Information graphs and their use for Bayesian network graph construction
| Autor: | Silja Renooij, Remi Wieten, Floris Bex, Henry Prakken |
|---|---|
| Přispěvatelé: | Transboundary Legal Studies, Sub Intelligent Systems, Intelligent Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Theoretical computer science
Formalism (philosophy) Computer science Applied Mathematics Informal logic Uncertainty Inference Bayesian network Defeasible estate Deduction Rotation formalisms in three dimensions Abductive reasoning Domain (software engineering) Theoretical Computer Science Qualitative probabilistic reasoning Bayesian networks Causal and evidential reasoning Artificial Intelligence Abduction Software |
| Zdroj: | International Journal of Approximate Reasoning, 136, 249-280. ELSEVIER SCIENCE INC International Journal of Approximate Reasoning, 136, 249. Elsevier |
| ISSN: | 0888-613X |
| Popis: | In this paper, we present the information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information, where ‘deduction’ is used for defeasible ‘forward’ inference. IGs formalise analyses performed by domain experts in the informal reasoning tools they are familiar with, such as mind maps used in crime analysis. Based on principles for reasoning with causal and evidential information given the evidence, we impose constraints on the inferences that may be performed with IGs. Our IG-formalism is intended to facilitate the construction of formal representations within AI systems by serving as an intermediary formalism between analyses performed using informal reasoning tools and formalisms that allow for formal evaluation. In this paper, we investigate the use of the IG-formalism as an intermediary formalism in facilitating Bayesian network (BN) graph construction. We propose a structured approach for automatically constructing from an IG a directed BN graph, together with qualitative constraints on the probability distribution represented by the BN. Moreover, we prove a number of formal properties of our approach and identify assumptions under which the construction of an initial BN graph can be fully automated. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect