Once again, I never said RL was all that was needed. I included concept formation (a.k.a cognitive structure formation) as a requirement. Cognitive structures provide the understanding, RL or other goal-directed mechanisms provide the will. I fail to see what about regulation and compensation is not implemented by RL, aside from the formation of the concepts/cognitive structures necessary to distinguish the appropriate circumstances for an action to be performed, which I already acknowledged.
On Tue, Jan 29, 2013 at 10:44 AM, Piaget Modeler <[email protected]>wrote: > You need more than just reinforcement learning. You need "regulation" > and "compensation" psychological terms Piaget used. > > Regulation is the correction of failed behaviors or reinforcement of > successful behaviors. > Compensation is the inversion of failed behaviors or the elimination of > undesirable side effects. > > Both regulation and compensation should be intrinsic in the cognitive > system, and in my view, should build new cognitive structures > tightly integrated into existing and new behaviors. This is way more > than Reinforcement Learning. > > ~PM > > ------------------------------ > Date: Tue, 29 Jan 2013 08:54:39 -0500 > Subject: [agi] RL Does Not Fully Explain Inner Direction > From: [email protected] > To: [email protected] > > > On Mon, Jan 28, 2013 at 6:21 PM, Aaron Hosford <[email protected]>wrote: > > In regards to the idea that intrinsic rewards are somehow different from > extrinsic ones, a reward signal can just as easily be modulated by internal > events (thoughts) as external ones (percepts). Furthermore, if you read up > on RL, you'll see that in all effective multi-step RL-style algorithms, > there is a backward chaining of reward, so that previous behaviors or other > early triggers for a behavior are rewarded, not just the immediate actions. > All actions, whether extrinsically or intrinsically rewarding, derive their > value from either immediate or indirect/backward-chained reward signals, > which means we can modulate behavior arbitrarily to any level of complexity > with relatively minimal difficulty by taking advantage of this backward > chaining. > > Well the fact that backwards chaining of the actions leading up to a > rewarded behavior is an interesting point. And while anyone with a little > imagination could come up with a creative means to develop a way to use RL > to reinforce complex behaviors based on parts of a behavior string that is > reinforced this is not explained by the backward-chained reward signals > that you mentioned. > But looking beyond that the claim that any internal motivation could be > explained by external reinforcement is unnecessarily complicated because it > is dependent on external rewards which would demand that things like the > massive levels of complexity of infinitesimal past rewards could explain > inner direction. This is the same problem as insisting that Bayesian > Reasoning along with some priors are all that is necessary to explain human > intelligence. Sorry but it just does not work - unless you change the > presumptions of what is meant by Reinforcement Learning or Bayesian > Reasoning. (Which is ok, I am just saying...) > Jim Bromer > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/23050605-2da819ff> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
