Formalizing correct evidential reasoning with arguments, scenarios and probabilities
نویسنده
چکیده
Artificial intelligence research on reasoning with criminal evidence in terms of arguments, hypothetical scenarios, and probabilities inspired the approach in this paper. That research showed that Bayesian Networks can be used for modeling arguments and structured hypotheses. Also well-known issues with Bayesian Network were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian Network, for instance as a causal model. The formalism presented here is shown to correspond to a probabilistic interpretation, while answering these issues. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a crime investigation example.
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Proof With and Without Probabilities Correct Evidential Reasoning with Presumptive Arguments, Coherent Hypotheses and Degrees of Uncertainty
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