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Local and Global Explanations of Agent Behavior: Integrating Strategy
  Summaries with Saliency Maps

Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

18 May 2020
Tobias Huber
Katharina Weitz
Elisabeth André
Ofra Amir
    FAtt
ArXivPDFHTML

Papers citing "Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps"

11 / 11 papers shown
Title
Behaviour Discovery and Attribution for Explainable Reinforcement Learning
Rishav Rishav
Somjit Nath
Vincent Michalski
Samira Ebrahimi Kahou
FAtt
OffRL
68
0
0
19 Mar 2025
A Tale of Two Imperatives: Privacy and Explainability
A Tale of Two Imperatives: Privacy and Explainability
Supriya Manna
Niladri Sett
91
0
0
30 Dec 2024
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
Melkamu Mersha
Khang Lam
Joseph Wood
Ali AlShami
Jugal Kalita
XAI
AI4TS
67
28
0
30 Aug 2024
ADESSE: Advice Explanations in Complex Repeated Decision-Making
  Environments
ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments
Sören Schleibaum
Lu Feng
Sarit Kraus
Jörg P. Müller
38
2
0
31 May 2024
RACCER: Towards Reachable and Certain Counterfactual Explanations for
  Reinforcement Learning
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning
Jasmina Gajcin
Ivana Dusparic
CML
21
3
0
08 Mar 2023
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
Yotam Amitai
Guy Avni
Ofra Amir
35
3
0
24 Jan 2023
Explainable Deep Reinforcement Learning: State of the Art and Challenges
Explainable Deep Reinforcement Learning: State of the Art and Challenges
G. Vouros
XAI
48
76
0
24 Jan 2023
Interpretable ML for Imbalanced Data
Interpretable ML for Imbalanced Data
Damien Dablain
C. Bellinger
Bartosz Krawczyk
D. Aha
Nitesh V. Chawla
22
1
0
15 Dec 2022
Reinforcement Learning in Practice: Opportunities and Challenges
Reinforcement Learning in Practice: Opportunities and Challenges
Yuxi Li
OffRL
34
9
0
23 Feb 2022
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,235
0
24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
230
3,681
0
28 Feb 2017
1