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Found 14 papers, 3 papers with code
Building Trustworthy AI by Addressing its 16+2 Desiderata with Goal-Directed Commonsense Reasoning
Current advances in AI and its applicability have highlighted the need to ensure its trustworthiness for legal, ethical, and even commercial reasons.
A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP
To validate our proposal, we describe (real) conversations in which the chatbot's goal is to keep the user entertained by talking about movies and books, and s(CASP) ensures (i) correctness of answers, (ii) coherence (and precision) during the conversation, which it dynamically regulates to achieve its specific purpose, and (iii) no deviation from the main topic.
Superfast Selection for Decision Tree Algorithms
We present a novel and systematic method, called Superfast Selection, for selecting the "optimal split" for decision tree and feature selection algorithms over tabular data.
NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classification
We evaluate the performance of our "semantic labelling algorithm" to quantify the efficacy of the semantic labelling for both the NeSy model and the NeSy-EBP model.
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability
A model with smaller number of rules and literals is easier to understand for human beings.
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning To Rank
FOLD-R++ is a new inductive learning algorithm for binary classification tasks.
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.
FOLD-R++: A Toolset for Automated Inductive Learning of Default Theories from Mixed Data
FOLD-R is an automated inductive learning algorithm for learning default rules with exceptions for mixed (numerical and categorical) data.
AUTO-DISCERN: Autonomous Driving Using Common Sense Reasoning
That is, while machine learning technology is good for observing and automatically understanding the surroundings of an automobile, driving decisions are better automated via commonsense reasoning rather than machine learning.
FOLD-R++: A Scalable Toolset for Automated Inductive Learning of Default Theories from Mixed Data
We also create a powerful tool-set by combining FOLD-R++ with s(CASP)-a goal-directed ASP execution engine-to make predictions on new data samples using the answer set program generated by FOLD-R++.
CASPR: A Commonsense Reasoning-based Conversational Socialbot
We present the philosophy behind CASPR's design as well as details of its implementation.
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.
DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots
We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot.
grASP: A Graph Based ASP-Solver and Justification System
As a result, justification for why a literal is in the answer set is hard to produce.
