Abstract
Diagnosis is widely used in many different disciplines to identify the nature and cause of a certain phenomenon. We present \(t\mathsf {L}\), a new logical framework able to formalise diagnostic reasoning, i.e., an hybrid learning technique based both on deduction and experiments. In this paper we introduce tL, a Labeled Modal Logic, garnishing with temporal and statistical information and a basic propositional language.
After proposing examples on how tL effectively works, we sketch the main ideas about the full deduction system à la Prawitz we are currently developing.
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Notes
- 1.
Roughly speaking, a false positive, commonly called a ‘false alarm’, is a result that indicates a given condition exists, when it does not, and on the reverse way for false negative.
- 2.
In principle, tests can be considered without false negatives when the number of false negative results is irrelevant to the decision process.
- 3.
If a test was both correct and complete, then the test would be the property it reveals.
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Cristani, M., Olivieri, F., Tomazzoli, C., Zorzi, M. (2019). Towards a Logical Framework for Diagnostic Reasoning. In: Jezic, G., Chen-Burger, YH., Howlett, R., Jain, L., Vlacic, L., Šperka, R. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2018. KES-AMSTA-18 2018. Smart Innovation, Systems and Technologies, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-92031-3_14
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