Multi-agent belief change is an area concerned with the belief dynamics of a network of communicating agents. A network is represented by a graph, where vertices represent agents that share information via a process of minimizing disagreements between themselves. Previous work by Delgrande, Lang, and Schaub addressed belief change through global minimization, with a weak notion of distance between agents. We extend it by applying iterative procedures that take distance into account. We have identified two approaches to iteration: in the first, a vertex incorporates information from its immediate neighbours only; in the second, a vertex incorporates information from progressively more distant neighbours. Our research has both theoretical and practical contributions: first, we define the iterative approaches, find relationships between them, and investigate their logical properties; then, we introduce a software system called Equibel that implements both the global and iterative approaches, using Answer Set Programming and Python.
Copyright is held by the author.
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Delgrande, James
Member of collection