PhD Dissertation Defense by Rajiv Ranjan KUMAR | Towards Improving System Performance in Large Scale Multi-Agent Systems with Selfish Agents

Intelligent agents are becoming increasingly prevalent in a wide variety of domains including but not limited to transportation, safety and security. To better utilize the intelligence, there has been increasing focus on frameworks and methods for coordinating these intelligent agents. This thesis is specifically targeted at providing solution approaches for improving large scale multi-agent systems with selfish intelligent agents. In such systems, the performance of an agent depends on not just his/her own efforts, but also on other agent’s decisions. The complexity of interactions among multiple agents, coupled with the large-scale nature of the problem domains and the uncertainties associated with the environment, make decision making very challenging. In this work, we specifically study the problem from the perspective of a centralized aggregator, that needs to maximize the revenue of the entire system.

To that end, we study this problem from strategic and operational point of view. With regards to strategic decision making, we propose planning and deep reinforcement learning based solution algorithms to improve the system performance by optimizing the adaptive operating hours of selfish agents and by providing flexible work schedules to them. From operational point of view, we propose novel mechanism to incentivise selfish agents, so that performance of all the agents and the overall system improve . Basically, through strategic and operational decision making, we assist selfish agents in making intelligent decisions that results in improved system performance.

Rajiv Ranjan Kumar is a Ph.D. candidate in the School of Computing and Information Systems, Singapore Management University, advised by Associate Professor Pradeep Varakantham. He received his B. Tech in Computer Science & Engineering from KIIT Bhubaneswar, India, and M. Tech in Computer Science Engineering from NIT, Nagpur, India. He then worked as a senior software engineer at Persistent Systems Limited, Nagpur, before joining as a research engineer in SMU in 2013. His key research interests include optimization and planning for large scale multi-agent systems, data-driven optimization and planning, sequential decision making under uncertainty, game theory and reinforcement learning for multi-agent systems. His PhD work is focused on techniques for improving system performance in large scale multi-agent systems with selfish agents.