Baader F, Nutt W (2003) Basic description logics. In: Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (eds) The description logic handbook. Cambridge University Press, Cambridge, pp 43–95
Google Scholar
Biundo S, Wendemuth A (2016) Companion-technology for cognitive technical systems. Künstliche Intell 30(1):71–75
Article
Google Scholar
Bjork EL, Bjork RA, Anderson MC (1998) Varieties of goal-directed forgetting. In: Golding JM, MacLeod CM (eds) Intentional forgetting: interdisciplinary approaches, vol 103. Lawrence Erlbaum, Mahwah
Google Scholar
Clancey WJ (1983) The epistemology of a rule-based expert system—a framework for explanation. Artif Intell 20(3):215–251
Article
Google Scholar
Cropper A, Muggleton SH. Metagol system. https://github.com/metagol/metagol
De Raedt L (2008) Logical and relational learning. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68856-3
Book
MATH
Google Scholar
Eppler MJ, Mengis J (2004) The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society 20(5):325–344. https://doi.org/10.1080/01972240490507974
Article
Google Scholar
Fails JA, Olsen DR Jr (2003) Interactive machine learning. In: Proceedings of the 8th international conference on Intelligent User Interfaces. ACM, New York, pp 39–45
Google Scholar
Forbus KD, Hinrichs TR (2006) Companion cognitive systems—a step toward human-level AI. AI Mag 27(2):83–95
Google Scholar
Fürnkranz J, Kliegr T, Paulheim H (2018) On cognitive preferences and the interpretability of rule-based models. arXiv:1803.01316[cs.LG] (Preprint)
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27. Curran Associates, Inc., pp 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
Gulwani S, Hernandez-Orallo J, Kitzelmann E, Muggleton SH, Schmid U, Zorn B (2015) Inductive programming meets the real world. Commun ACM 58(11):90–99
Article
Google Scholar
Hengstler M, Enkel E, Duelli S (2016) Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices. Technol Forecast Soc Change 105:105–120
Article
Google Scholar
Hilbert M, López P (2011) The world’s technological capacity to store, communicate, and compute information. Science 332(6025):60–65
Article
Google Scholar
Huth EJ (1989) The information explosion. Bull N Y Acad Med 65(6):647–672
Google Scholar
Jameson A, Schäfer R, Weis T, Berthold A, Weyrath T (1999) Making systems sensitive to the user’s changing resource limitations. Knowl Based Syst 12(8):413–425
Article
Google Scholar
Kruschke JK (2008) Models of categorization. In: Sun R (ed) The Cambridge handbook of computational psychology. Cambridge University Press, Cambridge, pp 267–301
Chapter
Google Scholar
Lakkaraju H, Bach SH, Leskovec J (2016) Interpretable decision sets: a joint framework for description and prediction. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 1675–1684
Chapter
Google Scholar
Lombrozo T (2016) Explanatory preferences shape learning and inference. Trends Cogn Sci 20(10):748–759
Article
Google Scholar
Lombrozo T, Vasilyeva N (2017) Causal explanation. In: Waldmann M (ed) Oxford handbook of causal reasoning. Oxford University Press, Oxford, pp 415–432
Google Scholar
Loza Mencía E, Fürnkranz J (2018) Interpretable machine learning. In: ECDA (ed) Book of abstracts, 5th European conference on data analysis, pp 56–60. http://groups.uni-paderborn.de/eim-i-fg-huellermeier/ecda2018/downloads/ECDA2018-BoA.pdf
Marcus G (2018) Deep learning: a critical appraisal. arXiv:1801.00631v1 [cs.AI] (Preprint)
Markman AB, Gentner D (1996) Commonalities and differences in similarity comparisons. Mem Cogn 24(2):235–249
Article
Google Scholar
Michie D (1988) Machine learning in the next five years. In: Proceedings of the third European working session on learning. Pitman, New York, pp 107–122
Google Scholar
Muggleton S (1995) Inverse entailment and Progol. New Gener Comput 13(3–4):245–286
Article
Google Scholar
Muggleton S, De Raedt L (1994) Inductive logic programming: theory and methods. J Logic Programm 19–20:629–679
MathSciNet
Article
MATH
Google Scholar
Muggleton SH, Lin D, Tamaddoni-Nezhad A (2015) Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach Learn 100:49–73. https://doi.org/10.1007/s10994-014-5471-y
MathSciNet
Article
MATH
Google Scholar
Muggleton SH, Schmid U, Zeller C, Tamaddoni-Nezhad A, Besold T (2018) Ultra-strong machine learning: comprehensibility of programs learned with ILP. Mach Learn 107(7):1119–1140. https://doi.org/10.1007/s10994-018-5707-3
