Abstract
Intelligent virtual agents are typically embedded in a social environment and must reason about social cause and effect. Social causal reasoning is qualitatively different from physical causal reasoning that underlies most current intelligent systems. Besides physical causality, the assessments of social cause emphasize epistemic variables including intentions, foreknowledge and perceived coercion. Modeling the process and inferences of social causality can enrich the believability and the cognitive capabilities of social intelligent agents. In this paper, we present a general computational model of social causality and responsibility, and empirically evaluate and compare the model with several other approaches.
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Mao, W., Gratch, J. (2005). Social Causality and Responsibility: Modeling and Evaluation. In: Panayiotopoulos, T., Gratch, J., Aylett, R., Ballin, D., Olivier, P., Rist, T. (eds) Intelligent Virtual Agents. IVA 2005. Lecture Notes in Computer Science(), vol 3661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550617_17
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DOI: https://doi.org/10.1007/11550617_17
Publisher Name: Springer, Berlin, Heidelberg
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