Handling Unforeseen Failures Using Argumentation-Based Learning
In our paper, a new argumentation-based learning technique is proposed to handle unforeseen failures for a robot. The method is in the category of online incremental learning techniques that uses argumentation theory for modeling the support and attack relations between arguments in the knowledgebase of the robot, which itself is constructed in an online incremental manner.
Hamed Ayoobi is a Ph.D. researcher in the artificial intelligence department of the University of Groningen, Netherlands. He investigates the topics related to machine learning, computer vision, argumentation theory, and robotics. He has a MSc degree in Artificial Intelligence and Robotics from Yazd University, Iran. He was a research intern at Linnaeus University, Sweden working on topics related to verification of concurrent machine code using formal methods.