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1) What is the source data for that plot?
2) You specify "lighter = better", but how are they normalized across games and algorithms? How is better and worse quantified to get a "lightness"?
Edit: Found #2 in the second paper. Still don't know what 25 wins is white and 0 wins is black means? How do you "win" some of these?
Two papers are here:
While interesting, this research is not on the path to "true" artificial intelligence. It may solve playing a majority of video games given enough effort, but that is not the author's stated goal. Why do I think this? The representations of the various algorithms are not unified. The answer to the question "what algorithm lies halfway between JinJerry and YOLOBOT" is difficult to answer. This is because they are disc…
In the end I could see a scenario where all strategy algorithms are ran in parallel with some understanding of the number of turns required for each strategy choice to provide payoff. That net can then weigh the choices and choose the relevant strategy algorithm for a period of time until it deems changing strategy algorithms is appropriate.
Things can become even blurrier if the net can choose components from multiple algorithms (depending on the game and the strategy). There are a few whole directions I'd want to go with this work if I were pursuing it.
Togelius' work ignores a core paradigm, of which we are aware of the human brain; neuroplasticity.
If one of togelius' solvers fails, all bets are off, the artilect is lobotomized.
See deepmind's "self-encoding" work: http://www-personal.umich.edu/~reedscot/iclr_project.html
On the grid graph comparison of how different algorithms perform on different games, two questions: 1) What is the source data for that plot? 2) You specify "lighter = better", but how are they normalized across games and algorithms? How is better and worse quantified to get a "lightness"? Edit: Found #2 in the second paper. Still don't know what 25 wins is white and 0 wins is black means? How do you "win" some of th…
This is a great post. (EDIT: Great Blog all around!). The grid of how different algorithms perform is particularly nice. I would love to see how that grid with new algorithms (specifically those new algorithms that make use of multiple algorithms). Also, the name hyper-heuristics sounds like a recently made up term to stand out in search queries. The more long established name for it has been meta-heuristics (which i…
The difference between meta-heuristics and hyper-heuristics is that the former is a much broader concept, including such things as genetic algorithms. Hyper-heuristics is specifically about selecting among heuristics, which excludes e.g. evolutionary algorithms. Graham Kendall, one of the inventors of the concept, explains that they would have called it meta-heuristics if that name was not already taken.
Great post. OT: I accidentally swipe left/right every time I'm on blogspot, taking me to the previous/next post. Maybe I'm just fat-fingering all around, but this is a horrible UX.
This is a great post. (EDIT: Great Blog all around!). The grid of how different algorithms perform is particularly nice. I would love to see how that grid with new algorithms (specifically those new algorithms that make use of multiple algorithms). Also, the name hyper-heuristics sounds like a recently made up term to stand out in search queries. The more long established name for it has been meta-heuristics (which i…
Thanks, appreciated! The difference between meta-heuristics and hyper-heuristics is that the former is a much broader concept, including such things as genetic algorithms. Hyper-heuristics is specifically about selecting among heuristics, which excludes e.g. evolutionary algorithms. Graham Kendall, one of the inventors of the concept, explains that they would have called it meta-heuristics if that name was not alread…
On the grid graph comparison of how different algorithms perform on different games, two questions: 1) What is the source data for that plot? 2) You specify "lighter = better", but how are they normalized across games and algorithms? How is better and worse quantified to get a "lightness"? Edit: Found #2 in the second paper. Still don't know what 25 wins is white and 0 wins is black means? How do you "win" some of th…
The source of data is a lot of simulations we ran ourselves. All of the games can be won or lost - there's also a score measure as well, but that was not used in producing that graphic.
Glad you submitted this as I've needed an update of game AI field. A bit different than it was in my day but still shows what I argued all along: hybrid methods will be the best. Far game generation, I agree that generating many new, unpredictable experiences for the algorithm is ideal. I also agree it's within computational reach. Doesn't necessarily take AI, though. For instance, much of that could be done with dec…