Fetching the paper…
Reading the bibliography…
Artificial intelligence (AI) has been embedded into many aspects of people's daily lives and it has become normal for people to have AI make decisions for them.
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1911
Earlier work this paper cites.
T. Zahavy, N. Ben-Zrihem, and S. Mannor, “Graying the black box: Understanding DQNs,” in Proceedings of The 33rd International Conference on Machine Learning . PMLR, Jun. 2016, pp. 1899–1908, iSSN: 1938-7228. [Online]. Available: https://proceedings.mlr.press/v48/zahavy16.html
1938
Earlier work this paper cites.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , ser. Adaptive Computation and Machine Learning series, F. Bach, Ed. Cambridge, MA, USA: A Bradford Book, Feb. 1998
1998
Earlier work this paper cites.
R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” vol. 12, 1999
1999
Earlier work this paper cites.
2001
Earlier work this paper cites.
S. Džeroski, L. De Raedt, and K. Driessens, “Relational Reinforcement Learning,” Machine Learning , vol. 43, no. 1, pp. 7–52, Apr. 2001. [Online]. Available: https://doi.org/10.1023/A:1007694015589
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
2004
Earlier work this paper cites.
J. Y. Halpern and J. Pearl, “Causes and Explanations: A Structural-Model Approach. Part II: Explanations,” The British Journal for the Philosophy of Science , vol. 56, no. 4, pp. 889–911, 2005, publisher: [Oxford University Press, The British Society for the Philosophy of Science]. [Online]. Available: https://www.jstor.org/stable/3541871
2005
Earlier work this paper cites.
S. Sloman, Causal Models: How People Think about the World and Its Alternatives . New York: Oxford University Press, 2005. [Online]. Available: https://oxford.universitypressscholarship.com/10.1093/acprof:oso/9780195183115.001.0001/acprof-9780195183115
2005
Earlier work this paper cites.
M. V. Otterlo, “A survey of reinforcement learning in relational domains,” undefined , 2005. [Online]. Available: https://www.semanticscholar.org/paper/A-survey-of-reinforcement-learning-in-relational-Otterlo/1b4471121868c9774aba82d81cb3af75963aeedd
2005
Earlier work this paper cites.
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. A. Freitas, “Comprehensible classification models: a position paper,” ACM SIGKDD Explorations Newsletter , vol. 15, no. 1, pp. 1–10, Mar. 2014. [Online]. Available: https://doi.org/10.1145/2594473.2594475
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
B. Goodman and S. Flaxman, “European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation”,” AI Magazine , vol. 38, no. 3, pp. 50–57, Oct. 2017, number: 3. [Online]. Available: https://ojs.aaai.org/index.php/aimagazine/article/view/2741
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Hein, A. Hentschel, T. Runkler, and S. Udluft, “Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies,” Engineering Applications of Artificial Intelligence , vol. 65, no. C, pp. 87–98, Oct. 2017. [Online]. Available: https://doi.org/10.1016/j.engappai.2017.07.005
2017
Cited alongside, same era.
B. Hayes and J. A. Shah, “Improving Robot Controller Transparency Through Autonomous Policy Explanation,” in 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI , Mar. 2017, pp. 303–312, iSSN: 2167-2148
2017
Cited alongside, same era.
F. K. Došilović, M. Brčić, and N. Hlupić, “Explainable artificial intelligence: A survey,” in 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) , May 2018, pp. 0210–0215
2018
Cited alongside, same era.
A. Adadi and M. Berrada, “Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI),” IEEE Access , vol. 6, pp. 52 138–52 160, 2018, conference Name: IEEE Access
T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady, “explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning,” IEEE Transactions on Visualization and Computer Graphics , vol. 26, no. 1, pp. 1064–1074, Jan. 2020, conference Name: IEEE Transactions on Visualization and Computer Graphics
2020
Later among the works it cites.
C. Molnar, Interpretable Machine Learning . Lulu.com, 2020, google-Books-ID: jBm3DwAAQBAJ
2020
Later among the works it cites.
J. Huang, P. P. Angelov, and C. Yin, “Interpretable policies for reinforcement learning by empirical fuzzy sets,” Engineering Applications of Artificial Intelligence , vol. 91, p. 103559, May 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S095219762030049X
2020
Later among the works it cites.
P. Madumal, T. Miller, L. Sonenberg, and F. Vetere, “Explainable Reinforcement Learning Through a Causal Lens,” AAAI , 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
M. Brčić and D. Mlinarić, “Tracking Predictive Gantt Chart for Proactive Rescheduling in Stochastic Resource Constrained Project Scheduling,” Journal of Information and Organizational Sciences , vol. 42, no. 2, Dec. 2018, number: 2. [Online]. Available: https://jios.foi.hr/index.php/jios/article/view/1148
2018
Cited alongside, same era.
