Reasoning under Uncertainty

Díez, F. J.; Drużdżel, Marek J.

doi:10.1002/0470018860.s00028

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…There is a degree of uncertainty [7] attached to such human reasoning. Whilst humans are adept at reasoning with uncertainty that may involve different confidence levels for different aspects of their knowledge, it is very challenging to incorporate this in rulebased systems for decision making that suitably reflects human usage.…”

Section: Introduction: Reasoning Under Uncertaintymentioning

confidence: 99%

Uncertainty Management for Rule-Based Decision Support Systems

Mahesar

Dimitrova

Magee

et al. 2017

2017 IEEE 29th International Conference on Tools With Artificial Intelligence (ICTAI)

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Abstract-We present an uncertainty management scheme in rule-based systems for decision making in the domain of urban infrastructure. Our aim is to help end users make informed decisions. Human reasoning is prone to a certain degree of uncertainty but domain experts frequently find it difficult to quantify this precisely, and thus prefer to use qualitative (rather than quantitative) confidence levels to support their reasoning. Secondly, there is uncertainty in data when it is not currently available (missing). In order to incorporate human-like reasoning within rule-based systems we use qualitative confidence levels chosen by domain experts in urban infrastructure. We introduce a mechanism for the representation of confidence of input facts and inference rules, and for the computation of confidence in the inferred facts. We also present a mechanism for computing inferences in the presence of missing facts, and their effect on the confidence of inferred facts.

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Section: Introduction: Reasoning Under Uncertaintymentioning

confidence: 99%

Uncertainty Management for Rule-Based Decision Support Systems

Mahesar

Dimitrova

Magee

et al. 2017

2017 IEEE 29th International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

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A comparative modeling study on non-climatic and climatic risk assessment on Asian Tiger Mosquito (Aedes albopictus)

Shabani

Tehrany²,

Solhjouy‐Fard

et al. 2018

PeerJ

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Aedes albopictus, the Asian Tiger Mosquito, vector of Chikungunya, Dengue Fever and Zika viruses, has proven its hardy adaptability in expansion from its natural Asian, forest edge, tree hole habitat on the back of international trade transportation, re-establishing in temperate urban surrounds, in a range of water receptacles and semi-enclosures of organic matter. Conventional aerial spray mosquito vector controls focus on wetland and stagnant water expanses, proven to miss the protected hollows and crevices favoured by Ae. albopictus. New control or eradication strategies are thus essential, particular in light of potential expansions in the southeastern and eastern USA. Successful regional vector control strategies require risk level analysis. Should strategies prioritize regions with non-climatic or climatic suitability parameters for Ae. albopictus? Our study used current Ae. albopictus distribution data to develop two independent models: (i) regions with suitable non-climatic factors, and (ii) regions with suitable climate for Ae. albopictus in southeastern USA. Non-climatic model processing used Evidential Belief Function (EBF), together with six geographical conditioning factors (raster data layers), to establish the probability index. Validation of the analysis results was estimated with area under the curve (AUC) using Ae. albopictus presence data. Climatic modeling was based on two General Circulation Models (GCMs), Miroc3.2 and CSIRO-MK30 running the RCP 8.5 scenario in MaxEnt software. EBF non-climatic model results achieved a 0.70 prediction rate and 0.73 success rate, confirming suitability of the study site regions for Ae. albopictus establishment. The climatic model results showed the best-fit model comprised Coldest Quarter Mean Temp, Precipitation of Wettest Quarter and Driest Quarter Precipitation factors with mean AUC value of 0.86. Both GCMs showed that the whole study site is highly suitable and will remain suitable climatically, according to the prediction for 2055, for Ae. albopictus expansion.

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Reasoning under Uncertainty

Cited by 2 publications

References 40 publications

Uncertainty Management for Rule-Based Decision Support Systems

Uncertainty Management for Rule-Based Decision Support Systems

A comparative modeling study on non-climatic and climatic risk assessment on Asian Tiger Mosquito (Aedes albopictus)

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