<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">\begin{thebibliography}{100}
\expandafter\ifx\csname url\endcsname\relax
  \def\url#1{\texttt{#1}}\fi
\expandafter\ifx\csname urlprefix\endcsname\relax\def\urlprefix{URL }\fi
\expandafter\ifx\csname href\endcsname\relax
  \def\href#1#2{#2} \def\path#1{#1}\fi

\bibitem{ch12-Pawlak-in-Mem-2012}
A.~Skowron, Z.~Suraj (Eds.), Rough Sets and Intelligent Systems. Professor
  Zdzislaw Pawlak in Memoriam, Series Intelligent Systems Reference Library,
  Springer, 2013.
\newblock \href {https://doi.org/10.1007/978-3-642-30344-9;
  10.1007/978-3-642-30341-8} {\path{doi:10.1007/978-3-642-30344-9;
  10.1007/978-3-642-30341-8}}.

\bibitem{ch12-SON-AS-Memoriambook2013}
H.~S. Nguyen, A.~Skowron, Rough sets: {F}rom rudiments to challenges, in:
  Skowron and Suraj  \cite{ch12-Pawlak-in-Mem-2012}, pp. 75--173.
\newblock \href {https://doi.org/10.1007/978-3-642-30344-9_3}
  {\path{doi:10.1007/978-3-642-30344-9_3}}.

\bibitem{ch12-Rodos2021}
S.~Dutta, A.~Skowron, Toward a computing model dealing with complex phenomena:
  Interactive granular computing, in: N.~T. Nguyen, L.~Iliadis,
  I.~Maglogiannis, B.~Trawinski (Eds.), Computational Collective Intelligence -
  13th International Conference, {ICCCI} 2021, Rhodes, Greece, September 29 -
  October 1, 2021, Proceedings, Vol. 12876 of Lecture Notes in Computer
  Science, Springer, Heidelberg, 2021, pp. 199--214.
\newblock \href {https://doi.org/10.1007/978-3-030-88081-1_15}
  {\path{doi:10.1007/978-3-030-88081-1_15}}.

\bibitem{ch12-CSP2021}
S.~Dutta, A.~Skowron,
  \href{http://ceur-ws.org/Vol-2951/paper18.pdf}{Interactive granular computing
  connecting abstract and physical worlds: An example}, in: H.~Schlingloff,
  T.~Vogel (Eds.), Proceedings of the 29th International Workshop on
  Concurrency, Specification and Programming (CS{\&amp;}P 2021), Berlin, Germany,
  September 27-28, 2021, Vol. 2951 of {CEUR} Workshop Proceedings, CEUR-WS.org,
  2021, pp. 46--59.
\newline\urlprefix\url{http://ceur-ws.org/Vol-2951/paper18.pdf}

\bibitem{ch5-InfSci-sub-2024}
A.~Skowron, J.~Stepaniuk, Toward rough set based insightful reasoning in
  intelligent systems (2024) 2(submitted).

\bibitem{ch5-ASC-sub-AS-DS-2024}
A.~Skowron, D.~{\'S}l\k{e}zak, A rough set perspective on intelligence
  systems(submitted) (2024).

\bibitem{ch12-wisdom-webseter}
{Webster's New World College Dictionary}, Wisdom,
  \url{https://www.yourdictionary.com/wisdom}, accessed: 2024.11.20 (2024).

\bibitem{ch12-perception-book2024}
B.~Nanay, Perception: The Basics, Routledge, Taylor\&amp; Francis, New York, NY,
  2024.

\bibitem{ch12-Barsalou-perceptual-symbol-sys}
L.~W. Barsalou, Perceptual symbol systems, Behavioral and Brain Sciences 22
  (1999) 577--660.
\newblock \href {https://doi.org/10.1017/S0140525X99002149}
  {\path{doi:10.1017/S0140525X99002149}}.

\bibitem{ch12-Noe-book}
A.~No\"{e}, Action in Perception, MIT Press, 2004.

\bibitem{ch12-zadeh01new}
L.~A. Zadeh, A new direction in {AI}: Toward a computational theory of
  perceptions, AI Magazine 22~(1) (2001) 73--84.
\newblock \href {https://doi.org/10.1609/aimag.v22i1.1545}
  {\path{doi:10.1609/aimag.v22i1.1545}}.

