SOAR: an architecture for general intelligence
Artificial Intelligence
Unified theories of cognition
Dynamic representation of decision-making
Mind as motion
Dynamic stochastic models for decision making under time constraints
Journal of Mathematical Psychology
Learning in Dynamic Decision Making: The Recognition Process
Computational & Mathematical Organization Theory
Reasoning and unsupervised learning in a fuzzy cognitive map
Information Sciences—Informatics and Computer Science: An International Journal
Restored fuzzy measures in expert decision-making
Information Sciences: an International Journal
A complex network approach to text summarization
Information Sciences: an International Journal
A simple graphical approach for understanding probabilistic inference in Bayesian networks
Information Sciences: an International Journal
Integrated human decision making model under belief-desire-intention framework for crowd simulation
Proceedings of the 40th Conference on Winter Simulation
An integrated human decision making model for evacuation scenarios under a BDI framework
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Human behavioral simulation using affordance-based agent model
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: design and development approaches - Volume Part I
Information Sciences: an International Journal
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Behavioral modeling with the new bio-inspired coordination generalized molecule model algorithm
Information Sciences: an International Journal
Hi-index | 0.08 |
Decision field theory (DFT), widely known in the field of mathematical psychology, provides a mathematical model for the evolution of the preferences among options of a human decision-maker. The evolution is based on the subjective evaluation for the options and his/her attention on an attribute (interest). In this paper, we extend DFT to cope with the dynamically changing environment. The proposed extended DFT (EDFT) updates the subjective evaluation for the options and the attention on the attribute, where Bayesian belief network (BBN) is employed to infer these updates under the dynamic environment. Four important theorems are derived regarding the extension, which enhance the usability of EDFT by providing the minimum time steps required to obtain the stabilized results before running the simulation (under certain assumptions). A human-in-the-loop experiment is conducted for the virtual stock market to illustrate and validate the proposed EDFT. The preliminary result is quite promising.