Hi John Thanks for the Input. If I understood the first part of your thought correctly; agents could be collaborative-type of quanta forming some kind of generative frequency, relative to the properties of the agent form?
On the second part; each agent should be a hard-coded fractal, with the scalability property being a replicated constant? Some type of soft-coded, management system gets all the pieces working in the purposed direction? Last, I noticed your point on "feedback". If open-looped (probably recursive), closed-looped (probably self recursive). To be learning, one would probably require both? Rob > From: a...@listbox.com > To: a...@listbox.com > Subject: RE: [agi] Couple thoughts > Date: Tue, 24 Feb 2015 06:16:11 -0500 > > > -----Original Message----- > > From: Nanograte Knowledge Technologies via AGI [mailto:a...@listbox.com] > > > > How about we discuss how to build an adaptive, complex machine? > > > > Proposed definition: > > An adaptive, complex machine would have the ability to learn from its > > installable knowledge base and environmental experiences and be able to > > consistently apply functional evidence of this learning in order to solve > ever- > > complex problems with in a fraction of human-effort time. > > I picture many agents, thinking about bacteria lately, not sure how many > agents are needed to reach a particular level of adaptiveness. Some systems > might require three agents some a billion. It depends on things, > computational resources and, complexity of agents including individual > communication complexity characteristics and how the complexity scales and > forms patterns across groups of agents IOW how the complexity and entropy of > the MAS behaves - perhaps how adaptive patterns are formed? Interesting > looking at cymatics here I wonder if in a massive MAS, MMAS? if there is > something similar going on since you have this inverse-quantization effect > forming wave signals and the agents can be coordinated to focus waveforms in > various ways to generate computational patterns... or - internally let's say > in biological cells the DNA does perform quantum networking/coordination - > similar could be attempted in a computer MAS cymatically. > > I would say engineer into each agent some of these preconceived limiting > unknowns in how the system behaves. For example: How many agents would the > system require to adapt? We don't know so engineer into each the ability to > control how the whole system scales. And then have feedback into each agent > from the whole system - sounds like a genetic system and it could have that > adaptiveness but - keep in mind we are in a computer and have different > computational characteristics verses biologic molecular. Comparably there > are impediments and enhancements but we needn't reverse engineer the > limitations in molecular biology such as in artificial life that's where > that goes very wrong I think. Also ALife intelligence computation might have > gone wrong in being overly focused on genetics in terms of evolution and not > on DNA quantum communication abilities... > > John > > > > > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/26941503-0abb15dc > Modify Your Subscription: https://www.listbox.com/member/?&; > Powered by Listbox: 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
