Argument Calculus and Networks

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📝 Original Info

  • Title: Argument Calculus and Networks
  • ArXiv ID: 1303.1504
  • Date: 2013-03-08
  • Authors: Researchers from original ArXiv paper

📝 Abstract

A major reason behind the success of probability calculus is that it possesses a number of valuable tools, which are based on the notion of probabilistic independence. In this paper, I identify a notion of logical independence that makes some of these tools available to a class of propositional databases, called argument databases. Specifically, I suggest a graphical representation of argument databases, called argument networks, which resemble Bayesian networks. I also suggest an algorithm for reasoning with argument networks, which resembles a basic algorithm for reasoning with Bayesian networks. Finally, I show that argument networks have several applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.

💡 Deep Analysis

Deep Dive into Argument Calculus and Networks.

A major reason behind the success of probability calculus is that it possesses a number of valuable tools, which are based on the notion of probabilistic independence. In this paper, I identify a notion of logical independence that makes some of these tools available to a class of propositional databases, called argument databases. Specifically, I suggest a graphical representation of argument databases, called argument networks, which resemble Bayesian networks. I also suggest an algorithm for reasoning with argument networks, which resembles a basic algorithm for reasoning with Bayesian networks. Finally, I show that argument networks have several applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.

📄 Full Content

A major reason behind the success of probability calculus is that it possesses a number of valuable tools, which are based on the notion of probabilistic independence. In this paper, I identify a notion of logical independence that makes some of these tools available to a class of propositional databases, called argument databases. Specifically, I suggest a graphical representation of argument databases, called argument networks, which resemble Bayesian networks. I also suggest an algorithm for reasoning with argument networks, which resembles a basic algorithm for reasoning with Bayesian networks. Finally, I show that argument networks have several applications: Nonmonotonic reasoning, truth maintenance, and diagnosis.

Reference

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