Boolean Games: Inferring Agents' Goals Using Taxation Queries

Boolean Games: Inferring Agents' Goals Using Taxation Queries

Abhijin Adiga, Sarit Kraus, Oleg Maksimov, S. S. Ravi

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1585-1591. https://doi.org/10.24963/ijcai.2020/220

In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.
Keywords:
Knowledge Representation and Reasoning: Knowledge Representation and Game Theory; Social Choice
Agent-based and Multi-agent Systems: Agent Theories and Models
Agent-based and Multi-agent Systems: Noncooperative Games