b'Tim Miller'

research

∙ 08/25/2023

Diverse, Top-k, and Top-Quality Planning Over Simulators

Diverse, top-k, and top-quality planning are concerned with the generati...

0 Lyndon Benke, et al. ∙

research

∙ 05/31/2023

Human-centric Literature on Trust for SfTI Veracity Spearhead

This article summarizes the literature on trust of digital technologies ...

0 Kelly Blincoe, et al. ∙

research

∙ 03/20/2023

Deceptive Reinforcement Learning in Model-Free Domains

This paper investigates deceptive reinforcement learning for privacy pre...

0 Alan Lewis, et al. ∙

research

∙ 03/10/2023

Explaining Model Confidence Using Counterfactuals

Displaying confidence scores in human-AI interaction has been shown to h...

0 Thao Le, et al. ∙

research

∙ 03/09/2023

Explainable Goal Recognition: A Framework Based on Weight of Evidence

We introduce and evaluate an eXplainable Goal Recognition (XGR) model th...

0 Abeer Alshehri, et al. ∙

research

∙ 02/24/2023

Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support

In this paper, we argue for a paradigm shift from the current model of e...

0 Tim Miller, et al. ∙

research

∙ 12/08/2022

Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs

Although many machine learning methods, especially from the field of dee...

0 Vilde B. Gjærum, et al. ∙

research

∙ 11/19/2022

Concept-based Explanations using Non-negative Concept Activation Vectors and Decision Tree for CNN Models

This paper evaluates whether training a decision tree based on concepts ...

0 Gayda Mutahar, et al. ∙

research

∙ 08/31/2022

Are we measuring trust correctly in explainability, interpretability, and transparency research?

This paper presents an argument for why we are not measuring trust suffi...

0 Tim Miller, et al. ∙

research

∙ 06/06/2022

Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence

In this paper, we show that counterfactual explanations of confidence sc...

0 Thao Le, et al. ∙

research

∙ 10/06/2021

Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief

Many AI applications involve the interaction of multiple autonomous agen...

10 Christian Muise, et al. ∙

research

∙ 09/07/2021

Modelling Strategic Deceptive Planning in Adversarial Multi-Agent Systems

Deception is virtually ubiquitous in warfare, and should be a central co...

0 Lyndon Benke, et al. ∙

research

∙ 04/29/2021

Collaborative Human-Agent Planning for Resilience

Intelligent agents powered by AI planning assist people in complex scena...

0 Ronal Singh, et al. ∙

research

∙ 04/15/2021

LEx: A Framework for Operationalising Layers of Machine Learning Explanations

Several social factors impact how people respond to AI explanations used...

0 Ronal Singh, et al. ∙

research

∙ 02/23/2021

Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions

As the use of algorithmic systems in high-stakes decision-making increas...

0 Henrietta Lyons, et al. ∙

research

∙ 02/22/2021

Fair and Responsible AI: A Focus on the Ability to Contest

As the use of artificial intelligence (AI) in high-stakes decision-makin...

0 Henrietta Lyons, et al. ∙

research

∙ 02/08/2021

Designing for Contestation: Insights from Administrative Law

A paper presented at the Workshop on Contestability in Algorithmic Syste...

0 Henrietta Lyons, et al. ∙

research

∙ 02/05/2021

Deceptive Reinforcement Learning for Privacy-Preserving Planning

In this paper, we study the problem of deceptive reinforcement learning ...

0 Zhengshang Liu, et al. ∙

research

∙ 02/03/2021

Directive Explanations for Actionable Explainability in Machine Learning Applications

This paper investigates the prospects of using directive explanations to...

0 Ronal Singh, et al. ∙

research

∙ 11/15/2020

Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies

This article philosophically analyzes online exam supervision technologi...

0 Simon Coghlan, et al. ∙

research

∙ 10/15/2020

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Trust is a central component of the interaction between people and AI, i...

0 Alon Jacovi, et al. ∙

research

∙ 06/27/2020

Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors

Convolutional neural network (CNN) models for computer vision are powerf...

14 Ruihan Zhang, et al. ∙

research

∙ 02/20/2020

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos

Large scale contextual representation models, such as BERT, have signifi...

0 Dianbo Liu, et al. ∙

research

∙ 01/28/2020

Distal Explanations for Explainable Reinforcement Learning Agents

Causal explanations present an intuitive way to understand the course of...

14 Prashan Madumal, et al. ∙

research

∙ 10/07/2019

Improving Performance of Multiagent Cooperation Using Epistemic Planning

In most multiagent applications, communication is essential among agents...

0 Abeer Alshehri, et al. ∙

research

∙ 07/29/2019

Let's Make It Personal, A Challenge in Personalizing Medical Inter-Human Communication

Current AI approaches have frequently been used to help personalize many...

0 Mor Vered, et al. ∙

research

∙ 05/27/2019

Explainable Reinforcement Learning Through a Causal Lens

Prevalent theories in cognitive science propose that humans understand a...

0 Prashan Madumal, et al. ∙

research

∙ 03/28/2019

What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS

Epistemic planning --- planning with knowledge and belief --- is essenti...

0 Guang Hu, et al. ∙

research

∙ 03/05/2019

A Grounded Interaction Protocol for Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) systems need to include an exp...

0 Prashan Madumal, et al. ∙

research

∙ 11/07/2018

Contrastive Explanation: A Structural-Model Approach

The topic of causal explanation in artificial intelligence has gathered ...

0 Tim Miller, et al. ∙

research

∙ 10/30/2018

Emotionalism within People-Oriented Software Design

In designing most software applications, much effort is placed upon the ...

0 Mohammadhossein Sherkat, et al. ∙

research

∙ 06/21/2018

Towards a Grounded Dialog Model for Explainable Artificial Intelligence

To generate trust with their users, Explainable Artificial Intelligence ...

0 Prashan Madumal, et al. ∙

research

∙ 12/02/2017

Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences

In his seminal book `The Inmates are Running the Asylum: Why High-Tech P...

0 Tim Miller, et al. ∙

research

∙ 06/22/2017

Explanation in Artificial Intelligence: Insights from the Social Sciences

There has been a recent resurgence in the area of explainable artificial...

0 Tim Miller, et al. ∙

research

∙ 02/21/2016

Social planning for social HRI

Making a computational agent 'social' has implications for how it percei...

0 Liz Sonenberg, et al. ∙

Tim Miller

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