How can fuzzy logic enhance decision analysis? This paper explores the application of fuzzy γ-models for decision analysis and making, leveraging these models as information aggregation operators. By using the degree of satisfaction of sub-criteria as input, fuzzy γ-models can output aggregated evidence, offering advantages in complex decision-making scenarios. Furthermore, the research generalizes fuzzy γ-models into a hierarchical network, enabling the decision-making process to aggregate and propagate information through such a network. This trainable network can perceive and interpret complex decisions using those fuzzy models, paving the way for more nuanced and adaptive decision systems. The simulation study examines the learning behaviors of the fuzzy γ-models using two numerical examples, demonstrating the effectiveness of this approach. The network's ability to learn and adapt highlights its potential for enhancing decision-making processes in various fields, especially where the information is complex and uncertain.
This paper, published in the International Journal on Artificial Intelligence Tools, fits well within the journal's scope by presenting an innovative methodology for decision-making using fuzzy logic and hierarchical networks. The focus on trainable networks and simulation studies aligns with the journal's emphasis on practical AI applications and tools.