MathSciNet
Article
MATH
Google Scholar
Niessen C, Göbel K, Siebers M, Schmid U Time to forget: a review and conceptual framework of intentional forgetting in the digital world of work. Z Arbeits Org [German Journal of Work and Organizational Psychology] (to appear)
Potter J, Wetherell M (1987) Discourse and social psychology: beyond attitudes and behaviour. Sage, Thousand Oaks
Google Scholar
Pu P, Chen L (2007) Trust-inspiring explanation interfaces for recommender systems. Knowl Based Syst 20(6):542–556
Article
Google Scholar
Rabold J, Siebers M, Schmid U (2018) Explaining black-box classifiers with ILP—empowering LIME with Aleph to approximate non-linear decisions with relational rules. In: Riguzzi F, Bellodi E, Zese R (eds) Proceedings of the 28th international conference on inductive logic programming, pp 105–117
Reed SK, Bolstad CA (1991) Use of examples and procedures in problem solving. J Exp Psychol Learn Mem Cogn 17(4):753–766
Article
Google Scholar
Ribeiro MT, Singh S, Guestrin C (2016) “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 1135–1144. ACM. http://arxiv.org/abs/1602.04938
Roth-Berghofer T, Richter MM (2008) Schwerpunkt: Erklärungen [special issue: explanations]. Künstliche Intell 22(2)
Sadoski M, Paivio A (2013) Imagery and text: a dual coding theory of reading and writing. Routledge, Abingdon
Book
Google Scholar
Schmid U (1994) Programmieren lernen: Unterstützung des Erwerbs rekursiver Programmiertechniken durch Beispielfunktionen und Erklärungstexte [Learning programming: Acquisition of recursive programming skills from examples and explanations]. Kognitionswissenschaft 4(1):47–54
Google Scholar
Schmid U (2018) Inductive programming as approach to comprehensible machine learning. In: Beierle C, Kern-Isberner G, Ragni M, Stolzenburg F, Thimm M (eds) Proceedings of the 7th workshop on dynamics of knowledge and belief (DKB-2018) and the 6th workshop KI & Kognition (KIK-2018), co-located with 41st German conference on artificial intelligence, vol 2194. CEUR Workshop Proceedings
Schmid U, Kitzelmann E (2011) Inductive rule learning on the knowledge level. Cogn Syst Res 12(3):237–248
Article
Google Scholar
Siebers M, Göbel K, Niessen C, Schmid U (2017) Requirements for a companion system to support identifying irrelevancy, pp 1–2. https://doi.org/10.1109/COMPANION.2017.8287076
Soucek R, Moser K (2010) Coping with information overload in email communication: evaluation of a training intervention. Comput Hum Behav 26(6):1458–1466. https://doi.org/10.1016/j.chb.2010.04.024
Article
Google Scholar
Srinivasan A (2004) The Aleph manual. http://www.cs.ox.ac.uk/activities/machinelearning/Aleph/
Suthers DD (1993) An analysis of explanation and its implications for the design of explanation planners. Ph.D. Thesis, University of Massachusetts
Sweeney L (2001) Information explosion. In: Zayatz L, Doyle P, Theeuwes J, Lane J (eds) Confidentiality, disclosure, and data access: theory and practical applications for statistical agencies. Urban Institute, Washington, pp 43–74
Google Scholar
Tintarev N, Masthoff J (2012) Evaluating the effectiveness of explanations for recommender systems. User Model User Adapt Interact 22(4):399–439
Article
Google Scholar
Tintarev N, Masthoff J (2015) Explaining recommendations: design and evaluation. In: Recommender systems handbook. Springer, Berlin, pp 353–382
Chapter
Google Scholar
Wang W, Benbasat I (2007) Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J Manag Inf Syst 23(4):217–246. https://doi.org/10.2753/MIS0742-1222230410
Article
Google Scholar
Winston PH (1975) Learning structural descriptions from examples. In: Winston PH (ed) The psychology of computer vision. McGraw-Hill, New York, pp 157–210
Google Scholar
Zeller C, Schmid U (2016) Automatic generation of analogous problems to help resolving misconceptions in an intelligent tutor system for written subtraction. In: Coman A, Kapetanakis S (eds) Workshops proceedings for the 24th international conference on case-based reasoning, CEUR workshop proceedings, vol 1815, pp 108–117. http://ceur-ws.org/Vol-1815/paper11.pdf
Zeller C, Schmid U (2017) A human like incremental decision tree algorithm: combining rule learning, pattern induction, and storing examples. In: Leyer M (ed) LWDA conference proceedings, vol 1917, pp 64–73. CEUR workshop proceedings. http://ceur-ws.org/Vol-1917/paper12.pdf