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Found 48 papers, 8 papers with code
A Community-driven vision for a new Knowledge Resource for AI
The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI.
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.
Symbolic Rule Extraction from Attention-Guided Sparse Representations in Vision Transformers
These binarized concept activations are used as input to the FOLD-SE-M algorithm, which generates a rule-set in the form of logic programs.
Reliable Collaborative Conversational Agent System Based on LLMs and Answer Set Programming
As the Large-Language-Model-driven (LLM-driven) Artificial Intelligence (AI) bots became popular, people realized their strong potential in Task-Oriented Dialogue (TOD).
Proceedings 40th International Conference on Logic Programming
Formal and operational semantics: including non-monotonic reasoning, probabilistic reasoning, argumentation, and semantic issues of combining logic with neural models.
Improving Interpretability and Accuracy in Neuro-Symbolic Rule Extraction Using Class-Specific Sparse Filters
In this paper, we identify the root cause of this accuracy loss as the post-training binarization of filter activations to extract the rule-set.
CoGS: Model Agnostic Causality Constrained Counterfactual Explanations using goal-directed ASP
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.
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.
CoGS: Causality Constrained Counterfactual Explanations using goal-directed ASP
Ethical and legal considerations require informing individuals of changes in input attribute values (features) that could lead to a desired outcome for the user.
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.
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks
Given symbolic concepts, as ASP constraints, that the CNN is biased towards, we convert the concepts to their corresponding vector representations.
CFGs: Causality Constrained Counterfactual Explanations using goal-directed ASP
More importantly, we show how CFGs navigates between these worlds, namely, go from our initial state where we obtain an undesired outcome to the imagined goal state where we obtain the desired decision, taking into account the causal relationships among features.
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.
Improved Pothole Detection Using YOLOv7 and ESRGAN
Potholes are common road hazards that is causing damage to vehicles and posing a safety risk to drivers.
Counterfactual Explanation Generation with s(CASP)
Our approach utilizes answer set programming and the s(CASP) goal-directed ASP system.
Using Logic Programming and Kernel-Grouping for Improving Interpretability of Convolutional Neural Networks
FOLD-SE-M then generates a rule-set that can be used to make predictions.
Locksynth: Deriving Synchronization Code for Concurrent Data Structures with ASP
We present Locksynth, a tool that automatically derives synchronization needed for destructive updates to concurrent data structures that involve a constant number of shared heap memory write operations.
Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs
To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.
Reliable Natural Language Understanding with Large Language Models and Answer Set Programming
NLU applications developed using the STAR framework are also explainable: along with the predicates generated, a justification in the form of a proof tree can be produced for a given output.
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.
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.
Parallel Logic Programming: A Sequel
Since its inception, logic programming has been recognized as a programming paradigm with great potential for automated exploitation of parallelism.
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.
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming
These rules are learned in an online manner and applied with an ASP solver to predict an action for the agent.
Inductive logic programming
Natural Language Understanding
+2
Towards Dynamic Consistency Checking in Goal-directed Predicate Answer Set Programming
However, the performance of existing s(CASP) implementations is not on par with other ASP systems: model consistency is checked once models have been generated, in keeping with the generate-and-test paradigm.
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.
Generating Concurrent Programs From Sequential Data Structure Knowledge Using Answer Set Programming
We tackle the problem of automatically designing concurrent data structure operations given a sequential data structure specification and knowledge about concurrent behavior.
Knowledge-Assisted Reasoning of Model-Augmented System Requirements with Event Calculus and Goal-Directed Answer Set Programming
We also show how answer set programming (ASP) and its query-driven implementation s(CASP) can be used to directly realize the event calculus model of the requirements.
Modeling and Reasoning in Event Calculus using Goal-Directed Constraint Answer Set Programming
Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI.
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.
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 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.
Constraint Answer Set Programming without Grounding
The resulting model, s(CASP), can constrain variables that, as in CLP, are kept during the execution and in the answer sets.
Programming Languages Logic in Computer Science
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
Computing Stable Models of Normal Logic Programs Without Grounding
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.
Logic in Computer Science
Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning
A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow.
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
A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns
In this paper we describe a physician-advisory system for CHF management that codes the entire set of clinical practice guidelines for CHF using answer set programming.
