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Explainable Artificial Intelligence (XAI), i.e., the development of more transparent and interpretable AI models, has gained increased traction over the last few years.
In: 5th Australian joint conference on artificial intelligence. vol. 92, pp. 343–348. World Scientific (1992)
Quinlan, J.R., et al.: Learning with continuous classes · 1992
Earlier work this paper cites.
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey (1996)
1996
Earlier work this paper cites.
In: ICML. vol. 97, pp. 152–160 (1997)
Kimura, H., Miyazaki, K., Kobayashi, S.: Reinforcement learning in pomdps with function approximation · 1997
Earlier work this paper cites.
Artificial Intelligence 101(1-2), 99–134 (1998)
Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains · 1998
Earlier work this paper cites.
In: Aaai/iaai. pp. 769–774 (1998)
Uther, W.T., Veloso, M.M.: Tree based discretization for continuous state space reinforcement learning · 1998
Earlier work this paper cites.
In: Proceedings of the 2000 ACM conference on Computer supported cooperative work - CSCW ’00. ACM Press (2000)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations · 2000
Earlier work this paper cites.
Software available at http://torcs. sourceforge. net 4(6), 2 (2000)
Wymann, B., Espié, E., Guionneau, C., Dimitrakakis, C., Coulom, R., Sumner, A.: Torcs, the open racing car simulator · 2000
Earlier work this paper cites.
In: Proceedings of the 3rd International Conference on Development and Learning. pp. 112–19 (2004)
Barto, A.G., Singh, S., Chentanez, N.: Intrinsically motivated learning of hierarchical collections of skills · 2004
Earlier work this paper cites.
The British Journal for the Philosophy of Science 56(4), 889–911 (2005)
Halpern, J.Y.: Causes and explanations: A structural-model approach. part II: Explanations · 2005
Earlier work this paper cites.
In: Proceedings of the 13th international conference on Intelligent user interfaces - IUI ’08. ACM Press (2008)
Glass, A., McGuinness, D.L., Wolverton, M.: Toward establishing trust in adaptive agents · 2008
Earlier work this paper cites.
IET Intelligent Transport Systems 4(2), 128 (2010)
Arel, I., Liu, C., Urbanik, T., Kohls, A.: Reinforcement learning-based multi-agent system for network traffic signal control · 2010
Earlier work this paper cites.
Data Mining and Knowledge Discovery 23(1), 128–168 (2010)
Ikonomovska, E., Gama, J., Džeroski, S.: Learning model trees from evolving data streams · 2010
Earlier work this paper cites.
WIREs Data Mining and Knowledge Discovery 1(1), 14–23 (2011)
Loh, W.Y.: Classification and regression trees · 2011
Earlier work this paper cites.
Decision Support Systems 51(4), 782–793 (2011)
Martens, D., Vanthienen, J., Verbeke, W., Baesens, B.: Performance of classification models from a user perspective · 2011
Earlier work this paper cites.
The International Journal of Robotics Research 32(11), 1238–1274 (2013)
Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: A survey · 2013
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks (2013)
2013
Earlier work this paper cites.
ACM SIGKDD Explorations Newsletter 15(1), 1–10 (2014)
Freitas, A.A.: Comprehensible classification models · 2014
Earlier work this paper cites.
In: Proceedings of the 23rd international conference on World wide web - WWW ’14. ACM Press (2014)
Nguyen, T.T., Hui, P.M., Harper, F.M., Terveen, L., Konstan, J.A.: Exploring the filter bubble · 2014
Earlier work this paper cites.
In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images · 2015
Earlier work this paper cites.
Rusu, A.A., Colmenarejo, S.G., Gulcehre, C., Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., Hadsell, R.: Policy distillation (2015)
2015
Earlier work this paper cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: Openai gym (2016)
2016
Earlier work this paper cites.
OJ L 119, 1–88 (2016)
European Commission, Parliament: Regulation (EU) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) · 2016
Earlier work this paper cites.
In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29. pp. 2280–2288. Curran Associates, Inc. (2016), http://papers.nips.cc/paper/6300-examples-are-not-enough-learn-to-criticize-criticism-for-interpretability.pdf
Kim, B., Khanna, R., Koyejo, O.O.: Examples are not enough, learn to criticize! criticism for interpretability · 2016
Cited alongside, same era.
Lipton, Z.C.: The mythos of model interpretability (2016)
2016
Cited alongside, same era.
In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16. ACM Press (2016)
Ribeiro, M.T., Singh, S., Guestrin, C.: ”why should i trust you?” · 2016
Cited alongside, same era.
Zahavy, T., Zrihem, N.B., Mannor, S.: Graying the black box: Understanding dqns (2016)
2016
Cited alongside, same era.
In: Proceedings of the 34th International Conference on Machine Learning - Volume 70. p. 166–175. ICML’17, JMLR.org (2017)
Communications of the ACM 61(10), 36–43 (2018)
Lipton, Z.C.: The mythos of model interpretability · 2018
Later among the works it cites.
Digital Signal Processing 73, 1–15 (2018)
Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks · 2018
Later among the works it cites.
PMLR 80:5045-5054 (2018)
Verma, A., Murali, V., Singh, R., Kohli, P., Chaudhuri, S.: Programmatically interpretable reinforcement learning · 2018
Later among the works it cites.
