Exploiting domain knowledge to improve norm synthesis

Social norms enable coordination in multiagent systems by constraining agent behaviour in order to achieve a social objective. Automating the design of social norms has been shown to be NP-complete, requiring a complete state enumeration. A planning-based solution has been proposed previously to improve performance. This approach leads to verbose, problem-specific norms due to the propositional representation of the domain. We present a first-order extension of this work that benefits from state and operator abstractions to synthesise more expressive, generally applicable norms. We propose optimisations that can be used to reduce the search performed during synthesis, and formally prove the correctness of these optimisations. Finally, we empirically illustrate the benefits of these optimisations in an example domain.