While many of my A.s.a. H. agents have been specialists they do typically share many mental concepts, reflexes, and instincts with one another. Things like "pupillary" light reflex*, sensation, etc.
* and general sensor auto ranging (another preprocessor)
While I managed to load the first layer of an A.s.a. H. light program* onto an Arduino Mega** the Arduino Uno Rev 3 proved inadequate for an even lighter version than this. According to its specs the new Arduino Uno Rev 4 should be adequate, however, and so I've bought a couple of these to try out.
* Essentially the code of my 14 May 2012 blog but with input and output through the Arduino gpio and having inputs accepted whenever one or more of the inputs change significantly: | new In(N) - old In(N) | > (C)( old In(N) ).
** See my blog of 1 January 2023.
I have noticed that AIs are being used more and more to generate and to compare various models of phenomena.** Models of the proton involving charmed quarks being one example.***
* An example of a specialist AI, but in software rather than hardware.
** Such as my use of neural networks to model plasma confinement: for example, my paper in Trans. Kansas Acad. Sci., vol. 98, num. 3-4, pg 144, 1995 and Experiments With Machine Creativity, chapter 5 of my book TWELVE PAPERS, tables 4 and 5.
*** For example, in Nature, vol. 608, pg. 483, 2022.
Division of labor, specialization,* is one of the ways I've been trying to deal with complexity and the curse of dimensionality.** Typical social insects have something like 3-4 castes. Many small multicellular organisms have 10 or more cell types. The U.S. Bureau of Labor Statistics classifies all workers into 867 occupations divided into 23 categories. How many specialized agents one uses should depend upon the environment you're working in and the tasks you're attempting to perform. I currently have 52 small robots*** in my lab and the makings**** of about 20 more. Software is even more varied.
So what specialized hardware has proven to be useful/required? 1. wheeled (and tracked too?) 2. fliers 3. swimmers 4. walkers? (quadrupedal? bipedal too?) 5. pick and place (arms) 6. gantry? 7. snake? 8. hybrid? (for stair climbing?) 9. various grippers/manipulators 10. several different processors? (and memories?) 11. different sensory systems
* In both hardware and software.
** See, for example, my blog of 18 August 2021.
*** This includes a fair amount of duplication. It can take a couple of tries before I manage to get a workable robotic agent capable of performing a given required set of tasks.
**** Lego, Vex IQ, Vex EDR, Meccano, etc.
I have explored various attention mechanisms. Pain is another of these. A.s.a.'s hunger (low battery voltage) can trigger a search for a recharging station. High temperature measured on a gripper can trigger withdrawal of the manipulator. High transverse accelerations while moving along rough terrain can cause a robot to slow down......
With computer vision it is conventional to pretrain lower levels in the network to represent features like edges and colors. These are then available for later application when learning new images. A.s.a. H. was designed to have a generalized version of such transfer learning.*
* See, for example, my papers Trans. Kansas Acad. Sci. vol. 109, pg 159, 2006 and vol. 120, pg 108, 2017 and my blog of 5 Nov. 2015.
A. C. Grayling has said that truth is an idealization, something to be approximated toward as a limit. I make use of multiple theories of truth, e.g.:
1. correspondence 2. coherence 3. consensus
1. I use observation and experiment. 2. I see how it fits with my prior models of things. 3. And I see what models other workers use.
