A Unified Framework for Nonmonotonic Reasoning with Vagueness and Uncertainty

10/01/2019
by   Sandip Paul, et al.
0

An answer set programming paradigm is proposed that supports nonmonotonic reasoning with vague and uncertain information. The system can represent and reason with prioritized rules, rules with exceptions. An iterative method for answer set computation is proposed. The terminating conditions are identified for a class of logic programs using the notion of difference equations. In order to obtain the difference equations the set of rules are depicted by a signal-flow-graph like structure.

READ FULL TEXT
research
08/30/2011

Translating Answer-Set Programs into Bit-Vector Logic

Answer set programming (ASP) is a paradigm for declarative problem solvi...
research
03/13/2013

Interval Structure: A Framework for Representing Uncertain Information

In this paper, a unified framework for representing uncertain informatio...
research
02/27/2020

Belief Base Revision for Further Improvement of Unified Answer Set Programming

A belief base revision is developed. The belief base is represented usin...
research
11/30/2013

Characterizing and Extending Answer Set Semantics using Possibility Theory

Answer Set Programming (ASP) is a popular framework for modeling combina...
research
03/08/2000

Smodels: A System for Answer Set Programming

The Smodels system implements the stable model semantics for normal logi...
research
10/26/2021

How Should AI Interpret Rules? A Defense of Minimally Defeasible Interpretive Argumentation

Can artificially intelligent systems follow rules? The answer might seem...
research
09/22/2020

Extending Answer Set Programs with Neural Networks

The integration of low-level perception with high-level reasoning is one...

Please sign up or login with your details

Forgot password? Click here to reset