Computational Models of Natural Argument
|Start||July 13th 2009 (iCal)|
|End||July 13th 2009|
|Papers due:||March 6th 2009|
|Notification:||April 17th 2009|
|Camera ready due:||May 15th 2009|
The series of workshops on Computational Models of Natural Argument is continuing to attract high quality submissions from researchers around the world since its inception in 2001. Like the past editions, CMNA-9 acts to nurture and provide succor to the ever growing community working on Argument and Computation, a field developed in recent years overlapping Argumentation Theory and Artificial Intelligence.
AI has witnessed a prodigious growth in uses of argumentation throughout many of its subdisciplines: agent system negotiation protocols that demonstrate higher levels of sophistication and robustness; argumentation-based models of evidential relations and legal processes that are more expressive; groupwork tools that use argument to structure interaction and debate; computer-based learning tools that exploit monological and dialogical argument structures in designing pedagogic environments; decision support systems that build upon argumentation theoretic models of deliberation to better integrate with human reasoning; and models of knowledge engineering structured around core concepts of argument to simplify knowledge elicitation and representation problems. Furthermore, benefits have not been unilateral for AI, as demonstrated by the increasing presence of AI scholars in classical argumentation theory events and journals, and AI implementations of argument finding application in both research and pedagogic practice within philosophy and argumentation theory.
The workshop focuses on the issue of modelling "natural" argumentation. Naturalness may involve the use of means which are more visual than linguistic to illustrate a point, such as graphics or multimedia. Or to the use of more sophisticated rhetorical devices, interacting at various layers of abstraction. Or the exploitation of "extra-rational" characteristics of the audience, taking into account emotions and affective factors.