Social Causality and Responsibility: Modeling and Evaluation

作者: Wenji Mao , Jonathan Gratch

DOI: 10.1007/11550617_17

关键词:

摘要: Intelligent virtual agents are typically embedded in a social environment and must reason about cause effect. Social causal reasoning is qualitatively different from physical that underlies most current intelligent systems. Besides causality, the assessments of emphasize epistemic variables including intentions, foreknowledge perceived coercion. Modeling process inferences causality can enrich believability cognitive capabilities agents. In this paper, we present general computational model responsibility, empirically evaluate compare with several other approaches.

参考文章(19)
Bertram F. Malle, Dare A. Baldwin, Louis J. Moses, Intentions and Intentionality: Foundations of Social Cognition The MIT Press. ,(2001)
John Langshaw Austin, How to do things with words ,(1962)
H. Chockler, J. Y. Halpern, Responsibility and blame: a structural-model approach Journal of Artificial Intelligence Research. ,vol. 22, pp. 93- 115 ,(2004) , 10.1613/JAIR.1391
Joseph Y. Halpern, Riccardo Pucella, Causes and Explanations: A Structural-Model Approach: Part 1: Causes uncertainty in artificial intelligence. pp. 194- 202 ,(2001)
Jonathan Gratch, Wenji Mao, A Utility-Based Approach to Intention Recognition adaptive agents and multi-agents systems. ,(2004)
Jonathan Gratch, Stacy Marsella, A domain-independent framework for modeling emotion Cognitive Systems Research. ,vol. 5, pp. 269- 306 ,(2004) , 10.1016/J.COGSYS.2004.02.002
Jim Blythe, Decision-Theoretic Planning Ai Magazine. ,vol. 20, pp. 37- 54 ,(1999) , 10.1609/AIMAG.V20I2.1455