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What if instead of spending time to train the ML model they just made a dummy client with it’s own simple probabilistic state machine or behaviour tree to balan

What if instead of spending time to train the ML model they just made a dummy client with it’s own simple probabilistic state machine or behaviour tree to balance the game? How much time and resources would be spent for above mentioned approach compared to ML approach? I think due to statistical nature of ML, it is seen as kind of an hammer to every problem that might be solved statistically(which there are lots of them) but it might not be the most effective use of engineering resources.

Dzugaru 6y ago
Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI. We've recently started using deep reinforcement learning for our games and its almost like a miracle how simple, effective and scalable the system is. There are some mentioned problems like designing rewards for player enjoyment, but its definitely got a massive reduction in engineering effort.
- Jasper_ 6y ago
  > Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI. That's effectively what game AIs are, today. Users want an AI they can model and simulate in their head, and isn't too brutal of a challenge. Today's machine learning cannot provide a model like that.
  - summerlight 6y ago
    The whole point of this research is to early study on unknown aspects of game's design and its consequence on user behaviors and game balance. AI should be able to find and exhibit game play unexpected to the designer, which is not easily achievable with a tight control given to game designers. It's more of systematic state space exploration, not making AI fun to play with.
drawkbox 6y ago
What is really interesting to me is to train the game models from the existing player base. This is fairly common but more simulation replaying in networked games with fake bots based on that gameplay. Previously I have done similar in networked games where the way points and reactions are reused in AI agents that mimic players. The networked players that we captured, if another player dropped, we replayed captured ghost plays essentially with more interactivity that seem more real than just AI. This allowed us to have networking game experience of matchup, that can drop a player, but seemingly did not but still have that player act human not just bot-like. This is now used in all our matchup/networking games because it makes the matchup/networked experience better for the player even if they end up playing a sim and not knowing it. Taking that further if you could train the machine learning models from the existing player base for an additional level of focus it could really make the game fun and you could use it to even drive player behavior. Machine learning definitely has a place in game testing and progression tuning. However, even more it would be nice if it can learn on the fly and not end up in predictable AI, one possible way would be capturing actual human player behavior and modeling that.

Dzugaru 6y ago

Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI. We've recently started using deep reinforcement learning for our games and its almost like a miracle how simple, effective and scalable the system is. There are some mentioned problems like designing rewards for player enjoyment, but its definitely got a massive reduction in engineering effort.

Jasper_ 6y ago
> Anecdotally: I've never seen a "simple state machine" or "simple behaviour tree" in game AI. That's effectively what game AIs are, today. Users want an AI they can model and simulate in their head, and isn't too brutal of a challenge. Today's machine learning cannot provide a model like that.
- summerlight 6y ago
  The whole point of this research is to early study on unknown aspects of game's design and its consequence on user behaviors and game balance. AI should be able to find and exhibit game play unexpected to the designer, which is not easily achievable with a tight control given to game designers. It's more of systematic state space exploration, not making AI fun to play with.

drawkbox 6y ago

What is really interesting to me is to train the game models from the existing player base. This is fairly common but more simulation replaying in networked games with fake bots based on that gameplay. Previously I have done similar in networked games where the way points and reactions are reused in AI agents that mimic players. The networked players that we captured, if another player dropped, we replayed captured ghost plays essentially with more interactivity that seem more real than just AI. This allowed us to have networking game experience of matchup, that can drop a player, but seemingly did not but still have that player act human not just bot-like. This is now used in all our matchup/networking games because it makes the matchup/networked experience better for the player even if they end up playing a sim and not knowing it. Taking that further if you could train the machine learning models from the existing player base for an additional level of focus it could really make the game fun and you could use it to even drive player behavior. Machine learning definitely has a place in game testing and progression tuning. However, even more it would be nice if it can learn on the fly and not end up in predictable AI, one possible way would be capturing actual human player behavior and modeling that.