https://onlinelibrary.wiley.com/doi/pdf/10.1207/s15516709cog2801_7

Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions

Understanding complex systems is fundamental to understanding science. The complexity of such systems makes them very difficult to understand because they are composed of multiple interrelated levels that interact in dynamic ways. The goal of this study was to understand how experts and novices differed in their understanding of two complex systems, the human respiratory system and an aquarium ecosystem. In particular, we examined how a representation of complex systems, Structure-Behavior-Function theory (SBF), might account for these differences. SBF is particularly relevant in understanding biological systems because an important domain principle is the relation between form, function, and mechanism. Our results demonstrated that there were minimal differences between the expert and novice groups on structures, but that the locus of the difference was on understanding causal behaviors and functions, the least salient elements of the systems. Mental model analysis provided largely convergent results. We...

https://www.jstor.org/stable/pdfplus/25473553.pdf

Fish Swim, Rocks Sit, and Lungs Breathe: Expert-Novice Understanding of Complex Systems

(2006). Understanding Complex Systems: Some Core Challenges. Journal of the Learning Sciences: Vol. 15, No. 1, pp. 53-61.

Understanding Complex Systems: Some Core Challenges

Teleological modeling is fundamental to understanding and explaining many complex systems, especially engineered systems. Research on engineering design and problem solving has developed several ontologies for expressing teleology, for example, functional representation, function–behavior–structure, and structure–behavior–function (SBF). In this paper, we view SBF as a programming language. SBF models of engineering systems have been used in several computer programs for automated design and problem solving. The SBF language captures the expressive power of the earlier programs and provides a basis for interactive construction of SBF models. We provide a precise specification of the SBF language. We also describe an interactive model construction tool called SBFAuthor.

Structure, behavior, and function of complex systems: The structure, behavior, and function modeling language

Configuration design constraint processes in design co-operative design design object modelling shape design integrated design environments design rationale case-based reasoning in design design analysis architectures for design knowledge learning in design conceptual design design processes.

Artificial Intelligence in Design '92

Knowledge-based support for learning about physical devices is a classical problem in research on intelligent tutoring systems (ITS). The large amount of knowledge engineering needed, however, presents a major difficulty in constructing ITS's for learning how devices work. Many knowledge-based design systems, on the other hand, already contain libraries of device designs and models. This provides an opportunity for reusing the legacy device libraries for supporting the learning of how devices work. We report on an experiment on the computational feasibility of this reuse of device libraries. In particular, we describe how the structure-behavior-function (SBF) device models in an autonomous knowledge-based design system called KRITIK enable device explanation and exploration in an interactive design and learning environment called Interactive Kritik.

Towards Design Learning Environments - I: Exploring How Devices Work

Since these numbers were larger than we expected, we requested the JCISE Editor for help in managing the reviewing of the submitted papers. As Guest Editors of this special issue, we managed the reviewing about twenty five submissions that focused on knowledge-based design. The JCISE Editor and Associate Editors managed the reviews of other submissions. The twenty five or so submissions in knowledge-based design were reviewed (and many re-reviewed) by close to seventy five reviewers over several months. The fourteen papers included in this special issue represent the result of this long, deliberative process. A few other papers submitted to the special issue will soon appear in regular issues of JCISE.

/pdf/artificial-intelligence-in-design-an-overview-4uqm8ttpk6.pdf

Artificial Intelligence in Design: An Overview

Very rarely do physical devices function as intended by their designers the first time they are implemented. Usually, there are two ways in which the behavior of a device may deviate from the intended one: the device may not exhibit a desired behavior or it may result in an undesirable behavior. We describe a model-based method for solving the latter task. This method involves diagnosis and repair of the failed device and verification of the modified device. It uses compiled structure-behavior-function (SBF) models of how the device works. In an SBF model, the behaviors and the structural elements of a device act as indices into causal mechanisms that explain how the structure of the device produces its behaviors. The causal mechanisms in turn serve as indices into qualitative relations between device variables. The KRITIK2 system uses this indexing scheme to access relevant causal mechanisms and qualitative relations, and uses this knowledge for solving the diagnosis, repair, and verification subtasks of redesign. KRITIK2 shows that this model-based method is sufficient for parametric redesign even for devices in which a single cause results in multiple effects and a single structural element plays a role in multiple causal behaviors.

Andrés Gómez de Silva Garza

Papers

Towards Design Learning Environments - I: Exploring How Devices Work

Artificial Intelligence in Design: An Overview

Model-based redesign: indexing causal mechanisms and qualitative relations