{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:36:51Z","timestamp":1763203011434,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031746260"},{"type":"electronic","value":"9783031746277"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-74627-7_35","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:00:27Z","timestamp":1735653627000},"page":"420-432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Using Part-Based Representations for\u00a0Explainable Deep Reinforcement Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8670-0248","authenticated-orcid":false,"given":"Manos","family":"Kirtas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Tsampazis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Loukia","family":"Avramelou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1177-9139","authenticated-orcid":false,"given":"Nikolaos","family":"Passalis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1288-3667","authenticated-orcid":false,"given":"Anastasios","family":"Tefas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"issue":"11","key":"35_CR1","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1177\/0278364913495721","volume":"32","author":"J Kober","year":"2013","unstructured":"Kober, J., Bagnell, J.A., Peters, J.: Reinforcement learning in robotics: a survey. Int. J. Rob. Res. 32(11), 1238\u20131274 (2013)","journal-title":"Int. J. Rob. Res."},{"issue":"6","key":"35_CR2","doi-asserted-by":"publisher","first-page":"4909","DOI":"10.1109\/TITS.2021.3054625","volume":"23","author":"BR Kiran","year":"2022","unstructured":"Kiran, B.R., et al.: Deep reinforcement learning for autonomous driving: a survey. IEEE Trans. Intell. Transp. Syst. 23(6), 4909\u20134926 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Hayes, B., Shah, J.A.: Improving robot controller transparency through autonomous policy explanation. In: Proceedings of the ACM\/IEEE International Conference on Human-Robot Interaction, HRI \u201917, New York, NY, USA, pp. 303\u2013312. Association for Computing Machinery (2017)","key":"35_CR3","DOI":"10.1145\/2909824.3020233"},{"doi-asserted-by":"crossref","unstructured":"Keane, M.T., Kenny, E.M.: How case-based reasoning explains neural networks: a theoretical analysis of XAI using post-hoc explanation-by-example from a survey of ANN-CBR twin-systems. In: Bach, K., Marling, C. (eds.) Case-Based Reasoning Research and Development, pp.\u00a0155\u2013171. Springer, Cham (2019)","key":"35_CR4","DOI":"10.1007\/978-3-030-29249-2_11"},{"issue":"7","key":"35_CR5","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)","journal-title":"PLoS ONE"},{"doi-asserted-by":"crossref","unstructured":"Slack, D., Hilgard, S., Jia, E., Singh, S., Lakkaraju, H.: Fooling LIME and SHAP: adversarial attacks on post hoc explanation methods. In: Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society, AIES \u201920, New York, NY, USA, pp.\u00a0180\u2013186. Association for Computing Machinery (2020)","key":"35_CR6","DOI":"10.1145\/3375627.3375830"},{"key":"35_CR7","first-page":"9623","volume":"36","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Booth, S., Ribeiro, M.T., Shah, J.: Do feature attribution methods correctly attribute features? Proc. AAAI Conf. Artif. Intell. 36, 9623\u20139633 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"doi-asserted-by":"crossref","unstructured":"Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., Zhong, C.: Interpretable machine learning: fundamental principles and 10 grand challenges. Stat. Surv. 16, 1\u201385 (2022)","key":"35_CR8","DOI":"10.1214\/21-SS133"},{"key":"35_CR9","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.neunet.2012.05.003","volume":"33","author":"A Lemme","year":"2012","unstructured":"Lemme, A., Reinhart, R.F., Steil, J.J.: Online learning and generalization of parts-based image representations by non-negative sparse autoencoders. Neural Netw. 33, 194\u2013203 (2012)","journal-title":"Neural Netw."