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>>On one hand, this is a testament to the power of contemporary machine learning methods and the latest computer chips to process vast amounts of data. On the o

>>On one hand, this is a testament to the power of contemporary machine learning methods and the latest computer chips to process vast amounts of data. On the other, it’s a reminder of how fundamentally unintelligent AI agents are. forgive any ignorance, but how do changes to the game affect AI behavior? E.g. New characters, new maps, balance changes, bug fixes, etc. Do the AI have to be re-trained in a similar amount time, or is it expedited? How adaptive are the A.I. systems? What I'm getting at is - in a real world scenario (like the city transportation example from the article), how quickly can the A.I. respond to things unexpected changes (natural disasters, accidents, etc)? Will they be able to handle the unexpected in a safe time frame?

xapata 8y ago
Depends on the program, of course. You're asking how robust the trained model is to outliers and how effective it is at extrapolation. Different training algorithms have different behaviors.
relenzo 8y ago
A Reinforcement Learning machine would have to be re-trained for changes such as that, but not from scratch. Depending on the size of the change, it would take much less time to adapt to such a change. Several notes: -The article notes that a lot of the hard parts of human video-game playing have been done 'for' the AI by hardcoding. It doesn't actually have to look at the screen to parse information--it has direct access to game variables that player would have to access through menus. More relevant to your question, a lot of the strategic decisions like item purchasing, ward placement, and probably character placement were just picked by the programmers or set to be ignored. The whole thing is less impressive than the headline sounds. I think it was just learning--where to run on the map and when to attack things? -RL is based on deep learning, and there are fundamental issues with deep learning's ability to adapt to genuinely new scenarios. None of these system can presently adapt to something genuinely unprecendeted in a time frame you would consider 'safe'. They need at least several opportunities to observe how the world works after the changes and the consequences of their actions. To try to make this concrete--they can't reason about what implications a flood/power outage/landslide has for their traffic management. They can only learn from trial and error <-(the important part) how traffic behaves during a disaster.
- xapata 8y ago
  > RL is based on deep learning False. Reinforcement Learning is a category of algorithm. There are many possible implementations, not only "deep learning". Further, you might be surprised at how well a generalized, trained model might mimic "reasoning".

xapata 8y ago

Depends on the program, of course. You're asking how robust the trained model is to outliers and how effective it is at extrapolation. Different training algorithms have different behaviors.

relenzo 8y ago

A Reinforcement Learning machine would have to be re-trained for changes such as that, but not from scratch. Depending on the size of the change, it would take much less time to adapt to such a change. Several notes: -The article notes that a lot of the hard parts of human video-game playing have been done 'for' the AI by hardcoding. It doesn't actually have to look at the screen to parse information--it has direct access to game variables that player would have to access through menus. More relevant to your question, a lot of the strategic decisions like item purchasing, ward placement, and probably character placement were just picked by the programmers or set to be ignored. The whole thing is less impressive than the headline sounds. I think it was just learning--where to run on the map and when to attack things? -RL is based on deep learning, and there are fundamental issues with deep learning's ability to adapt to genuinely new scenarios. None of these system can presently adapt to something genuinely unprecendeted in a time frame you would consider 'safe'. They need at least several opportunities to observe how the world works after the changes and the consequences of their actions. To try to make this concrete--they can't reason about what implications a flood/power outage/landslide has for their traffic management. They can only learn from trial and error <-(the important part) how traffic behaves during a disaster.

xapata 8y ago
> RL is based on deep learning False. Reinforcement Learning is a category of algorithm. There are many possible implementations, not only "deep learning". Further, you might be surprised at how well a generalized, trained model might mimic "reasoning".