Re: Explanation, Mechanistic Reasoning, and Abstraction: Hypertext and Hypermodels

> On 27 Nov 2021, at 12:00, Adam Sobieski <adamsobieski@hotmail.com> wrote:
> 
> One can consider the following, increasingly detailed, set of explanatory sentences.
>  
> The robot caused the elevator to arrive.
> The robot pressed the button which caused the elevator to arrive.
> The robot used its arm to press the button which caused the elevator to arrive.
> The robot pressed the button which closed a circuit which sent electricity to a control system while simultaneously causing the button to light up. The elevator control system, having received the electric signal from the button press, dispatched an elevator to the floor that the robot was on.
> The robot used its arm, hand, and finger to press the button which closed a circuit which sent electricity to a control system while simultaneously causing the button to light up. The elevator control system, having received the electric signal from the button press, dispatched an elevator to the floor that the robot was on.

These fit nicely with plausible reasoning expressed in terms of operations on graphs in episodic memory. You may start with the goal of moving to a different floor, deciding between using the stairs (harder) and the elevator (easier), navigating to the elevator, and summoning it with the button provided for that purpose.

Plausible reasoning can use context specific rules when available, and when not, deliberative reasoning to find a satisfactory solution, and saving the conclusion as a new rule.  We don’t need to invoke deeper models as we can usually make use of higher level knowledge. We learn about elevators in functional terms: the purpose for moving between floors, the information the elevator provides to us, and the controls we use to operate it. The deeper models in terms of electrical circuits and computer control only become important in respect to people involved with installation and maintenance.

Plausible reasoning involves models that are simple and effective, based upon past experience, and without the need for extensive statistics as would be the case for Bayesian models. Plausible reasoning uses a patchwork of context dependent informal knowledge in contrast to logic and formal semantics.

Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
W3C Data Activity Lead & W3C champion for the Web of things