Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Bayesian reasoning in an abductive mechanism for argument generation and analysis
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Journal of Biomedical Informatics
Argumentation-Based Inference and Decision Making--A Medical Perspective
IEEE Intelligent Systems
Probabilistic Semantics for the Carneades Argument Model Using Bayesian Networks
Proceedings of the 2010 conference on Computational Models of Argument: Proceedings of COMMA 2010
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
PISA: A framework for multiagent classification using argumentation
Data & Knowledge Engineering
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With some sense-making software, investigators can use causal networks to visualize possible stories explaining the evidence. Despite the different domains, there are interesting correspondences between that type of application and a proposed intelligent learning environment (ILE) in which science students could visualize and debate causal scenarios accounting for clinical findings. The proposed ILE will extend the design of the GenIE Assistant, a system to generate first-draft genetic counseling letters. This paper compares the underlying computational models of sense-making software and the GenIE Assistant. Then it discusses refinements of the Assistant's causal argumentation schemes to support debate in the ILE. The refinements are at a level of abstraction that seem applicable to computational models for sense-making and evidential reasoning in law.