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{ "item_title" : "Rational Reasoning with Finite Conditional Knowledge Bases", "item_author" : [" Christian Eichhorn "], "item_description" : "Nonmonotonic reasoning is a discipline of computer science, epistemology, and cognition: It models inferences where classical logic is inadequate in symbolic AI, defines normative models for reasoning with defeasible information in epistemology, and models human reasoning under information change in cognition. Its building blocks are defeasible rules formalised as DeFinetti conditionals. In this thesis, Christian Eichhorn examines qualitative and semi-quantitative inference relations on top said conditionals, using the conditional structure of the knowledge base and Spohn's Ordinal Conditional Functions, using established properties. Converting network approaches from probabilistics, he shows how to approach the relations with regard to implementation.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/47/604/823/3476048233_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Rational Reasoning with Finite Conditional Knowledge Bases|Christian Eichhorn

Rational Reasoning with Finite Conditional Knowledge Bases : Theoretical and Implementational Aspects

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Overview

Nonmonotonic reasoning is a discipline of computer science, epistemology, and cognition: It models inferences where classical logic is inadequate in symbolic AI, defines normative models for reasoning with defeasible information in epistemology, and models human reasoning under information change in cognition. Its building blocks are defeasible rules formalised as DeFinetti conditionals. In this thesis, Christian Eichhorn examines qualitative and semi-quantitative inference relations on top said conditionals, using the conditional structure of the knowledge base and Spohn's Ordinal Conditional Functions, using established properties. Converting network approaches from probabilistics, he shows how to approach the relations with regard to implementation.

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Details

  • ISBN-13: 9783476048233
  • ISBN-10: 3476048233
  • Publisher: J.B. Metzler
  • Publish Date: January 2019
  • Dimensions: 8.27 x 5.83 x 0.81 inches
  • Shipping Weight: 1.04 pounds
  • Page Count: 365

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