Ashish Dhiman

Indian Institute of Technology Kharagpur

Papers

Total Citations

H-Index

About

Ashish Dhiman is a researcher advancing the frontier of multi-agent systems and computational social science, with a focus on how artificial agents can autonomously discover and reason about social norms. His most cited work, "Identifying Norms from Observation Using MCMC Sampling" (2021), tackles a fundamental challenge in dynamic, multi-agent environments: rather than assuming norms are pre-programmed, Dhiman develops techniques that allow agents to infer norms purely from observing interactions. This approach leverages Markov Chain Monte Carlo sampling to efficiently identify the unwritten rules governing agent behavior, a critical step toward more adaptive and autonomous systems. Though early in his citation trajectory—with his top paper garnering 2 citations—Dhiman’s work addresses a pressing gap in the literature, bridging machine learning, game theory, and social simulation. His research holds promise for applications ranging from robotic coordination to modeling human social dynamics, positioning him as an emerging voice in the study of emergent norms and decentralized intelligence.