Information Technology Reference
In-Depth Information
are stronger than others in a social debate). To accomplish this task, we will an-
alyze real-world debates, like sustainable energy and political discussions within
the e-Policy project.
There is a large literature on revising beliefs in artificial intelligence and knowl-
edge representation. In particular, work by Alchourron et al. [1] was influential
in defining a number of basic postulates (known as
AGM postulates
in the liter-
ature) that a belief revision operator should respect, in order for that operator
to be considered rational. Cayrol et al. [8] propose a framework for revising an
abstract
AF
along these lines. However, considering our application, which is
modeling possible outcomes of human debates, respecting the AGM postulates
may not be a necessary requirement after all. We plan however to investigate
the application of these and other methods, and evaluate which one performs
best in simulating opinion diffusion in social networks.
To the best of our knowledge, our proposal is original in the social sciences,
where argumentation has never been used for social simulation. It represents also
a way for qualitative approaches to fit ABM formal requirements: for instance,
discourse analysis results can be formalized as
AF
and fed into a simulator. Our
approach envisages possible new grounds for cross-fertilization between the social
and computer sciences, whereby surveys could be devised to retrieve arguments
rather than numeric variables, possibly with the aid of argument extraction
tools [23], and ABMs could be calibrated with empirically grounded
AF
s, to
study the spreading of information, ideas and innovations with a finer-grained
realism.
References
1. Alchourron, C.E., Gardenfors, P., Makinson, D.: On the logic of theory change:
Partial meet functions for contraction and revision. J. Symb. Logic 50, 510-530
(1985)
2. Andrighetto, G., Villatoro, D., Conte, R.: Norm internalization in artificial soci-
eties. AI Communications 23, 325-333 (2010)
3. Arieli, O.: Conflict-tolerant semantics for argumentation frameworks. In: del Cerro,
L.F., Herzig, A., Mengin, J. (eds.) JELIA 2012. LNCS, vol. 7519, pp. 28-40.
Springer, Heidelberg (2012)
4. Axelrod, R.M.: The complexity of cooperation: agent-based models of competition
and collaboration. Princeton University Press, Princeton (1997)
5. Axelrod, R.M.: The dissemination of culture: A model with local convergence and
global polarization. J. Conflict Resolut. 41(2), 203-226 (1997)
6. Baroni, P., Giacomin, M.: Semantics of abstract argument systems. In: Argumen-
tation in Artificial Intelligence. Springer (2009)
7. Bistarelli, S., Santini, F.: A common computational framework for semiring-based
argumentation systems. In: ECAI 2010 - 19th European Conference on Artificial
Intelligence, Lisbon, Portugal, pp. 131-136 (August 2010)
8. Cayrol, C., de Saint-Cyr, F.D., Lagasquie-Schiex, M.C.: Revision of an argumen-
tation system. In: Brewka, G., Lang, J. (eds.) Principles of Knowledge Representa-
tion and Reasoning: Proc. 11th Int. Conf., KR 2008, September 16-19, pp. 124-134.
AAAI Press, Sydney (2008)
