In the office hours on Tuesday, Jeff mentioned there being a potential approach for system motivation using entropy. That made me look back at an article that Wissner-Gross & Freer published in April called "Causal Entropic Forces" that got some press for simulating adaptive behavior. It very much aligns with what Jeff mentioned, so I figured it'd be worth sharing.
For anyone who didn't catch it, here's the paper<http://math.mit.edu/~freer/papers/PhysRevLett_110-168702.pdf>, a video of the simulations <http://www.entropica.com/>, and a decent, less-technical overview<http://davidruescas.com/2013/04/22/causal-entropy-maximization-and-intelligence/> . The gist: without any guidance, the authors were able to simulate agents setting and achieving goals, just using a "simple" model that had the agents moving towards states that afforded them the greatest future entropy. Or better still, the agents appeared intelligent because they were putting themselves in positions that gave them the most options. If you wanted to take a swing at implementing their model, the meat and potatoes is in the path integration of equation 11, but I think the trickiest part could be parametrizing the path temperature. Then it sounds like you have your agent run Monte Carlo simulations on potential futures at each timestep (Eron's "Imagine That" hack is ahead of the game). It could be a useful motivation engine within NuPIC, somewhere down the line. It feels like it's a slightly higher abstraction than the current neuron level, and it would definitely need to make use of the sensor-motor pathways. Nothing that this community can't handle. Tom
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