Stanford AI Class Finished

Stanford AI class is over. I was very interesting and I learned a lot new things. Unfortunately I sucked at the first 2 questions of final exam and my score is not so high as I would like it to be. But there will be new interesting courses in spring - I will take my "revenge" there. So far I have signed up to Machine Learning, Game Theory and Design and Analysis of Algorithms courses and I hope I will have enough time for that. :)

Understanding Pac-Man Ghost Behavior

A very interesting article about the ghost AI in the world famous game.

Google AI Challenge. Final.

So the Google AI Contest is over. I have finished in 150 place, which is good, but could be better :).

The winner - bocsimacko - has shared his algorithm and source code in his blog. The same was done by the runner up - _iouri_ - here. As the two best algorithms are revealed, I will not bother with revealing my :) and start with homework - now the task is to go through at least one of them and understand all the ideas discovered and implemented.

I am looking forward to the next Google AI Challenge and this time I will prepare thoroughly to get the high place in it.

Google AI Challenge. Continued.

Yesterday's 6 hours of coding and implementing the new version of my bot, that killed the previous one on every of 100 testing maps, are not wasted - I am on the 14^th place with ELO 3335.

Google AI Challenge

University of Waterloo Computer Science Club organized AI Contest, sponsored by Google. Contestants are asked to create a bot that plays PlanetWars game, which is based on GalCon. The game field consists of several planets, that are occupied by one of the players or neutral. Planets produce ships (bigger planets do it quicker) players use to conquer new planets. The goal is to beat the other player.

I am also taking part in the tournament and, francly speaking, doing well. Currently I am around 200th place (username: 2stupidogs) in total ranking and one of the best in Estonia. Here are some strategy thoughts I can share with you as the starting point. These were used by my first 2 bots. (Now I have implemented a more deeper algorithm.) The organisers provide everyone with default strategy bot, that can we further improved. And by making the following small improvements you can make top 500 easily.

Default bot finds his strongest planet and sends half of the fleet from it to the near planet, that it considers to be weak.

1. First change is to send as many ships as needed to conquer the planet, not just the half.
2. Then change the planet score formula (Determines how weak enemy planet is). It should depend on distanse, growth rate and current fleet on the planet. For example, count in how many days the new planet will regenerate the fleet you had to use to conquer it. The less it takes, the better the planet for you.
3. Don't send fleet to the planet where is was sent already. (Well, this is doubtable, but you can try.)
4. Make a list of weakest planets for your strongest planet (not just the one weakest planet), and send a fleet to all of them as soon as you have enough ships.
5. Make a list of your strongest planets and look for weakest planets for every of them.

Just implementing this strategy I managed to make into top 350 list, but it was less contestants then.