In RL there's no need to assign values to everything, they are derived from the basic values (i.e. instinct: life, reproduction, food, etc.). If an individual's organism produces good sensations for ice cream, it's producing good reinforcement value for ice cream. The organism can have different values for different food based on it's value for life. For example, in general, poison tastes bitter and bitter, for most people, is bad. Sugar is energy, and sweet things are, in general, good. The same for fat. For the sexual positions, what counts are the sensations, as with food. And what's not in our instincts is derived from experience (near things in time and space contribute to the value). This is the way psicologic traumas are created, also (a side effect).
On 6/23/07, Mike Tintner <[EMAIL PROTECTED]> wrote:
C P Raphael: > "Reinforcement learning is a simple theory that *only* solves problems > for which we can design value functions." > In other words... almost anything in real life... > What about if the values are EXTREMELY crude and fluctuating - like the value to you of Mars ice cream vs Ben & Jerry's Phish food or whatever, and the value of this sexual position vs that one? That is, after all, one of the primary functions of emotions - to serve as extremely crude and fluctuating evaluations of different actions - comparisons that are so crude often as to be pre-mathematical. "How much do you like that ice cream?" "Well, I like it 'a lot'." "And that one?" "Well, a lot too. But maybe 'a bit more'." Could reinforcement learning still embrace such crudities - or would you need a totally different kind of programming? ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?&;
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