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Counterfactual Generation with Answer Set Programming
In our framework, we show how counterfactual explanations are computed and justified by imagining worlds where some or all factual assumptions are altered/changed.
A Survey of the Various Methodologies Towards making Artificial Intelligence More Explainable
Machines are being increasingly used in decision-making processes, resulting in the realization that decisions need explanations.
Counterfactual Explanation Generation with s(CASP)
Our approach utilizes answer set programming and the s(CASP) goal-directed ASP system.
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