If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Winograd schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.
This paper deals with the relationship between intelligent behaviour, on the one hand, and the mental qualities needed to produce it, on the other. We consider two well-known opposing positions on this issue: one due to Alan Turing and one due to John Searle (via the Chinese Room). In particular, we argue against Searle, showing that his answer to the so-called System Reply does not work. The argument takes a novel form: we shift the debate to a different and more plausible room where the required conversational behaviour is much easier to characterize and to analyze. Despite being much simpler than the Chinese Room, we show that the behaviour there is still complex enough that it cannot be produced without appropriate mental qualities.
Mitchell, Tom, Levesque, Hector
Mitchell and Levesque provide commentary on the two AAAI Classic Paper awards, given at the AAAI-05 conference in Pittsburgh, Pennsylvania. The two winning papers were "Quantifying the Inductive Bias in Concept Learning," by David Haussler, and "Default Reasoning, Nonmonotonic Logics, and the Frame Problem," by Steve Hanks and Drew McDermott.
See also:A Fundamental Tradeoff in Knowledge Representation and Reasoning. Slides. Department of Computer and Information Science. Norwegian University of Science and Technology. IT3706 - Knowledge Representation and Modelling, 2005.Knowledge Representation and Reasoning. Morgan Kaufmann, 2004.Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1989.Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (1st ed.). James Allen, Ronald J. Brachman, Erik Sandewall, Hector J. Levesque, Ray Reiter, and Richard Fikes (Eds.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.Annual Review of Computer Science Vol. 1: 255-287