[D] A few questions from a behavioral scientist on reinforcement learning…
I’ve recently started getting interested in AI. As far as I know, from my friend who is more familiar with AI, in reinforcement learning only 2 concepts are applied: reinforcement (layman’s term: reward), and punishment.
However, in behavioral science there are many more elements that may be useful to implement in an AI. There are concepts such as:
- Discriminative stimuli, or stimulus generalization.
- Continuous and discrete stimulus/response fields.
- “Schedules of reinforcement” which change the frequency of an organisms response to a stimulus (such as variable ratio, fixed ratio, variable interval, fixed interval)
I’m not familiar with any AI that uses these concepts and others from behavior analysis. Is anyone familiar with any AI that tries to implement these?
It seems to me a concept such as stimulus generalization may be a useful step in artificial general intelligence for ‘one shot learning’ – to give an example from a study: if you reinforce a pigeon with food everytime it pecks a key under a 550 milimicron (color wavelength) light, then you stop reinforcement (giving it food if it pecks), and you change the color of the light slightly to 560 or 570, the pigeon still pecks the key but fewer times. Therefore ‘stimulus generalization’ occurs. In other words, the pigeon responds similarly to similar stimuli (but with a reduced frequency). The more you change the color – to say 580, then 590, the less responses (or pecks on the key). This also occurs with punishment.
Is anyone familiar with this being applied to AI? Or anything additional from behavioral science for that matter? If only reinforcement and punishment are applied I fear that’s really limiting the great potential of AI.