Since the utility function is approximated anyway, it becomes an abstract concept—especially in the case of evolved brains. For an evolved creature, the evolutionary utility function can be linked to long term reproductive fitness, and the value function can then be defined appropriately.
For a designed agent, it’s a useful abstraction. We can conceptually rate all possible futures, and then roughly use that to define a value function that optimizes towards that goal.
It’s really just a mathematical abstraction of the notion of X is better than Y. It’s not worth arguing about. It’s also proven in the real world—agents based on utility formalizations work. Well.
It certainly is worth discussing, and I’m sorry but you are not correct that “agents based on utility formalizations work. Well.”
That topic came up at the AAAI symposium I attended last year. Specifically, we had several people there who built real-world (as opposed to academic, toy) AI systems. Utility based systems are generally not used, except as a small component of a larger mechanism.
Pretty much all of the recent ML systems are based on a utility function framework in a sense—they are trained to optimize an objective function. In terms of RL in particular, Deepmind’s Atari agent works pretty well, and builds on a history of successful practical RL agents that all are trained to optimize a ‘utility function’.
That said, for complex AGI, we probably need something more complex than current utility function frameworks—in the sense that you can’t reduce utility to an external reward score. The brain doesn’t appear to have a simple VNM single-axis utility concept, which is some indication that we may eventually drop that notion for complex AI. My conception of ‘utility function’ is loose, and could include whatever it is the brain is doing.
Since the utility function is approximated anyway, it becomes an abstract concept—especially in the case of evolved brains. For an evolved creature, the evolutionary utility function can be linked to long term reproductive fitness, and the value function can then be defined appropriately.
For a designed agent, it’s a useful abstraction. We can conceptually rate all possible futures, and then roughly use that to define a value function that optimizes towards that goal.
It’s really just a mathematical abstraction of the notion of X is better than Y. It’s not worth arguing about. It’s also proven in the real world—agents based on utility formalizations work. Well.
It certainly is worth discussing, and I’m sorry but you are not correct that “agents based on utility formalizations work. Well.”
That topic came up at the AAAI symposium I attended last year. Specifically, we had several people there who built real-world (as opposed to academic, toy) AI systems. Utility based systems are generally not used, except as a small component of a larger mechanism.
Pretty much all of the recent ML systems are based on a utility function framework in a sense—they are trained to optimize an objective function. In terms of RL in particular, Deepmind’s Atari agent works pretty well, and builds on a history of successful practical RL agents that all are trained to optimize a ‘utility function’.
That said, for complex AGI, we probably need something more complex than current utility function frameworks—in the sense that you can’t reduce utility to an external reward score. The brain doesn’t appear to have a simple VNM single-axis utility concept, which is some indication that we may eventually drop that notion for complex AI. My conception of ‘utility function’ is loose, and could include whatever it is the brain is doing.