6 ms·

Any predictions for how long it will take for an AI to win against the world's best player?

• ktRolster 9y ago
  AI can already win against the world's best player now. It's just a question of whether they can win with human-level micro.
  • obastani 9y ago
    Do you have a citation? As far as I know, existing AIs do not stand much of a chance again humans [1]. My understanding is that even if AIs can beat humans is small skirmishes due to superior micro, they lose so badly at macro that it simply doesn't matter. [1] http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/custom-ai-programs-take-on-top-ranked-humans-in-starcraft http://spectrum.ieee.org/automaton/robotics/artificial-intel...
    • sidusknight 9y ago
      Yeah, you're right. Not sure what the guy is talking about.
      • keerthiko 9y ago
        > the guy is talking about I don't want to be overly semantic or PC on HN, but just saying the GP may be female, judging by their name on profile. Being misgendered could be very offputting and discourages participation, so you may have wanted to say "this person" even if it doesn't sound as offhand as you'd have liked it to come across.
        • cm2012 9y ago
          In the NorthEast US, guy is often a gender neutral term. It's totally fine to say, "Hey you guys" to a group of only women.
          • figgis 9y ago
            Personal experience tells me it's a pretty common thing across the US. > I don't want to be overly semantic or PC on HN Proceeds to be overly semantic or PC on HN...
          • suchow 9y ago
            Calling a group of women "you guys" is different from calling a person of unknown gender "guy" because in the former case the gender-neutral usage is the only possible interpretation, whereas in the latter it might either be gender-neutral usage or an assumption of the commenter's gender. Given that this ambiguity is nearly always a distraction from the primary purpose of the conversation (as it is here), rightfully or not, pragmatically it is better to use terms that steer clear of it. See Bryan Garner's Modern English Usage for fuller advice on safely navigating this linguistic and sociocultural terrain.
        • mpfundstein 9y ago
          I am so happy to be European... you guys in America always take everything to the total extreme.
        • knicholes 9y ago
          How are you so sure the commenter is a person? ;)
    • flamedoge 9y ago
      it's not exactly macro.. AI can run perfect macro commands. humans right now have better priority model (in this case, protecting/killing workers) for which the AI in question wasn't trained.
      • keerthiko 9y ago
        Are you sure you're interpreting 'macro' correctly? 'Macro' in the context of RTS games isn't the same as 'macro commands' in scripting, but refers to basically all high level decisions. RTS is divided up into Macro, tactics and micro: - Macro: resource prioritization (army vs expansions vs upgrades), scouting to understand the opponent's macro choices, choosing the right posture in response (defensive, harassment or offensive), and resource optimization (not getting supply blocked, scaling production with income, increasing income at the maximum possible rate, removing bottlenecks, etc), scouting the enemy army composition to prepare the ideal counter army composition - Tactics: Grand army decisions - flanks, baits, sneak attacks, hiding composition, multiprong attacks, positioning of siege units, timing attacks, knowing when to retreat (hit & run), scouting to gain advance notice of your opponent's tactics - Micro: optimizing unit lifespan and effectiveness within an isolated skirmish for the given goal (usually to 'win' the engagement) -- pulling back weakened units to avoid aggro while it still deals damage, healing, surface area for melee units, trapping enemy units with terrain or skills, optimizing spellcaster energy usage, prioritizing targets based on multiple parameters (range, damage, cost, count, follow-ups), etc. AI can "run" macro well, but they are poor at the macro decision-making part, which includes priority model as you mention (the responsive posture choice above and others). Up to low grandmaster tier, being significantly better at macro than your opponent while close in tactics and micro is usually enough to win consistently. It is the most impactful part of an RTS (and is where most of the 'S' lies).
        • sidusknight 9y ago
          Your definition of macro is a lot more encompassing than how I see it used in SC2. It normally refers to just spending resources properly (constantly producing units and workers, setting up infrastructure, etc).
  • sidusknight 9y ago
    There isn't any SC2 AI that's anywhere near as good as a player in an actual game. Sure, they can demonstrate some sick unit control, but that alone isn't enough to win games.
  • cm2012 9y ago
    Cool Starcraft related username!
