Paper Info
Title
Exploiting domain knowledge to improve norm synthesis
Abstract
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.
Year
DOI
Venue
2010
10.5555/1838206.1838317
AAMAS
Keywords
Field
DocType
norm synthesis,exploiting domain knowledge,applicable norm,operator abstraction,constraining agent behaviour,example domain,complete state enumeration,first-order extension,multiagent system,social objective,planning-based solution,social norm,first order,domain knowledge,social norms,conflict resolution
Abstraction,Domain knowledge,Bitwise operation,Computer science,Conflict resolution,Correctness,Norm (social),Theoretical computer science,Propositional representation,Multi-agent system,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
6
0.48
9
Authors
3
Name
Order
Citations
PageRank
George Christelis1191.14
Michael Rovatsos276773.71
Ronald P. A. Petrick330924.24