Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Computer Science

First Advisor

Xiaolin Hu

Second Advisor

Sushil Prasad

Third Advisor

Xiaojun Cao

Fourth Advisor

Mariana Montiel


Multi-agent multi-team systems are commonly seen in environments where hierarchical layers of goals are at play. For example, theater-wide combat scenarios where multiple levels of command and control are required for proper execution of goals from the general to the foot soldier. Similar structures can be seen in game environments, where agents work together as teams to compete with other teams. The different agents within the same team must, while maintaining their own ‘personality’, work together and coordinate with each other to achieve a common team goal. This research develops strategy-based multi-agent multi-team systems, where strategy is framed as an instrument at the team level to coordinate the multiple agents of a team in a cohesive way. A formal specification of strategy and strategy-based multi-agent multi-team systems is provided. A framework is developed called SiMAMT (strategy- based multi-agent multi-team systems). The different components of the framework, including strategy simulation, strategy inference, strategy evaluation, and strategy selection are described. A graph-matching approximation algorithm is also developed to support effective and efficient strategy inference. Examples and experimental results are given throughout to illustrate the proposed framework, including each of its composite elements, and its overall efficacy.

This research make several contributions to the field of multi-agent multi-team systems: a specification for strategy and strategy-based systems, and a framework for implementing them in real-world, interactive-time scenarios; a robust simulation space for such complex and intricate interaction; an approximation algorithm that allows for strategy inference within these systems in interactive-time; experimental results that verify the various sub-elements along with a full-scale integration experiment showing the efficacy of the proposed framework.