},{"issue":"12","key":"35_CR10","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.1109\/TNNLS.2015.2479223","volume":"27","author":"E Hosseini-Asl","year":"2016","unstructured":"Hosseini-Asl, E., Zurada, J.M., Nasraoui, O.: Deep learning of part-based representation of data using sparse autoencoders with nonnegativity constraints. IEEE Trans. Neural Netw. Learn. Syst. 27(12), 2486\u20132498 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"35_CR11","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TNNLS.2014.2310059","volume":"26","author":"J Chorowski","year":"2015","unstructured":"Chorowski, J., Zurada, J.M.: Learning understandable neural networks with nonnegative weight constraints. IEEE Trans. Neural Netw. Learn. Syst. 26(1), 62\u201369 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"35_CR12","doi-asserted-by":"publisher","first-page":"3969","DOI":"10.1109\/TNNLS.2017.2747861","volume":"29","author":"BO Ayinde","year":"2018","unstructured":"Ayinde, B.O., Zurada, J.M.: Deep learning of constrained autoencoders for enhanced understanding of data. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 3969\u20133979 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"doi-asserted-by":"crossref","unstructured":"Ferro, M., Fernandes, B., Bastos-Filho, C.: Non-negative structured pyramidal neural network for pattern recognition. In: Proceedings of the International Joint Conference on Neural Networks, pp.\u00a01\u20137 (2018)","key":"35_CR13","DOI":"10.1109\/IJCNN.2018.8489216"},{"doi-asserted-by":"crossref","unstructured":"Ferro, M.S., Fernandes, B.J., Bastos-Filho, C.J.: Non-negative pyramidal neural network for parts-based learning. In: Proceedings of the International Joint Conference on Neural Networks, pp.\u00a01709\u20131716 (2017)","key":"35_CR14","DOI":"10.1109\/IJCNN.2017.7966057"},{"issue":"6755","key":"35_CR15","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788\u2013791 (1999)","journal-title":"Nature"},{"doi-asserted-by":"crossref","unstructured":"Tanaka, K.: Columns for complex visual object features in the inferotemporal cortex: clustering of cells with similar but slightly different stimulus selectivities. Cereb. Cortex 13, 90\u201399 (2003)","key":"35_CR16","DOI":"10.1093\/cercor\/13.1.90"},{"unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)","key":"35_CR17"},{"unstructured":"Schulman, J., Levine, S., Moritz, P., Jordan, M.I., Abbeel, P.: Trust region policy optimization. arXiv preprint arXiv:1502.05477 (2015)","key":"35_CR18"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (2015)","key":"35_CR19","DOI":"10.1109\/ICCV.2015.123"},{"unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the International Conference on Artificial Intelligence and Statistics, vol.\u00a09 Proceedings of Machine Learning Research, 13\u201315 May, Chia Laguna Resort, Sardinia, Italy, pp.\u00a0249\u2013256. PMLR (2010)","key":"35_CR20"},{"doi-asserted-by":"crossref","unstructured":"Kirtas, M., Tsampazis, K., Passalis, N., Tefas, A.: Deepbots: a webots-based deep reinforcement learning framework for robotics. In: Proceedings of the International Conference on Artificial Intelligence Applications and Innovations, pp.\u00a064\u201375 (2020)","key":"35_CR21","DOI":"10.1007\/978-3-030-49186-4_6"},{"doi-asserted-by":"crossref","unstructured":"Kirtas, M., Tsampazis, K., Tosidis, P., Passalis, N., Tefas, A.: Chapter 21 - deep learning for robotics examples using opendr. In: Iosifidis, A., Tefas, A. (eds.) Deep Learning for Robot Perception and Cognition, pp.\u00a0579\u2013596. Academic Press (2022)","key":"35_CR22","DOI":"10.1016\/B978-0-32-385787-1.00026-9"},{"doi-asserted-by":"crossref","unstructured":"Kirtas, M., Passalis, N., Mourgias-Alexandris, G., Dabos, G., Pleros, N., Tefas, A.: Robust architecture-agnostic and noise resilient training of photonic deep learning models. IEEE Trans. Emerg. Topics Comput. Intell. (2022)","key":"35_CR23","DOI":"10.1109\/TETCI.2022.3182765"},{"unstructured":"Mnih, V., et al.: Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)","key":"35_CR24"},{"doi-asserted-by":"crossref","unstructured":"Passalis, N., Tefas, A.: Learning deep representations with probabilistic knowledge transfer. In: Proceedings of the European Conference on Computer Vision, pp.\u00a0268\u2013284 (2018)","key":"35_CR25","DOI":"10.1007\/978-3-030-01252-6_17"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74627-7_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:15:45Z","timestamp":1735654545000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74627-7_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746260","9783031746277"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74627-7_35","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}