Explaining Aha! moments in artificial agents through IKE-XAI: Implicit Knowledge Extraction for eXplainable AI

During the learning process, a child develops a mental representation of the task he or she is learning. A Machine Learning algorithm develops also a latent representation of the task it learns. We investigate the development of the knowledge construction of an artificial agent through the analysis of its behavior, i.e., its sequences of moves while learning to perform the Tower of Hanoï (TOH) task. The TOH is a well-known task in experimental contexts to study the problem-solving processes and one of the fundamental processes of children’s knowledge construction about their world. We position ourselves in the field of explainable reinforcement learning for developmental robotics, at the crossroads of cognitive modeling and explainable AI. Our main contribution proposes a 3-step methodology named Implicit Knowledge Extraction with eXplainable Artificial Intelligence (IKE-XAI) to extract the implicit knowledge, in form of an automaton, encoded by an artificial agent during its learning. Our experiments show that the IKE-XAI approach helps understanding the development of the Q-learning agent behavior by providing a global explanation of its knowledge evolution during learning. IKE-XAI also allows researchers to identify the agent’s Aha! moment by determining from what moment the knowledge representation stabilizes and the agent no longer learns.

CHRAIBI KAADOUD Ikram;  BENNETOT Adrien;  MAWHIN Barbara;  CHARISI Vasiliki;  DIAZ RODRIGUEZ Natalia; 

2022-09-08

PERGAMON-ELSEVIER SCIENCE LTD

JRC127724

0893-6080 (online),   

https://www.sciencedirect.com/science/article/pii/S0893608022003021?via%3Dihub,    https://publications.jrc.ec.europa.eu/repository/handle/JRC127724,   

10.1016/j.neunet.2022.08.002 (online),