Herself’s Artificial Intelligence

This is an algorithm where an artificial intelligence interacts with the environment, receives either positive or negative feedback and adjusts its behavior due to this reinforcement.

Reinforcement learning is usually used to solve Markov decision problems. Markov decision problems have a set of states; possible actions for each state; probabilities and rewards for each action; and a goal for the artificial intelligence to reach.

We start by setting up an environment of states. The artificial intelligence then repeatedly explores our states and will learn an optimal method of finding a path from start to goal that gains it the most rewards. Think of a game character starting on one square of a grid and trying to get to the final square. Between the start and end square are squares filled with gold and traps, like a Wupus World game board. Reinforcement learning is used in control problems for robotics, not just in games.

Learning can be supervised or unsupervised.

One popular unsupervised reinforcement learning algorithm is the Q Learning Algorithm.

Start:
Loop:
For current state select an action
Collect reward or cost
Update Q Value
Move to new state
End Loop:

Update Q Value
Q-Value += Learning_Rate ( Reward_New_State + Discount_Factor( Max_Q_New_State – Q-Value ) )

Move to new state
Use either Greedy algorithm or a Probabilistic Greedy which calculates best choices and randomly chooses one of the better choices.

This algorithm can be slow and memory intensive but has been used successfully in game problems, scheduling problems, and robotic problems.

See also:
Hidden Markov Models
Why Reinforcement Learning is Important
Reinforcement Learning (CASTrader Blog)
Wiki, Reinforcement Learning
Wiki, Q Learning

More information:
Ms. Pac-Man Plays Herself

Computers can usually out-compute the human brain, but there are some tasks, such as visual object recognition, that the brain performs easily yet are very challenging for computers. The brain has a much more sophisticated and swift visual processing system than even the most advanced artificial vision system, giving us an uncanny ability to extract salient information after just a glimpse that is presumably too fleeting for conscious thought. To explore this phenomenon, neuroscientists have long used rapid categorization tasks, in which subjects indicate whether an object from a specific class (such as an animal) is present or not in the image.

Now, in a new MIT study, a computer model designed to mimic the way the brain itself processes visual information performs as well as humans do on rapid categorization tasks. The model even tends to make similar errors as humans, possibly because it so closely follows the organization of the brain’s visual system.

More Information:
First Impressions: Computer Model Behaves Like Humans on visual Categorization Task

Researchers from CNRS and the Spanish foundation Fatronik have designed a manipulator-type robot that is twice as fast as all existing robots. To achieve this feat, which has not gone unnoticed by industry, they have worked on optimizing both the shape of the robot and its control system. The robot, baptized Adept Quattro from the name of its manufacturer and because of its four arms, has just been launched on the market. Several hundred orders have already been placed, among others, by the food processing, health and beauty industries.

More Information:
Fastest Industrial Robot on the Market