Traveler Behavior Cognitive Reasoning Mechanism

EasyChair Preprint 11691

Abstract

In this work, we use the kosko's fuzzy cognitive maps to represent the reasoning mechanism in complex dynamic systems. The proposed approach focuses on two points: the first one is to improve the learning process by providing a connection between Kosko’s FCMs and reinforcement learning paradigm, and the second one is to diversify the states of FCM concepts by using an IF-THEN rules base based on the Mamdani-type fuzzy model. An important result is the creation of the transition maps between system states for helpful knowledge representation. After transition maps are validated, they are aggregated and merged as a unique map. This work is simulated under Matlab with Fuzzy Inference System Platform.

Keyphrases: Fuzzy Cognitive Maps, Reinforcement Learning, Traveling Salesman Problem

@booklet{EasyChair:11691,
  author    = {Ahmed Tlili and Salim Chikhi and Ajith Abraham},
  title     = {Traveler Behavior Cognitive Reasoning Mechanism},
  howpublished = {EasyChair Preprint 11691},
  year      = {EasyChair, 2024}}