Is the the CadiaPlayer limited to playing games like puzzles,cards and board-like games (like chess or poker) or can it handle some more complex computer games? What is the most advanced game the CadiaPlayer has succeeded in learning? And in that regard, where are it´s limits?
Have you worked on combining simulations and traditional game-tree serach as you mention at the end of the paper? How are you advancing the CadiaPlayer for it to reach it´s next level? What are your biggest obstacles for advancement?
What changed for the CadiaPlayer in the GGP competitions after 2008? Are we(HR) going to claim back "our" title this year?
P.S. What is a computer Go? (It´s mentioned a couple of times but I´m still unsure of what that is.)
1. What have the most important developments regarding General Game Playing been for the past decade? What advancements in General Game Playing and Artificial Intelligence have there been made independently of the increase in processing capabilities of computers.
2. The UCT Game Tree algorithm is used to keep track of the average return of simulations—could the aggregated metainformation of billions of moves (pertaining to a position in time, types of moves; in chess: “rook moved”, “promotion”, “castling” etc.) at a higher level be used to find some correlation for actions that are generally better than others and use that to help improve decision making?
Is it correct to think that if a program can learn strategies for a game it can also learn strategies to solve real-world problems? What about the game specification then: is there any chance to get them evaluated automatically?
Your contribution was innovative for GGP. Concerning current and future works, are you fully satisfied with this approach (Monte Carlo) and then you focus in get it better or do you have also other innovative ides?
If I have to implement an agent for a game, will a GGP be better than a game specific AI?