David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
OSCAR is a fully implemented architecture for a cognitive agent, based largely on the author’s work in philosophy concerning epistemology and practical cognition. The seminal idea is that a generally intelligent agent must be able to function in an environment in which it is ignorant of most matters of fact. The architecture incorporates a general-purpose defeasible reasoner, built on top of an efficient natural deduction reasoner for first-order logic. It is based upon a detailed theory about how the various aspects of epistemic and practical cognition should interact, and many of the details are driven by theoretical results concerning defeasible reasoning. The architecture is easily extensible by changing the set of inference schemes supplied to the reasoner. Existing inference schemes handle many kinds of epistemic cognition, including reasoning from perceptual input, causal reasoning and the frame problem, and reasoning defeasibly about probabilities. Work is underway to implement a system of defeasible decisiontheoretic planning.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
John Bolender (2001). A Two-Tiered Cognitive Architecture for Moral Reasoning. Biology and Philosophy 16 (3):339-356.
John Zeleznikow, George Vossos & Daniel Hunter (1993). The IKBALS Project: Multi-Modal Reasoning in Legal Knowledge Based Systems. [REVIEW] Artificial Intelligence and Law 2 (3):169-203.
Robert L. Causey (1991). The Epistemic Basis of Defeasible Reasoning. Minds and Machines 1 (4):437-458.
Added to index2009-01-28
Total downloads2 ( #347,663 of 1,100,742 )
Recent downloads (6 months)1 ( #289,271 of 1,100,742 )
How can I increase my downloads?