'Answer sets' diaporamas de présentation

Automating Common Sense Reasoning

Automating Common Sense Reasoning. Gopal Gupta Elmer Salazar, Kyle Marple, Zhuo Chen, Farhad Shakerin (and many others) Department of Computer Science The University of Texas at Dallas Support from NSF is gratefully acknowledged. Artificial Intelligence.

527 views • 52 slides

Automating Common Sense Reasoning

Automating Common Sense Reasoning. Gopal Gupta Department of Computer Science The University of Texas at Dallas. Artificial Intelligence. Intelligent reasoning by computers has been a goal of computer scientists ever since computers were first invented in the 1950s.

514 views • 49 slides

Answer set programming

Answer set programming. See Eiter et al 2009. Motivation: strong negation. Programme: man(dilbert). single(X) :- man(X), not husband(X). husband(X) :- man(X), not single(X). Expressing negative fact: - single(dilbert). Motivation: strong negation (2). Negation as failure :

572 views • 41 slides

Goal-directed Execution of Answer Set Programs

Goal-directed Execution of Answer Set Programs. Kyle Marple, Ajay Bansal, Richard Min, Gopal Gupta Applied Logic, Programming-Languages and Systems (ALPS) Lab The University of Texas at Dallas. Outline. Answer Set Programming General overview Odd Loops Over Negation (OLONs)

385 views • 26 slides

Goal-directed Execution of Answer Set Programs

Goal-directed Execution of Answer Set Programs. Kyle Marple, Ajay Bansal, Richard Min, Gopal Gupta Applied Logic, Programming-Languages and Systems (ALPS) Lab The University of Texas at Dallas. Outline. Answer Set Programming General overview Odd Loops Over Negation (OLONs)

266 views • 26 slides

Using answer set programming to answer complex queries

Using answer set programming to answer complex queries. Chitta Baral (joint work with Michael Gelfond and Richard Scherl) Arizona State University Tempe, AZ 86287 chitta@asu.edu http://www.public.asu.edu/~cbaral. QUERIES. Queries and Answers.

410 views • 24 slides

SciFinder Scholar 2007

SciFinder Scholar 2007. An overview of the new features. Created by: Andrea Wright Science Reference Thomas Cooper Library June 25, 2007. SciFinder Scholar 2007. There are six new features in the latest version of SciFinder Scholar: Categorize Answer Sets Save Answer Sets

276 views • 17 slides

SciFinder Scholar 2007

SciFinder Scholar 2007. An overview of the new features. Created by: Andrea Wright Science Reference Thomas Cooper Library June 25, 2007. SciFinder Scholar 2007. There are six new features in the latest version of SciFinder Scholar: Categorize Answer Sets Save Answer Sets

309 views • 17 slides

Logic Programs and Classical Planning with LTL Constraints

219 views • 12 slides

Learning by Answer Sets

Learning by Answer Sets. Chiaki Sakama Wakayama University, Japan. Presented at AAAI Spring Symposium on Answer Set Programming, March 2001. Nonmonotonic LP (NMLP) vs. Inductive LP (ILP). Purpose of Research. NMLP and ILP have different goals and complement each other.

209 views • 12 slides

The coherence principle

284 views • 11 slides