Synthesising Liberal Normative Systems

Norms have been extensively studied to coordinate multi-agent systems, and the literature has investigated two general approaches to norm synthesis: off-line (synthesising norms at design-time) and on-line (run-time synthesis). On-line synthesis is generally recognised to be appropriate for open systems, where aspects of the system remain unknown at design-time. In this paper we present LION, an algorithm aimed at synthesising liberal normative systems. LION's normative systems respect the agents' autonomy to the greatest possible extent, constraining their behaviour when only necessary to avoid undesirable system states. LION's norm synthesis is also driven by the need to construct compact normative systems. The key to the success of LION in this multi-objective synthesis process is that it learns about and exploits norm synergies. More precisely, LION can learn when norms are either substitutable or complementary. We show empirically that LION significantly outperforms the state of the art by synthesising normative systems that are more liberal while maintaining representation compactness.

[1]  P. Samuelson.  Complementarity-An Essay on the 40th Anniversary of the Hicks-Allen Revolution in Demand Theory , 1974 .

[2]  Ya-lun Chou,et al.  Statistical analysis, with business and economic applications , 1975 .

[3]  George W McKenzie.  Complementarity, Substitutability, and Independence , 1977 .

[4]  Moshe Tennenholtz,et al.  On Social Laws for Artificial Agent Societies: Off-Line Design , 1995, Artif. Intell..

[5]  Moshe Tennenholtz,et al.  Minimal Social Laws , 1998, AAAI/IAAI.

[6]  Rudolf van der Velden,et al.  The training of school-leavers: Complementarity or substitution? , 2000 .

[7]  Frank Dignum,et al.  Autonomous agents with norms , 1999, Artificial Intelligence and Law.

[8]  Guido Boella,et al.  Introduction to normative multiagent systems , 2006, Comput. Math. Organ. Theory.

[9]  Michael Wooldridge,et al.  On the Logic of Normative Systems , 2007, IJCAI.

[10]  Sandip Sen,et al.  Emergence of Norms through Social Learning , 2007, IJCAI.

[11]  Andrew Ward,et al.  Governance Bundles, Firm Performance, and the Substitutability and Complementarity of Governance Mechanisms , 2009 .

[12]  Sandip Sen,et al.  Effects of Social Network Topology and Options on Norm Emergence , 2009, COIN@AAMAS&IJCAI&MALLOW.

[13]  Gabriel M. Ahlfeldt,et al.  Substitutability and Complementarity of Urban Amenities: External Effects of Built Heritage in Berlin , 2010 .

[14]  Michael Luck,et al.  Norm Emergence in Tag-Based Cooperation , 2010 .

[15]  Bastin Tony Roy Savarimuthu,et al.  Obligation Norm Identification in Agent Societies , 2010, J. Artif. Soc. Soc. Simul..

[16]  Michael Luck,et al.  Norm Diversity and Emergence in Tag-Based Cooperation , 2010, COIN@AAMAS&MALLOW.

[17]  Sandip Sen,et al.  Social Instruments for Robust Convention Emergence , 2011, IJCAI.

[18]  Marc Esteva,et al.  Using Experience to Generate New Regulations , 2011, IJCAI.

[19]  Eamonn J. Keogh,et al.  Experimental comparison of representation methods and distance measures for time series data , 2010, Data Mining and Knowledge Discovery.

[20]  Bastin Tony Roy Savarimuthu,et al.  Identifying prohibition norms in agent societies , 2012, Artificial Intelligence and Law.

[21]  Michael Wooldridge,et al.  Automated synthesis of normative systems , 2013, AAMAS.

[22]  Nathan Griffiths,et al.  Learning agent influence in MAS with complex social networks , 2014, Autonomous Agents and Multi-Agent Systems.

[23]  Mehdi Dastani,et al.  Norm approximation for imperfect monitors , 2014, AAMAS.

[24]  Michael Wooldridge,et al.  Minimality and simplicity in the on-line automated synthesis of normative systems , 2014, AAMAS.