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, “A Survey of Methods for Explaining Black Box Models,” ACM Computing Surveys , vol. 51, no. 5, pp. 93:1–93:42, Aug. 2018. [Online]. Available: https://doi.org/10.1145/3236009
2018
Cited alongside, same era.
G. Montavon, W. Samek, and K.-R. Müller, “Methods for interpreting and understanding deep neural networks,” Digital Signal Processing , vol. 73, pp. 1–15, Feb. 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1051200417302385
2018
Cited alongside, same era.
X. Wang, Y. Chen, J. Yang, L. Wu, Z. Wu, and X. Xie, “A Reinforcement Learning Framework for Explainable Recommendation,” in 2018 IEEE International Conference on Data Mining (ICDM) , Nov. 2018, pp. 587–596, iSSN: 2374-8486
2018
Cited alongside, same era.
S. Greydanus, A. Koul, J. Dodge, and A. Fern, “Visualizing and Understanding Atari Agents,” in Proceedings of the 35th International Conference on Machine Learning . PMLR, Jul. 2018, pp. 1792–1801, iSSN: 2640-3498. [Online]. Available: https://proceedings.mlr.press/v80/greydanus18a.html
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Brčić, M. Katić, and N. Hlupić, “Planning horizons based proactive rescheduling for stochastic resource-constrained project scheduling problems,” European Journal of Operational Research , vol. 273, no. 1, pp. 58–66, Feb. 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0377221718306593
2019
Cited alongside, same era.
2020
Later among the works it cites.
N. Pawlowski, D. C. d. Castro, and B. Glocker, “Deep Structural Causal Models for Tractable Counterfactual Inference,” NeurIPS , 2020
2020
Later among the works it cites.
K. Sokol and P. Flach, “Explainability fact sheets: a framework for systematic assessment of explainable approaches,” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , ser. FAT* ’20. New York, NY, USA: Association for Computing Machinery, Jan. 2020, pp. 56–67. [Online]. Available: https://doi.org/10.1145/3351095.3372870
2020
Later among the works it cites.
E. Tjoa and C. Guan, “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 11, pp. 4793–4813, Nov. 2021, conference Name: IEEE Transactions on Neural Networks and Learning Systems
2021
Later among the works it cites.
“Gartner Identifies Three Areas Where Legal & Compliance Leaders Should Focus Their Technology Investments.” [Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2021-09-23-gartner-identifies-three-areas-where-legal-and-compliance-leaders-should-focus-their-technology-investments
2021
Later among the works it cites.
“Gartner Identifies Four Trends Driving Near-Term Artificial Intelligence Innovation.” [Online]. Available: https://www.gartner.com/en/newsroom/press-releases/2021-09-07-gartner-identifies-four-trends-driving-near-term-artificial-intelligence-innovation
2021
Later among the works it cites.
“Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL LAYING DOWN HARMONISED RULES ON ARTIFICIAL INTELLIGENCE (ARTIFICIAL INTELLIGENCE ACT) AND AMENDING CERTAIN UNION LEGISLATIVE ACTS,” 2021. [Online]. Available: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206
2021
Later among the works it cites.
N. Burkart and M. F. Huber, “A Survey on the Explainability of Supervised Machine Learning,” Journal of Artificial Intelligence Research , vol. 70, pp. 245–317, Jan. 2021. [Online]. Available: https://jair.org/index.php/jair/article/view/12228
2021
Later among the works it cites.
R. Guidotti, A. Monreale, D. Pedreschi, and F. Giannotti, “Principles of Explainable Artificial Intelligence,” in Explainable AI Within the Digital Transformation and Cyber Physical Systems: XAI Methods and Applications , M. Sayed-Mouchaweh, Ed. Cham: Springer International Publishing, 2021, pp. 9–31. [Online]. Available: https://doi.org/10.1007/978-3-030-76409-8_2
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Heuillet, F. Couthouis, and N. Díaz-Rodríguez, “Explainability in deep reinforcement learning,” Knowledge-Based Systems , vol. 214, p. 106685, Feb. 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950705120308145
2021
Later among the works it cites.
W. Guo, X. Wu, U. Khan, and X. Xing, “EDGE: Explaining deep reinforcement learning policies,” in Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., 2021. [Online]. Available: https://openreview.net/forum?id=Wp3we5kv6P
2021
Later among the works it cites.
“Explainable AI,” Mar. 2021. [Online]. Available: https://www.ibm.com/watson/explainable-ai
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.