Cooperation between human and AI planners
This project is part of the Robust RAIL: ICAI Lab on Responsible Decision Support for Efficient and Dynamic Railway Systems. This project is a collaboration between partners from Utrecht University (Computer Science and Social Sciences), and NS and ProRail.
Scientific Challenge
Future planning of infrastructure usage requires a dynamic approach in which human operators interact with automated planning tools to jointly optimize the planning process. This research project aims to improve this interaction in the logistical planning process of the railways. The key scientific challenges are to identify which factors of planning processes are relevant for optimal decision-making by the joint human-AI system, how these factors as well as the plans computed by the algorithms can be explained to human operators, how the human operators can be better supported (e.g., when under stress or time-pressure), and how to inform algorithmic decision-making protocols with strategic human interventions.
Methodology
A variety of methods from the social, behavioral, and AI sciences will be used. We will use qualitative methods to study how human planners at NS and ProRail currently operate. This study will reveal requirements, expectations, and potential pitfalls of human-AI interaction [1], specifically of interaction with (future) algorithmic planners. These results will be augmented with data science techniques to extract important factors from past decision-making and planning processes, to develop a (computational) cognitive model of the decision and planning process [2,5]. This model will serve as a benchmark for unsupported operator behavior. To specify the optimal decision support, we will experimentally identify the factors that contribute to the explainability of the algorithmic planner [3], as well as acceptability, trust, and joint decision-making efficiency. Through quantitative and modeling studies we will compare which explanation formats provide the best decision support under stressful circumstances. A goal of automated systems is that humans can make strategic decisions, while the automated system makes the appropriate corresponding operational decisions [4]. Through qualitative studies, we will investigate the right moment and the right level at which humans can intervene.
[1] Janssen, C. P., Donker, S. F., Brumby, D. P., & Kun, A. L. (2019). History and future of human-automation interaction. International Journal of Human-Computer Studies, 131, 99-107.
[2] Kolvoort, I.R., Fisher, B, Van Rooij, R., Schultz, K., & Van Maanen, L. (submitted). Probabilistic causal reasoning under time pressure.
[3] Koopman, T. & Renooij, S. (2021) Persuasive contrastive explanations for Bayesian networks. Proc. of European Conference on Symbolic and Quantitative Approaches with Uncertainty, p 229-242
[4] Michon, J. A. (1985). A critical review of driver behavior models: What do we know, what should we do? In Human behavior and traffic safety (pp. 487-525). Plenum Press, NY.
[5] Dastani, M., Hulstijn, J., & van der Torre, L. (2005), How to decide what to do? European Journal of Operational Research 160(3): p 762-784