题目：Recent Advances on Control of Probabilistic Boolean Networks: Optimal Reconstruction and Reinforcement Learning-Based Pinning Control
A Boolean network (BN) is well known as one of the mathematical models in gene regulatory networks. In BNs, time evolution of the state is modeled by a Boolean function. A probabilistic BN is one of the extended BNs. In PBNs, a Boolean function is randomly chosen from the candidates. In the last decade, much attention has been paid to analysis and control of BNs and PBNs. In this presentation, I will talk about two recent topics on PBNs in our research group. First, the optimal reconstruction problem is considered. In this problem, a PBN consisting of the main dynamics and the noisy dynamics can be derived from a part of Boolean functions and data. It is shown that the optimal noisy dynamics is given by a constant. Next, the pinning control problem is considered using reinforcement learning. A pinning controller can be obtained by appropriately designing a reward function.
Koichi Kobayashi received the B.E. degree in 1998 and the M.E. degree in 2000 from Hosei University, and the D.E. degree in 2007 from Tokyo Institute of Technology. From 2000 to 2004, he worked at Nippon Steel Corporation. From 2007 to 2015, he was an Assistant Professor at Japan Advanced Institute of Science and Technology. Since 2015, he has been an Associate Professor at the Graduate School of Information Science and Technology, Hokkaido University. His research interests include discrete event and hybrid systems.