Controlling Agents in Multi-Agent Systems: From Centralized Coordination to Strategic Planning

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Controlling Agents in Multi-Agent Systems: From Centralized 
Coordination to Strategic Planning"

By

Mr. Fengming ZHU


Abstract:

Multi-agent and multi-robot systems have garnered increasing attention across 
both theoretical domains (e.g., control theory and game theory), and 
practical applications (e.g., warehouse automation and human-robot 
collaboration). This thesis explores two complementary research directions 
toward the problem of controlling those agents in such systems. First, we 
investigate centralized control of robotic agents, framed as a universal 
planning problem that can be interpreted through the lens of constraint 
satisfaction. We therefore adopt logic programming as a declarative approach 
to model and solve the problem. However, this declarative paradigm suffers 
from the well-known grounding bottleneck, which unavoidably leads to poor 
scalability. Informed by this limitation, we later develop a rule-based but 
imperative system for warehouse automation in an industrial scenario, where 
multi-robot planning in a real-world scale is even interleaved with online 
task scheduling. It turns out to be beneficial if one also takes the 
advantage of warehouse environments while remaining the flexibility for 
varying scales of robot fleets.

Secondly, we are interested in the problem of controlling one single agent 
with the presence of multiple other agents about whom the protagonist agent 
only has limited prior knowledge. By modelling this setting as a strategic 
planning problem with an underlying skeleton of stochastic games, we manage 
to draw a unified framework to encompass a spectrum of theoretic 
formulations, such that a family of induced planners can be implemented and 
evaluated in a common language. Importantly, our computational framework can 
be carried over from stationary (0-memory) strategies to general K-memory 
strategies. We also formally show that best responding to mixed K- memory 
strategies is significantly harder than best responding to a single 
(potentially randomized) K-memory strategy.

Notably, our work also exhibits some overlap with research in cognitive 
science, particularly the study of Theory of Mind (ToM), and can be naturally 
applied to agent- based modelling in stock markets and proactive agents in 
robotics.


Date:                   Friday, 29 May 2026

Time:                   2:00pm - 4:00pm

Venue:                  Room 2128A
                        Lift 19

Chairman:               Prof. Shuhuai YAO (MAE)

Committee Members:      Prof. Fangzhen LIN (Supervisor)
                        Prof. Nevin ZHANG
                        Dr. Sunil ARYA
                        Dr. Yiding FENG (IEDA)
                        Dr. Dongmo ZHANG (Western Sydney University)