research
∙
08/30/2023
Deep Inductive Logic Programming meets Reinforcement Learning
One approach to explaining the hierarchical levels of understanding with...
share
research
∙
06/08/2023
Learnability with PAC Semantics for Multi-agent Beliefs
The tension between deduction and induction is perhaps the most fundamen...
research
∙
06/08/2023
Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?
In this paper, our aim is to briefly survey and articulate the logical a...
research
∙
06/08/2023
Toward A Logical Theory Of Fairness and Bias
Fairness in machine learning is of considerable interest in recent years...
research
∙
06/07/2023
Synthesising Recursive Functions for First-Order Model Counting: Challenges, Progress, and Conjectures
First-order model counting (FOMC) is a computational problem that asks t...
research
∙
06/04/2023
Agency and legibility for artists through Experiential AI
Experiential AI is an emerging research field that addresses the challen...
research
∙
06/01/2023
Experiential AI: A transdisciplinary framework for legibility and agency in AI
Experiential AI is presented as a research agenda in which scientists an...
research
∙
04/27/2023
Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration
AI and ML models have already found many applications in critical domain...
research
∙
07/26/2022
Using Abstraction for Interpretable Robot Programs in Stochastic Domains
A robot's actions are inherently stochastic, as its sensors are noisy an...
research
∙
04/07/2022
Abstracting Noisy Robot Programs
Abstraction is a commonly used process to represent some low-level syste...
research
∙
02/21/2022
Explainability in Machine Learning: a Pedagogical Perspective
Given the importance of integrating of explainability into machine learn...
research
∙
01/27/2022
Vision Checklist: Towards Testable Error Analysis of Image Models to Help System Designers Interrogate Model Capabilities
Using large pre-trained models for image recognition tasks is becoming i...
research
∙
01/17/2022
Principled Diverse Counterfactuals in Multilinear Models
Machine learning (ML) applications have automated numerous real-life tas...
research
∙
11/02/2021
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks
We propose a novel way to incorporate expert knowledge into the training...
research
∙
10/06/2021
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Many AI applications involve the interaction of multiple autonomous agen...
research
∙
09/13/2021
One Down, 699 to Go: or, synthesising compositional desugarings
Programming or scripting languages used in real-world systems are seldom...
research
∙
10/23/2020
Learning Implicitly with Noisy Data in Linear Arithmetic
Robustly learning in expressive languages with real-world data continues...
research
∙
09/18/2020
Principles and Practice of Explainable Machine Learning
Artificial intelligence (AI) provides many opportunities to improve priv...
research
∙
06/17/2020
Logic, Probability and Action: A Situation Calculus Perspective
The unification of logic and probability is a long-standing concern in A...
research
∙
06/15/2020
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains
The tension between deduction and induction is perhaps the most fundamen...
research
∙
06/02/2020
Generating Random Logic Programs Using Constraint Programming
Testing algorithms across a wide range of problem instances is crucial t...
research
∙
01/29/2020
On Constraint Definability in Tractable Probabilistic Models
Incorporating constraints is a major concern in probabilistic machine le...
research
∙
01/29/2020
Interventions and Counterfactuals in Tractable Probabilistic Models: Limitations of Contemporary Transformations
In recent years, there has been an increasing interest in studying causa...
research
∙
01/15/2020
SMT + ILP
Inductive logic programming (ILP) has been a deeply influential paradigm...
research
∙
11/26/2019
Logical Interpretations of Autoencoders
The unification of low-level perception and high-level reasoning is a lo...
research
∙
10/10/2019
The Quest for Interpretable and Responsible Artificial Intelligence
Artificial Intelligence (AI) provides many opportunities to improve priv...
research
∙
08/06/2019
Experiential AI
Experiential AI is proposed as a new research agenda in which artists an...
research
∙
06/24/2019
Implicitly Learning to Reason in First-Order Logic
We consider the problem of answering queries about formulas of first-ord...
research
∙
05/16/2019
A Correctness Result for Synthesizing Plans With Loops in Stochastic Domains
Finite-state controllers (FSCs), such as plans with loops, are powerful ...
research
∙
05/16/2019
Fairness in Machine Learning with Tractable Models
Machine Learning techniques have become pervasive across a range of diff...
research
∙
01/17/2019
Learning Tractable Probabilistic Models in Open Worlds
Large-scale probabilistic representations, including statistical knowled...
research
∙
11/29/2018
Scaling up Probabilistic Inference in Linear and Non-Linear Hybrid Domains by Leveraging Knowledge Compilation
Weighted model integration (WMI) extends weighted model counting (WMC) i...
research
∙
10/08/2018
Deep Tractable Probabilistic Models for Moral Responsibility
Moral responsibility is a major concern in automated decision-making, wi...
research
∙
10/04/2018
Abstracting Probabilistic Relational Models
Abstraction is a powerful idea widely used in science, to model, reason ...
research
∙
09/14/2018
Reasoning about Discrete and Continuous Noisy Sensors and Effectors in Dynamical Systems
Among the many approaches for reasoning about degrees of belief in the p...
research
∙
09/14/2018
On Plans With Loops and Noise
In an influential paper, Levesque proposed a formal specification for an...
research
∙
07/15/2018
Learning Probabilistic Logic Programs in Continuous Domains
The field of statistical relational learning aims at unifying logic and ...
research
∙
07/14/2018
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks
Probabilistic representations, such as Bayesian and Markov networks, are...
research
∙
01/25/2018
Probabilistic Planning by Probabilistic Programming
Automated planning is a major topic of research in artificial intelligen...
research
∙
09/21/2016
Semiring Programming: A Framework for Search, Inference and Learning
To solve hard problems, AI relies on a variety of disciplines such as lo...
research
∙
05/26/2016
The Symbolic Interior Point Method
A recent trend in probabilistic inference emphasizes the codification of...
research
∙
02/28/2014
Robot Location Estimation in the Situation Calculus
Location estimation is a fundamental sensing task in robotic application...
research
∙
09/26/2013
Reasoning about Probabilities in Dynamic Systems using Goal Regression
Reasoning about degrees of belief in uncertain dynamic worlds is fundame...
research
∙
09/10/2010