Thanks. Is there a reason behind it, or not? The reason I ask is because I'm comparing reinforcement learning systems to traditional AI planning systems.Trying to see how the accomplish the same job, of action selection, and what the real tradeoffs are. My initial guess is that RL systems may suffer from a lack of representational Flexibility available in AI planning systems, while traditional AI planning systems are not as fast as RL systems. The lack of Representational flexibility means that RL systems cannot take advantage of problem space abstractions and other techniques of AI planners. In theend they may be less efficient, and they may or may not scale. Combining these two approaches appears to be agood idea which I suspect is what the dynamic adaptive planning paradigm must be about. I'll dig into it further.
Other thoughts are appreciated... Cheers, ~PM Date: Mon, 16 Mar 2015 10:16:35 +0800 Subject: Re: [agi] Plans vs. Policies From: [email protected] To: [email protected]; [email protected] Either case is possible ;) On Mon, Mar 16, 2015 at 10:15 AM, Piaget Modeler via AGI <[email protected]> wrote: Are OpenCog's policies distinct structures from its plans or are they the same structure? Also, are the plans single action, as in Reinforcement Learning, or multi-action as in AI Planning? Kindly advise. ~PM Date: Mon, 16 Mar 2015 10:08:38 +0800 Subject: Re: [agi] Plans vs. Policies From: [email protected] To: [email protected] OpenCog uses policies to drive the creation of dynamic plans ;) On Mon, Mar 16, 2015 at 10:07 AM, Piaget Modeler via AGI <[email protected]> wrote: Ben, What does OpenCog use? Plans or policies? Why? ~PM Date: Mon, 16 Mar 2015 10:01:31 +0800 Subject: Re: [agi] Plans vs. Policies From: [email protected] To: [email protected] A traditional plan in the AI planning literature sense does not depend on future observations, but there is now a big literature on dynamic/adaptive planning algorithms as well... ben From: [email protected] To: [email protected] Subject: [agi] Plans vs. Policies Date: Sun, 15 Mar 2015 16:22:46 -0700 Reinforcement Learning uses "policies" to select actions while most work in AI Planning emphasizes the construction and representation of a "plan" which consists of a sequence of actions (or a hierarchyof composite and primitive actions). Kindly compare, contrast, evaluate trade-offs, and recommend the plans or policies approach Your rationale is appreciated. ~PM -- Ben Goertzel, PhD http://goertzel.org "The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." -- George Bernard Shaw AGI | Archives | Modify Your Subscription AGI | Archives | Modify Your Subscription -- Ben Goertzel, PhD http://goertzel.org "The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." -- George Bernard Shaw AGI | Archives | Modify Your Subscription AGI | Archives | Modify Your Subscription AGI | Archives | Modify Your Subscription -- Ben Goertzel, PhD http://goertzel.org "The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man." -- George Bernard Shaw ------------------------------------------- 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
