David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Logical AI involves representing knowledge of an agent’s world, its goals and the current situation by sentences in logic. The agent decides what to do by inferring that a certain action or course of action is appropriate to achieve the goals. We characterize brieﬂy a large number of concepts that have arisen in research in logical AI. Reaching human-level AI requires programs that deal with the common sense informatic situation. This in turn requires extensions from the way logic has been used in formalizing branches of mathematics and physical science. It also seems to require extensions to the logics themselves, both in the formalism for expressing knowledge and the reasoning used to reach conclusions. A large number of concepts need to be studied to achieve logical AI of human level. This article presents candidates. The references, though numerous, to articles concerning these concepts are still insuf- ﬁcient, and I’ll be grateful for more, especially for papers available on the web. This article is available in several forms via http://www-formal.stanford.edu/jmc/conceptsai.html.
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Jesse Hughes, Peter Kroes & Sjoerd Zwart (2007). A Semantics for Means-End Relations. Synthese 158 (2):207-231.
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