IJCAI-18 Workshop on Explainable AI (XAI). Vol. 37. 2018 (2018)
van der Waa, J., van Diggelen, J., van den Bosch, K., Neerincx, M.: Contrastive explanations for reinforcement learning in terms of expected consequences · 2018
Later among the works it cites.
In: Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence. pp. 1–6 (2019)
Coppens, Y., Efthymiadis, K., Lenaerts, T., Nowé, A., Miller, T., Weber, R., Magazzeni, D.: Distilling deep reinforcement learning policies in soft decision trees · 2019
Later among the works it cites.
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Andreas, J., Klein, D., Levine, S.: Modular multitask reinforcement learning with policy sketches · 2017
Cited alongside, same era.
In: 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE (2017)
Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., Srivastava, M., Preece, A., Julier, S., Rao, R.M., Kelley, T.D., Braines, D., Sensoy, M., Willis, C.J., Gurram, P.: Interpretability of deep learning models: A survey of results · 2017
Cited alongside, same era.
Doran, D., Schulz, S., Besold, T.R.: What does explainable ai really mean? a new conceptualization of perspectives (2017)
2017
Cited alongside, same era.
Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning (2017)
2017
Cited alongside, same era.
In: Proceedings of the 5th International Conference on Human Agent Interaction - HAI ’17. ACM Press (2017)
Fukuchi, Y., Osawa, M., Yamakawa, H., Imai, M.: Autonomous self-explanation of behavior for interactive reinforcement learning agents · 2017
Cited alongside, same era.
AI Magazine 38(3), 50–57 (2017)
Goodman, B., Flaxman, S.: European union regulations on algorithmic decision-making and a “right to explanation” · 2017
Cited alongside, same era.
In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction - HRI ’17. ACM Press (2017)
Hayes, B., Shah, J.A.: Improving robot controller transparency through autonomous policy explanation · 2017
Cited alongside, same era.
Engineering Applications of Artificial Intelligence 65, 87–98 (2017), https://doi.org/10.1016/j.engappai.2017.07.005
Hein, D., Hentschel, A., Runkler, T., Udluft, S.: Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies · 2017
Cited alongside, same era.
Du, M., Liu, N., Hu, X.: Techniques for interpretable machine learning · 2019
Later among the works it cites.
In: The Ninth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies (ENERGY 2019). vol. 9, pp. 24–32 (2019)
Fischer, L., Memmen, J.M., Veith, E.M., Tröschel, M.: Adversarial resilience learning—towards systemic vulnerability analysis for large and complex systems · 2019
Later among the works it cites.
ACM Computing Surveys 51(6), 1–37 (2019)
Israelsen, B.W., Ahmed, N.R.: “dave…i can assure you …that it’s going to be all right …” a definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships · 2019
Later among the works it cites.
In: Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence. pp. 47–53 (2019)
Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., Doshi-Velez, F.: Explainable reinforcement learning via reward decomposition · 2019
Later among the works it cites.
Lee, J.H.: Complementary reinforcement learning towards explainable agents (2019)
2019
Later among the works it cites.
In: Machine Learning and Knowledge Discovery in Databases, pp. 414–429. Springer International Publishing (2019)
Liu, G., Schulte, O., Zhu, W., Li, Q.: Toward interpretable deep reinforcement learning with linear model u-trees · 2019
Later among the works it cites.
Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens (2019)
2019
Later among the works it cites.
Artificial Intelligence 267, 1–38 (2019)
Miller, T.: Explanation in artificial intelligence: Insights from the social sciences · 2019
Later among the works it cites.
Nature Machine Intelligence 1(5), 206–215 (2019)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead · 2019
Later among the works it cites.
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al.: Mastering ATARI, go, chess and shogi by planning with a learned model (2019)
2019
Later among the works it cites.
Sequeira, P., Gervasio, M.: Interestingness elements for explainable reinforcement learning: Understanding agents’ capabilities and limitations (2019)
2019
Later among the works it cites.
In: 2019 Systems and Information Engineering Design Symposium (SIEDS). IEEE (2019)
Tomzcak, K., Pelter, A., Gutierrez, C., Stretch, T., Hilf, D., Donadio, B., Tenhundfeld, N.L., de Visser, E.J., Tossell, C.C.: Let tesla park your tesla: Driver trust in a semi-automated car · 2019
Later among the works it cites.
In: Proceedings of the 2019 International Conference on Artificial Intelligence, Robotics and Control. ACM (2019)
Veith, E., Fischer, L., Tröschel, M., Nieße, A.: Analyzing cyber-physical systems from the perspective of artificial intelligence · 2019
Later among the works it cites.
Littman, M., Kaelbling, L.: Background on pomdps (1999), https://cs.brown.edu/research/ai/pomdp/tutorial/pomdp-background.html , [Retrieved: 2020-04-15]
2020
Closest in time.
Molar, C.: Interpretable machine learning (2018), https://christophm.github.io/interpretable-ml-book/ , [Retrieved: 2020-03-31]
2020
Closest in time.
The European Commission (2018a), https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe , article; accessed 27.03.2020
The European Commission: Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions · 2020
Closest in time.
The European Commission (2018b), https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe , article; accessed 27.04.2020
The European Commission: Independent High-Level Expert Group on Artificial Intelligence set up by the European Commission · 2020
Closest in time.