{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:49:03Z","timestamp":1760028543328,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031700736"},{"type":"electronic","value":"9783031700743"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-70074-3_8","type":"book-chapter","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T05:48:57Z","timestamp":1727156937000},"page":"137-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Model-Agnostic Policy Explanations: Biased Sampling for\u00a0Surrogate Models"],"prefix":"10.1007","author":[{"given":"Bryan","family":"Lavender","sequence":"first","affiliation":[]},{"given":"Sandip","family":"Sen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,25]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fus. 58, 82\u2013115 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253519308103","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Barto, A.G., Sutton, R.S., Anderson, C.W.: Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. SMC-13(5), 834\u2013846 (1983). https:\/\/doi.org\/10.1109\/TSMC.1983.6313077","DOI":"10.1109\/TSMC.1983.6313077"},{"issue":"23","key":"8_CR3","doi-asserted-by":"publisher","first-page":"16893","DOI":"10.1007\/s00521-023-08423-1","volume":"35","author":"R Dazeley","year":"2023","unstructured":"Dazeley, R., Vamplew, P., Cruz, F.: Explainable reinforcement learning for broad-XAI: a conceptual framework and survey. Neural Comput. Appl. 35(23), 16893\u201316916 (2023)","journal-title":"Neural Comput. Appl."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Greenwell, B.M., Boehmke, B.C., McCarthy, A.J.: A simple and effective model-based variable importance measure. arXiv preprint arXiv:1805.04755 (2018)","DOI":"10.32614\/CRAN.package.vip"},{"key":"8_CR5","unstructured":"Greydanus, S., Koul, A., Dodge, J., Fern, A.: Visualizing and understanding Atari agents (2018)"},{"issue":"7","key":"8_CR6","doi-asserted-by":"publisher","first-page":"3762","DOI":"10.3390\/s23073762","volume":"23","author":"D Han","year":"2023","unstructured":"Han, D., Mulyana, B., Stankovic, V., Cheng, S.: A survey on deep reinforcement learning algorithms for robotic manipulation. Sensors 23(7), 3762 (2023)","journal-title":"Sensors"},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Iyer, R., Li, Y., Li, H., Lewis, M., Sundar, R., Sycara, K.: Transparency and explanation in deep reinforcement learning neural networks (2018)","DOI":"10.1145\/3278721.3278776"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Joo, H.T., Kim, K.J.: Visualization of deep reinforcement learning using grad-cam: how ai plays atari games? In: 2019 IEEE conference on games (CoG). pp.\u00a01\u20132. IEEE (2019)","DOI":"10.1109\/CIG.2019.8847950"},{"key":"8_CR9","doi-asserted-by":"publisher","unstructured":"Li, J., Rao, R., Shi, J.: Learning to trade with deep actor critic methods. In: 2018 11th International Symposium on Computational Intelligence and Design (ISCID), vol.\u00a002, pp. 66\u201371 (2018). https:\/\/doi.org\/10.1109\/ISCID.2018.10116","DOI":"10.1109\/ISCID.2018.10116"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Lyu, D., Yang, F., Liu, B., Gustafson, S.: SDRL: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 2970\u20132977 (2019)","DOI":"10.1609\/aaai.v33i01.33012970"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019). https:\/\/doi.org\/10.1016\/j.artint.2018.07.007, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0004370218305988","DOI":"10.1016\/j.artint.2018.07.007"},{"key":"8_CR12","unstructured":"Mnih, V., et all.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928\u20131937. PMLR (2016)"},{"key":"8_CR13","unstructured":"Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)"},{"key":"8_CR14","unstructured":"Molnar, C.: Interpretable Machine Learning, 2 edn. (2022). https:\/\/christophm.github.io\/interpretable-ml-book"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Ranjbar, N., Safabakhsh, R.: Using decision tree as local interpretable model in autoencoder-based lime. In: 2022 27th International Computer Conference, Computer Society of Iran (CSICC), pp.\u00a01\u20137. IEEE (2022)","DOI":"10.1109\/CSICC55295.2022.9780503"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Sieusahai, A., Guzdial, M.: Explaining deep reinforcement learning agents in the Atari domain through a surrogate model. In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 17(1), 82\u201390 (Oct 2021). https:\/\/doi.org\/10.1609\/aiide.v17i1.18894, https:\/\/ojs.aaai.org\/index.php\/AIIDE\/article\/view\/18894","DOI":"10.1609\/aiide.v17i1.18894"},{"key":"8_CR19","unstructured":"Sundararajan, M., Najmi, A.: The many Shapley values for model explanation. In: International Conference on Machine Learning, pp. 9269\u20139278. PMLR (2020)"},{"key":"8_CR20","unstructured":"Sutton, R.S.: Generalization in reinforcement learning: successful examples using sparse coarse coding. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol.\u00a08. MIT Press (1995)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. Ser. B (Methodol.) 58(1), 267\u2013288 (1996). http:\/\/www.jstor.org\/stable\/2346178","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"8_CR22","unstructured":"Vilone, G., Longo, L.: Explainable artificial intelligence: a systematic review (2020)"},{"key":"8_CR23","doi-asserted-by":"publisher","unstructured":"Wells, L., Bednarz, T.: Explainable AI and reinforcement learning-a systematic review of current approaches and trends. Front. Artif. Intell. 4 (2021). https:\/\/doi.org\/10.3389\/frai.2021.550030, https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2021.550030","DOI":"10.3389\/frai.2021.550030"},{"issue":"7","key":"8_CR24","doi-asserted-by":"publisher","first-page":"3680","DOI":"10.1109\/TNNLS.2021.3116063","volume":"34","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Liao, S., Liu, X., Li, Z., Lu, R.: Deep reinforcement learning on autonomous driving policy with auxiliary critic network. IEEE Trans. Neural Netw. Learn. Syst. 34(7), 3680\u20133690 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."}],"container-title":["Lecture Notes in Computer Science","Explainable and Transparent AI and Multi-Agent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70074-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T05:50:23Z","timestamp":1727157023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70074-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031700736","9783031700743"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70074-3_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"25 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EXTRAAMAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"extraamas2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/extraamas.ehealth.hevs.ch\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}