Page 170 - AWSAR 2.0

146 || AWSAR Awarded Popular Science Stories - 2019
But, there is a catch here. The seeker does not know the hider distribution. So, what can he do? We gave a statistically simple idea to the seeker:
See, you don’t know anything about hider distribution. So, equal importance is given to every box. This makes each box receiving 1/n probability and forms the uniform seeker distribution. One safe assumption is that the hider will always select locations using his distribution for which the probability is more than 1/n. Let’s call the set of locations for which the probability is 1/n as “good partition” and the rest as “bad partition”. Let’s say this good partition forms the “p” proportion of the n. Now, uniformly select a bunch of locations (“s”) at once to find at least one location from the good hider partition with very confidence (“c”). Repeat this process until you find a hider.
Now, the seeker asked us: But, how do I decide this “p”? How do I know how many boxes I should draw uniformly
at once?
We said, If you still have no idea about that. Go with p = 50% and a confidence of 95%. (In our research, we prove this bound relation among s, p, and c, which is independent of n.)
The Plot
The applicability of the
theory was studied extensively
on 73 biochemical problems
directly related to drug
discovery. These problems
were obtained from the
National Cancer Institute under
the National Institute of Health,
United States. The goal was
to construct machines that can automatically predict whether a biochemical compound has the potential to either inhibit or kill cancer cells. As mentioned earlier, our ILP engine uses
the hide-and-seek theory to generate good features essential for a neural network. Good features are those that distinctively express the compound concerning their resulting property: inhibiting or killing. One such feature is found to be of very high discriminative accuracy, of 90%, on known data of various chemical compounds:
The compound has a single bond, a double bond, two benzene rings connected, a fused ring, and a hetero-non-aromatic structure.
We can see that such a descriptive analysis would help to construct neural networks that have the potential to outperform neural networks built with only atom-bond descriptions of a chemical molecule. Using the hide-and-seek approach, we were able to construct very good features efficiently describing the chemical molecules. The neural networks built with these hide-and-seek-based
features are evaluated for all the 73 different problems.
The Climax and the Sequel
The neural networks built with the hide-and-seek-based features proved themselves winners against the neural networks built only with the atom- bond descriptions available for chemical compounds. One remarkable result is that 500 good features are sufficient to learn powerful predicting machines, which have far higher accuracies than the machines built with thousands of atom-bond features as used in the state-of-the-art machines.
This result makes the applicability of a simple game-theoretic formulation very useful for the whole community of Artificial Intelligence as a whole.
Let’s assume that the hider
distribution is known to the seeker and the seeker knows that his performance will be bad if he uses the same hider distribution. So, he constructs a distribution in which the locations for the hider’s selection probabilities are low get higher importance and the locations for which the hider’s selection probabilities are high gets lower importance.