65 citations · 104 across the 15 of their papers we have counts for
17 papers
P2C: Path to Counterfactuals
Sopam Dasgupta, Sadaf MD Halim, Joaquín Arias +2
Machine-learning models are increasingly driving decisions in high-stakes settings, such as finance, law, and hiring, thus, highlighting the need for transparency. However, the key…
MC3G: Model Agnostic Causally Constrained Counterfactual Generation
Sopam Dasgupta, Sadaf MD Halim, Joaquín Arias +2
Machine learning models increasingly influence decisions in high-stakes settings such as finance, law and hiring, driving the need for transparent, interpretable outcomes. However,…
Modeling Deontic Modal Logic in ASP
Gopal Gupta, Abhiramon Rajasekharan, Alexis R. Tudor +2
We consider the problem of implementing deontic modal logic. We show how (deontic) modal operators can be elegantly and directly expressed using default negation (negation-as-failu…
CoGS: Model Agnostic Causality Constrained Counterfactual Explanations using goal-directed ASP
Sopam Dasgupta, Joaquín Arias, Elmer Salazar +1
Machine learning models are increasingly used in critical areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes…
Automating Semantic Analysis of System Assurance Cases using Goal-directed ASP
Anitha Murugesan, Isaac Wong, Joaquín Arias +6
Assurance cases offer a structured way to present arguments and evidence for certification of systems where safety and security are critical. However, creating and evaluating these…
CoGS: Causality Constrained Counterfactual Explanations using goal-directed ASP
Sopam Dasgupta, Joaquín Arias, Elmer Salazar +1
Machine learning models are increasingly used in areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transpa…
CFGs: Causality Constrained Counterfactual Explanations using goal-directed ASP
Sopam Dasgupta, Joaquín Arias, Elmer Salazar +1
Machine learning models that automate decision-making are increasingly used in consequential areas such as loan approvals, pretrial bail approval, and hiring. Unfortunately, most o…
Counterfactual Generation with Answer Set Programming
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail approval, hiring, and many more. Unfo…
Counterfactual Explanation Generation with s(CASP)
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail, hiring, and many more. Unfortunately…
Graph-based Interpretation of Normal Logic Programs
Fang Li, Elmer Salazar, Gopal Gupta
In this paper we present a dependency graph-based method for computing the various semantics of normal logic programs. Our method employs \textit{conjunction nodes} to unambiguousl…
DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots
Fang Li, Huaduo Wang, Kinjal Basu +2
We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot. Commonsense knowledge about v…
Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming
Brendan Hall, Sarat Chandra Varanasi, Jan Fiedor +7
We consider requirements for cyber-physical systems represented in constrained natural language. We present novel automated techniques for aiding in the development of these requir…
Constraint Answer Set Programming without Grounding
Joaquín Arias, Manuel Carro, Elmer Salazar +2
Extending ASP with constraints (CASP) enhances its expressiveness and performance. This extension is not straightforward as the grounding phase, present in most ASP systems, remove…
Computing Stable Models of Normal Logic Programs Without Grounding
Kyle Marple, Elmer Salazar, Gopal Gupta
We present a method for computing stable models of normal logic programs, i.e., logic programs extended with negation, in the presence of predicates with arbitrary terms. Such prog…
Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
Zhuo Chen, Elmer Salazar, Kyle Marple +4
Management of chronic diseases such as heart failure (HF) is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a commi…
A New Algorithm to Automate Inductive Learning of Default Theories
Farhad Shakerin, Elmer Salazar, Gopal Gupta
In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patter…
A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns
Zhuo Chen, Kyle Marple, Elmer Salazar +2
Management of chronic diseases such as heart failure, diabetes, and chronic obstructive pulmonary disease (COPD) is a major problem in health care. A standard approach that the med…