On January 27 2019 23:14 Haukinger wrote: It's funny how people deem a korean terran exploiting marines with superior mechanical skill the highest level of play but if an AI uses blink stalkers it's just stupid with high apm.
It's not funny. Super, human micro requires very rare talent and huge amounts of practice. Super, AI micro requires coding it without APM limits.
Strategy, metagame, balance are all built on the capacity and potential of individual units. Unit's utility is based on what a player can do with it. What a human player can do with a unit is vastly different from what a AI can do with it.
Based on that, AI uses different tactics, different strategies, almost a different game.
A human can't click 1500 in a minute, AlphaStar did.
StarCraft is a realtime strategy game where speed is very important. They made a bot which has, at crucial times, played at least 2X faster than any human can.
It's like building a sports playing bot which can run 2x faster. Suddenly, all tactics and strategies are affected and the game is almost not the same.
--- DeepMind's only mistake is that they didn't put a proper peak-APM limit.
Frankly, I would love to see games where AlphaStar is limited to 50 peak APM, then more games where the limit is 100 peak apm, then 150 etc. Would love to see the strategies and tactics used.
On January 27 2019 23:14 Haukinger wrote: It's funny how people deem a korean terran exploiting marines with superior mechanical skill the highest level of play but if an AI uses blink stalkers it's just stupid with high apm.
it's not stupid, it's just worthless if we want to know how good the AI is strategically if it has unlimited apm because then it can win with anything. If the goal would be to just beat a human no matter how there would be no need for the Deepmind team to take this on, a regular micro bot would be enough.
It is like Jasper_Ty says. The more APM available to your bot, the harder it is for it to work correctly. Who knows what tricks the AI uses, or could use, vs other 'absurd no mistake' APM agents and that it wasn't using vs Mana or TLO?
I understand it is not interesting for you personally to improve your game. But we have had this discussion in the community ever since AlphaGo how long it would take for an AI to play properly and to beat top humans. I remember discussing with programmers who talked about how difficult of a problem it is to simulate combat before they make a move. Because that is what they tried to put into the BWAPI AIs. You see a game state, you see your units, you see the opponent units, you predict how the units will move and what they will attack, you calculate how much each side will lose, and then you know if this engagement is favorable.
So when people say that they consider the AI microing like crazy 'trivial', I just have to laugh.
Personally, I would like to see an unlimited APM AI. And I would love to see an AI play SC BW, because it seems to me that SC2 is way more straightforward and SC BW is way more on a knife edge and subtle. Maybe it is me being a former SC BW player, but I have this feeling that any play by an AI is way more exploitable in SC BW than in SC2.
Honestly, this reminds me of the debate we had back when SC2 was announced and when we had this influx of AoE, Civ, and C&C players arguing that slowing down the game, or making the interface easier, would give the game a richer game of strategy. The game is not what you think it is. Imagine an AI figuring out how to play Civ3. It is just calculating which builds to build first under which circumstances. It is boring. There is one solution. You calculate what it is, and you just carry it out.
Let's not forget that unlike Go or Chess, RTS games are convergent towards the end.
To sum it up. They created AI that basicly can beat everyone with a drone rush. But strategically it has less brains than a monkey. Message to news - we beat best Sc2 players.
Alphastar is impressive, it really is. But the strategies it came up with are not that interesting because humans cannot replicate them. The best plan is just the best plan you can execute correctly. And in order to make the AI come up with strategies that are useful to humans you will need to have a good human model to "limit" the AI in the correct way.
A simple APM limit is not gonna do the trick, sometimes a player can have huge "useful" apm spikes so you will need a model that account for that and many other things.
If you limit the AI too much, the AI will have to find workarounds to it's sub optimal micro by finding strategies that are sub-optimal for a player. If you do not limit the AI enough, the AI will come up with strategies that players will never be able to execute. Not to mention that each human player is different.
Still it would be very interesting to see if the AI can come up with good/decent "very-low-micro" strategies.
That one was interesting indeed. In fact the macro part of the game is not so "apm-limited". But we do not know if that strategy is actually good for human players too because the AI is playing with different limitations. If over-probing was done by all agents on different APM limits then it would be an indication that it is a good idea to always build more probes. With "strategy" I mean the whole game strategy, you can't take out a single thing (like over-probing) and discard the rest, because everything is connected in a single game.
For example the agent could be so good at winning with stalkers micro, that the only way to beat it is somehow to kill a lot of its probes. In this condition the agent would improve its chance of winning having a less than ideal number of probes. It would still have enough stalkers to win the engagements thanks to its god-like micro, but he would not lose to other kind of attacks to the mineral line. But if he was not so good at microing then it would have better chances by expanding its economy faster.
This is just an example, I don't know if this is the case.
An agent isn’t confident in its stalker micro, as it’s training vs bots, not humans. It won’t think it can win a four vs five stalker fight vs a human player because it is used to losing to other agents in such situations.
This is a common “weakness” to AI’s: a complete inability to take the psychology of the opponent into consideration. Agents tend to converge to the same cautious, conservative style which shows maximum respect to the opponent.
E.g. if you had to play a match versus an AI, it might be the case that if you just made blink stalkers it would vastly overestimate the strength of your army, because it doesn’t know you can’t actually micro blink stalkers at 1500 APM. And as a result it wouldn’t take any engagements.
This is why chess engines have a “contempt” setting programmed into them, which forces them to make sub-optimal moves which nevertheless increase winning chances against weaker opponents. This is a must in any sort of tournament or league play in chess, since it avoids draws.
I don’t know how you would program “contempt” into AlphaStar, other than training it versus agents with handicapped APM, such that it would develop more confidence in its micro, in order to better approach match conditions vs humans. But of course this doesn’t have any scientific value, only competitive utility.
On January 29 2019 05:38 Grumbels wrote: An agent isn’t confident in its stalker micro, as it’s training vs bots, not humans. It won’t think it can win a four vs five stalker fight vs a human player because it is used to losing to other agents in such situations.
This is a common “weakness” to AI’s: a complete inability to take the psychology of the opponent into consideration. Agents tend to converge to the same cautious, conservative style which shows maximum respect to the opponent.
E.g. if you had to play a match versus an AI, it might be the case that if you just made blink stalkers it would vastly overestimate the strength of your army, because it doesn’t know you can’t actually micro blink stalkers at 1500 APM. And as a result it wouldn’t take any engagements.
This is why chess engines have a “contempt” setting programmed into them, which forces them to make sub-optimal moves which nevertheless increase winning chances against weaker opponents. This is a must in any sort of tournament or league play in chess, since it avoids draws.
I don’t know how you would program “contempt” into AlphaStar, other than training it versus agents with handicapped APM, such that it would develop more confidence in its micro, in order to better approach match conditions vs humans. But of course this doesn’t have any scientific value, only competitive utility.
You release it on the ladder, with 1000s of instances playing vs humans 24/7. Machine learning and genetic algorithms (or whatnot) would cause bots to start taking some smaller sub optimal moves, just to gauge the reactions of the opponent. As the bots have played just other bots with same 'mechanical' capabilities, the ability to gauge opponent's mechanical skill wasn't necessary.