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  • 标题:Responsibility and Blame: A Structural-Model Approach
  • 本地全文:下载
  • 作者:H. Chockler ; J. Y. Halpern
  • 期刊名称:Journal of Artificial Intelligence Research
  • 印刷版ISSN:1076-9757
  • 出版年度:2004
  • 卷号:22
  • 页码:93-115
  • 出版社:American Association of Artificial
  • 摘要:Causality is typically treated an all-or-nothing concept; either A is a cause of B or it is not. We extend the definition of causality introduced by Halpern and Pearl [2004a] to take into account the degree of responsibility of A for B. For example, if someone wins an election 11-0, then each person who votes for him is less responsible for the victory than if he had won 6-5. We then define a notion of degree of blame, which takes into account an agent's epistemic state. Roughly speaking, the degree of blame of A for B is the expected degree of responsibility of A for B, taken over the epistemic state of an agent.
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