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Found 13 papers, 3 papers with code
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.
Counterfactual Explanation Generation with s(CASP)
Our approach utilizes answer set programming and the s(CASP) goal-directed ASP system.
FOLD-RM: A Scalable, Efficient, and Explainable Inductive Learning Algorithm for Multi-Category Classification of Mixed Data
FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed (numerical and categorical) data.
An ASP-based Approach to Answering Natural Language Questions for Texts
The resulting knowledge-base can then be used to perform reasoning with the help of an ASP system.
A Clustering and Demotion Based Algorithm for Inductive Learning of Default Theories
We present a clustering- and demotion-based algorithm called Kmeans-FOLD to induce nonmonotonic logic programs from positive and negative examples.
Knowledge-driven Natural Language Understanding of English Text and its Applications
Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research.
SQuARE: Semantics-based Question Answering and Reasoning Engine
We introduce a general semantics-based framework for natural language QA and also describe the SQuARE system, an application of this framework.
White-box Induction From SVM Models: Explainable AI with Logic Programming
In our new approach, however, the data-dependent hill-climbing search is replaced with a model-dependent search where a globally optimal SVM model is trained first, then the algorithm looks into support vectors as the most influential data points in the model, and induces a clause that would cover the support vector and points that are most similar to that support vector.
Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.
Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models.
Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers.
Heuristic Based Induction of Answer Set Programs: From Default theories to combinatorial problems
To the best of our knowledge, this is the first heuristic-based ILP algorithm to induce answer set programs with multiple stable models.
Logic in Computer Science
A New Algorithm to Automate Inductive Learning of Default Theories
An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories.
Logic in Computer Science
