Exploiting Vagueness for Multi-Agent Consensus
This addresses consensus modeling in multi-agent systems, particularly for scenarios with vague concepts, but it is incremental as it builds on existing logical frameworks.
The paper tackles the problem of achieving consensus among agents with diverse beliefs by using Kleene's three-valued logic to express vagueness, allowing agents to weaken their beliefs and reduce inconsistency, resulting in convergence to a smaller set of more precise shared beliefs and higher average payoff when payoff-dependent selection is used.
A framework for consensus modelling is introduced using Kleene's three valued logic as a means to express vagueness in agents' beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.