Inductive learning of answer set programs for autonomous surgical task planning Application to a training task for surgeons

• Published in: Machine learning Vol. 110; no. 7; pp. 1739 - 1763
• Main Authors: Meli, Daniele, Sridharan, Mohan, Fiorini, Paolo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-07-2021
• Subjects: Artificial Intelligence; Computer Science; Control; Machine Learning; Mechatronics; Natural Language Processing (NLP); Robotics; Simulation and Modeling; Surgical robotics; Inductive logic programming; Answer set programming
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-021-06013-7

The quality of robot-assisted surgery can be improved and the use of hospital resources can be optimized by enhancing autonomy and reliability in the robot’s operation. Logic programming is a good choice for task planning in robot-assisted surgery because it supports reliable reasoning with domain knowledge and increases transparency in the decision making. However, prior knowledge of the task and the domain is typically incomplete, and it often needs to be refined from executions of the surgical task(s) under consideration to avoid sub-optimal performance. In this paper, we investigate the applicability of inductive logic programming for learning previously unknown axioms governing domain dynamics. We do so under answer set semantics for a benchmark surgical training task, the ring transfer. We extend our previous work on learning the immediate preconditions of actions and constraints, to also learn axioms encoding arbitrary temporal delays between atoms that are effects of actions under the event calculus formalism. We propose a systematic approach for learning the specifications of a generic robotic task under the answer set semantics, allowing easy knowledge refinement with iterative learning. In the context of 1000 simulated scenarios, we demonstrate the significant improvement in performance obtained with the learned axioms compared with the hand-written ones; specifically, the learned axioms address some critical issues related to the plan computation time, which is promising for reliable real-time performance during surgery.