – y_prey;

double distance = sqrt ( distance_x*distance_x + distance_y*distance_y);

//normalized distance vector
distance_x = distance_x / distance;
distance_y = distance_y / distance;

//see what our attraction or repulsion is
double potential = -attraction/distance^j + repulsion/distance^k;

//now use that attraction/repulsion to see how far we move
double move_x = potential * distance_x;
double move_y = potential * distance_y;

//new location for us
double new_x = predator_x + move_x;
double new_y = predator_y + move_y;

}

More information:
The use of potential functions on modelling animal movement
Advanced Movement Model of Crowd Robots ( pdf )

Books:
AI for Game Developers

It’s tough for military personnel to tell if a civilian has bad or good intentions if they don’t speak the same language.

So, as several technology companies work to improve translation software, the National Institute of Standards and Technology also is doing its part. NIST announced this week that its researchers are evaluating instant two-way translation systems for the Spoken Language Communication and Translation System for Tactical Use (Transtac) at the Defense Advanced Research Projects Agency.

Darpa and NIST are focused on bidirectional translations of English and Arabic spoken in Iraq. NIST, U.S. Marines, and Iraqi Arabic speakers just completed lab and outdoor tests on prototypes in Gaithersburg, Md. The evaluations included controlled background noise from speakers, generators, garage doors, running vehicles, and radio broadcasts to mimic noise in real-world situations.

More information:
Darpa, NIST Evaluate Military Translantion Computers
Timex2 – DARPA Transtac site