David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Ethics and Information Technology 9 (1):39-47 (2007)
There has been considerable discussion in the past about the assumptions and basis of different ethical rules. For instance, it is commonplace to say that ethical rules are defaults rules, which means that they tolerate exceptions. Some authors argue that morality can only be grounded in particular cases while others defend the existence of general principles related to ethical rules. Our purpose here is not to justify either position, but to try to model general ethical rules with artificial intelligence formalisms and to compute logical consequences of different ethical theories. More precisely, this is an attempt to show that progress in non-monotonic logics, which simulates default reasoning, could provide a way to formalize different ethical conceptions. From a technical point of view, the model developed in this paper makes use of the Answer Set Programming (ASP) formalism. It is applied comparatively to different ethical systems with respect to their attitude towards lying. The advantages of such formalization are two-fold: firstly, to clarify ideas and assumptions, and, secondly, to use solvers to derive consequences of different ethical conceptions automatically, which can help in a rigorous comparison of ethical theories.
|Keywords||Answer Set Programming (ASP) categorical imperative computational ethics default logic non-monotonic logic|
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References found in this work BETA
Immanuel Kant (1785/2002). Groundwork for the Metaphysics of Morals. Oxford University Press.
Luciano Floridi & J. W. Sanders (2004). On the Morality of Artificial Agents. Minds and Machines 14 (3):349-379.
John McCarthy (1980). Circumscription — A Form of Non-Monotonic Reasoning. Artificial Intelligence 13:27–39.
Martin Davis & Hilary Putnam (1966). A Computing Procedure for Quantification Theory. Journal of Symbolic Logic 31 (1):125-126.
Ray Reiter (1980). A Logic for Default Reasoning. Artificial Intelligence 13:81-137.
Citations of this work BETA
Jean-Gabriel Ganascia (2010). Epistemology of AI Revisited in the Light of the Philosophy of Information. Knowledge, Technology & Policy 23 (1-2):57-73.
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