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.
A.            The robot caused the elevator to arrive.
B.            The robot pressed the button which caused the elevator to arrive.
C.            The robot used its arm to press the button which caused the elevator to arrive.
D.            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.
E.            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.

[..]

I view ontologies and rules as the basic building blocks for addressing
this problem: ontologies can define declarative relationships between
models, and rules can define operational conversion between models.
However, in spite of the fact that we've had ontologies and rules for
many years, I think this field is still in its infancy in terms of
having strong patterns and convenient languages for using them with RDF
to easily navigate between modeling levels.  "Levels" is not quite the
right word though, because different models form a network, not a single
hierarchy.

Interestingly this is reminiscent of a “reverse” version of _knowledge-driven_ activity recognition – one is given a series of low-level atomic actions, objects being manipulated, locations, and temporal relations between them (e.g., sequential); then, ontology-based reasoning is applied to infer higher-level activities (E-> A, instead of A->E) [1-4]


William

[1] L. Chen, C. D. Nugent, and H. Wang, “A Knowledge-Driven Approach to Activity Recognition in Smart Homes,” IEEE Trans. Knowl. Data Eng.,vol.24,no.6,pp.961–974,2012.
[2] R. Helaoui, D. Riboni, and H. Stuckenschmidt, “A Probabilistic Ontological Framework for the Recognition of Multilevel Human Activities,” in Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2013, pp. 345–354.
[3] G. Okeyo, L. Chen, and H. Wang, “Combining ontological and temporal formalisms for composite activity modelling and recognition in smart homes,” Futur. Gener. Comput. Syst., vol. 39, pp. 29–43, Oct. 2014.
[4] Van Woensel, W., Abidi, S. R. Abidi, S. S. R. Pro-Actively Guiding Patients through ADL via Knowledge-Based and Context-Driven Activity Recognition. In proceedings of 17th World Congress on Medical and Health Informatics (MEDINFO'19), Lyon, France.