Exploiting Vagueness for Multi-Agent Consensus

Abstract

The ultimate objective of artificial intelligence is to develop intelligent agents that can think andact rationally. In intelligent systems, agents rarely exist in isolation, but instead form part of alarger group of agents all sharing the same (or similar) goals. As such, a population of agentsneeds to be able to reach an agreement about the state of the world efficiently and accurately, and in a distributed manner, so that they can then make collective decisions.In this thesis we attempt to exploit vagueness in natural language so as to allow agents to bemore effective in forming consensus. In classical logic, a proposition can be either true or false, which inevitably leads to situations in which agents that disagree about the truth of a propositioncannot resolve their inconsistencies in an intuitive manner. By adopting an intermediate truthstate in cases where there is direct conflict between the beliefs of agents (i.e., where one believes the proposition to be true, and the other believes it to be false), we can combine the beliefs ofagents in order to form consensus. We can then repeat this process across the population byforming consensus between agents in an iterative manner, until the population converges to asingle, shared belief. This forms the basis of our initial model. We then extend this model ofconsensus for vague beliefs to take account of epistemic uncertainty. After demonstrating strongconvergence properties of both models, we apply our work to a swarm of 400 Kilobot robots,and study the resulting convergence in such a setting. Finally, we propose a model of consensusin which agents attempt to reach an agreement about a set of compound sentences, rather thanjust a set of propositional variables.

Date of Award	20 Mar 2018
Original language	English
Awarding Institution	The University of Bristol
Supervisor	Jonathan Lawry (Supervisor)