• aerovistae 9y ago
  Awhile. This just isn't like Go or Chess. The gap from perfect information to imperfect information is quite a chasm, and from turn-based to real-time is even more vast. I play Age of Empires 2 semi-competitively, and I just can't imagine the research progress that would have to be made for a pro to lose to an APM-limited AI agent. So much of the game comes down to intuiting what your opponent is planning without being able to see what they're doing, and more importantly intuiting what your opponent isn't ready for. The biggest difference, though, is the "RT" in "RTS"-- real time. This isn't turn-based anymore, where at a given moment you have a single choice to make, a single piece to move as in Chess and Go, and can then wait for the singular and visible reaction your opponent makes before making your next choice. My understanding it that the moves a program like AlphaGo makes are not interconnected-- it picks each move individually as an ideal move for that board state. It could take over halfway through the game for someone else and would make the same move that it would have made at that point if it had been in control the whole time and arrived at that board state on its own. But that doesn't work in a real-time game, since you and your opponent are now moving simultaneously and the "board" is never static. Your moves must be cohesive and planned and flow continuously without time to ponder, each connected to the last. There is no "one" move for a given state. Another facet of real-time play is the idea of distraction. It's very important in RTS's to keep your opponent distracted, to disrupt their plans and their focus, by coming from unexpected directions at unexpected times, sometimes concurrently with other operations against them. This can't happen in Chess or Go, where the demands on your focus are far less urgent and two things can't happen at once in a literal sense. Can an AI agent learn to appreciate the power of distraction? Can it learn to intuit what will be most disruptive to a human, and what won't be disruptive at all? How can you teach a computer to learn to be annoying? I will say, of course, that nobody saw AlphaGo coming. And I hope it's the same with RTS's. That would be so exciting. I would love to see an AI blow us away with previously unthought-of strategies. That would be the coolest thing ever. So I hope it happens. But I'd be astonished. RTS is just such a whole new level of thinking for AIs.
  • artursapek 9y ago
    Speaking of AoE 2, I really hope this research ends up benefiting that game. The AI in it has always been so bad but it's still my favorite game of all time.
• gwern 9y ago
  Churchill, of the SC AI competition, guesses it might take as much as 5 years: https://www.wired.com/story/googles-ai-declares-galactic-war-on-starcraft- https://www.wired.com/story/googles-ai-declares-galactic-war...
  • bytefactory 9y ago
    Ooh, this'll be interesting to see, as with AlphaGo, a lot of people disputed the "experts believed it would take ~10 more years" claim retrospectively. With SC2, no AI even comes close to beating even a silver level player, so even a 5 year timeline seems really soon. Let's see if DeepMind can beat it! What's your totally unscientific guess, Gwern?
    • gwern 9y ago
      I think it is doable in under 5 years, but this critically depends on the resources invested by DM and other DL orgs. Deep RL is hugely demanding of computational resources to iterate your designs - for example, the first AlphaGo took something like 3 GPU-years to train it once (2 or 3 months parallelized); however, with much more iteration, DM was able to get Master's from-scratch training down to under 1 month. Now an AG researcher can iterate rapidly with small-scale hobbyist or researcher resources, but if they had had to do it all themselves, Ke Jie would still be waiting for a worthy adversary... When I look at all the recent deep RL research ( https://www.reddit.com/r/reinforcementlearning/ https://www.reddit.com/r/reinforcementlearning/ ) I definitely feel that we can't be far from an architecture which could solve SC2, but I don't know if anyone is going to invest the team+GPUs to do it within that timeframe. (It might not even be as complex as people think: some well-tuned mix of imitation learning on those 500k+ human games, self-play, residual RNNs for memory/POMDP-solving, and use of recent work on planning over high-level environment modeling\, might well be enough.) \ "Learning model-based planning from scratch" https://arxiv.org/abs/1707.06170 https://arxiv.org/abs/1707.06170 , Pascanu et al 2017; "Imagination-Augmented Agents for Deep Reinforcement Learning" https://arxiv.org/abs/1707.06203 https://arxiv.org/abs/1707.06203 , Weber et al 2017 (blog: https://deepmind.com/blog/agents-imagine-and-plan/ https://deepmind.com/blog/agents-imagine-and-plan/ "Agents that imagine and plan"); "Path Integral Networks: End-to-End Differentiable Optimal Control" https://arxiv.org/abs/1706.09597 https://arxiv.org/abs/1706.09597 , Okada et al 2017; "Value Prediction Network" https://arxiv.org/abs/1707.03497 https://arxiv.org/abs/1707.03497 , Oh et al 2017; "Prediction and Control with Temporal Segment Models" https://arxiv.org/abs/1703.04070 https://arxiv.org/abs/1703.04070 , Mishra et al 2017