An affordance-based formalism for modeling human-involvement in complex systems for prospective control

Abstract

We propose a predictive modeling framework for human-involved complex systems in which humans play controlling roles. Affordance theory provides definitions of human actions and their associated properties, and the affordance-based Finite State Automata (FSA) model is capable of mapping the nondeterministic human actions into computable components in modeling formalism. In this paper, we further investigate the role of perception in human actions and examine the representation of perceptual elements in affordance-based modeling formalism. We also propose necessary and sufficient conditions for mapping perception-based human actions into systems theory to develop a predictive modeling formalism in the context of prospective control. A driving example is used to show how to build a formal model of humaninvolved complex system for prospective control. The suggested modeling frameworks will increase the soundness and completeness of a modeling formalism as well as can be used as guide to model human activities in a complex system.

How to Formally Model Human in Collaborative Robotics

“…This method clearly ignores realistic scenarios in which human actually performs an error. In another example, Kim et al [40] map human non-determinism to a finite state automaton. However, their model is limited to the context of prospective control.…”

Formal model of human erroneous behavior for safety analysis in collaborative robotics

“…Therefore, there is a vast literature on defining, formalizing and modeling human errors. Examples in this regard are CCT [35,36], extended SAL model with empirical data [37], state machines [38] and extended cognitive models [39].…”

Modeling Operator Behavior in the Safety Analysis of Collaborative Robotic Applications

“…Nevertheless, using data related to specific case studies can lead to the loss of generality. [17,19,33] are other examples that combine cognitive and formal models to capture erroneous human behaviors in interactions with devices.…”