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Chapter 10SOCIAL INTELLIGENCE FOR COMPUTERSMaking Artificial Entities Creative in their InteractionsJuliette RouchierGREQAM (CNRS)AbstractI review two main principles that have been developed to coordinate artificialagents in Multi-Agent systems. The first is based on the elaboration of complexcommunications among highly cognitive agents. The other is eco-resolution,where very simple agents have no consciousness of the existence of others. Bothapproaches fail to produce a social life that is close to that of humans, in terms ofcreativity or exchange of abstractions. Humans can build new ways of communicating,even with unknown entities, because they suppose that the other is ableto give a meaning to messages, and are able to transfer a protocol from one socialfield to another. Since we want social intelligence to be creative, it seems thata first step would be to have agents be willing to communicate and know morethan one reason and way to do so.1. IntroductionHere, I compare computers’ social intelligence to the human one. There isno generally agreed definition of social intelligence, but several elements seemto be indicated by the difference between human intelligence and more basiccognitions. These include: the ability to communicate with others in orderto undertake common actions; the ability displayed by a society to integratenewcomers (and conversely for individuals to adapt to new ways of interacting)in order to communicate with unknown people; the ability to understand whatothers want from you, how you can help, or conversely influence others so thatthey help you [1].Some progress has recently been made towards the understanding of socialintelligence for non-living entities in the field of Multi-Agent Systems (MAS).MAS focuses on the socialisation of artificial intelligences using a variety ofapproaches. Attempts to create a real artificial intelligence are often based upon