Work

A causal AI and explainable optimization framework for social robot design

Year: 2026

Type: article

Abstract: Mapping human-machine requirements in smart product design remains challenging. An integrated framework combining semiotic architecture product design (SAPAD), dual machine learning (DML), hesitant fuzzy quality function deployment (HFQFD), and multi-objective optimization is proposed. Home companion robots are used as a case study. User behavior is deconstructed via SAPAD to generate demand hypotheses. Causal inference is performed by DML to identify core demands impacting user experience, with priorities defined by integrating expert-assigned and causal weights. Evaluation uncertainty is addressed by HFQFD, through which validated demands are converted into design parameters. A hybrid approach integrating Cuckoo Catfish Optimizer (CCO), least squares support vector machine (LSSVM), and non-dominated sorting genetic algorithm II (NSGA-II) is developed for multi-objective optimization. Shapley Additive exPlanations (SHAP) analysis is employed to quantify each parameter's marginal contribution to conflicting requirements. A closed-loop process from behavioral observation to parameter optimization delivers transparent decision support for complex human-machine system design.

Source: iScience

DOI: 10.1016/j.isci.2026.115780

Authors: Zongming Liu, Xinan Liang, Fengqi Yang

Institutions: Shaanxi University of Science and Technology, Huaihua University

Language: English

Topic: Quality Function Deployment in Product Design

Subfield: Management of Technology and Innovation

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,240

Funders: Shaanxi University of Science and Technology, Shaanxi Provincial Science and Technology Department