\bibitem{ch5-Valiant}
L.~Valiant, Research directions,
  \url{https://people.seas.harvard.edu/~valiant/researchinterests.htm},
  accessed: 2024.05.28 (2024).

\bibitem{ch12-Mitchell-analogy}
M.~Mitchell, Abstraction and analogy-making in artificial intelligence, Annals
  Reports of the New York Academy of Sciences 1505 (2021) 79--101.
\newblock \href {https://doi.org/10.1111/nyas.14619}
  {\path{doi:10.1111/nyas.14619}}.

\bibitem{ch12-Mitchell-intelligence}
M.~Mitchell, \href{arxiv.org/abs/2112.06864}{Frontiers in Collective
  Intelligence: A Workshop Report 2021}, Santa Fe Institute, Santa Fe, NM,
  2021.
\newline\urlprefix\url{arxiv.org/abs/2112.06864}

\bibitem{ch12-CampagnerCD22}
A.~Campagner, D.~Ciucci, V.~Dorigatti, Uncertainty representation in dynamical
  systems using rough set theory, Theoretical of Computer Science 908 (2022)
  28--42.
\newblock \href {https://doi.org/10.1016/J.TCS.2021.11.009}
  {\path{doi:10.1016/J.TCS.2021.11.009}}.

\bibitem{ch12-DongWC23}
L.~Dong, R.~Wang, D.~Chen, Incremental feature selection with fuzzy rough sets
  for dynamic data sets, Fuzzy Sets Syst. 467 (2023) 108503.
\newblock \href {https://doi.org/10.1016/J.FSS.2023.03.006}
  {\path{doi:10.1016/J.FSS.2023.03.006}}.

\bibitem{ch12-Wolski10}
M.~Wolski, Rough sets in terms of discrete dynamical systems, in: M.~S.
  Szczuka, M.~Kryszkiewicz, S.~Ramanna, R.~Jensen, Q.~Hu (Eds.), Rough Sets and
  Current Trends in Computing - 7th International Conference, {RSCTC} 2010,
  Warsaw, Poland, June 28-30,2010. Proceedings, Vol. 6086 of Lecture Notes in
  Computer Science, Springer, 2010, pp. 237--246.
\newblock \href {https://doi.org/doi.org/10.1007/978-3-642-13529-3_26}
  {\path{doi:doi.org/10.1007/978-3-642-13529-3_26}}.

\bibitem{ch12-smart-mach-think}
S.~Gerrish, How Smart Machines Think, MIT Press, Cambridge, MA, 2018.

\bibitem{ch10-keefe00book}
R.~Keefe, Theories of Vagueness, Cambridge Studies in Philosophy, Cambridge,
  UK, 2000.

\bibitem{ch12-friedman01elements}
T.~Hastie, R.~Tibshirani, J.~H. Friedman, The Elements of Statistical Learning:
  Data Mining, Inference, and Prediction, Springer-Verlag, Heidelberg, 2009.
\newblock \href {https://doi.org/10.1007/b94608} {\path{doi:10.1007/b94608}}.

\bibitem{ch12-skowron05Marcus}
A.~Skowron, Rough sets and vague concepts, Fundamenta Informaticae 64~(1-4)
  (2005) 417--431.

\bibitem{ch12-SkowronSwiniarski05Regina}
A.~Skowron, R.~W. Swiniarski, Rough sets and higher order vagueness, in:
  D.~Slezak, G.~Wang, M.~S. Szczuka, I.~D{\"{u}}ntsch, Y.~Yao (Eds.), Rough
  Sets, Fuzzy Sets, Data Mining, and Granular Computing, 10th International
  Conference, RSFDGrC 2005, Regina, Canada, August 31 - September 3, 2005,
  Proceedings, Part {I}, Vol. 3641 of Lecture Notes in Computer Science,
  Springer, 2005, pp. 33--42.
\newblock \href {https://doi.org/10.1007/11548669_4}
  {\path{doi:10.1007/11548669_4}}.

\bibitem{ch12-Barwise97book}
J.~Barwise, J.~Seligman, Information Flow: The Logic of Distributed Systems,
  Cambridge University Press, Cambridge, 1997.
\newblock \href {https://doi.org/10.1017/CBO9780511895968}
  {\path{doi:10.1017/CBO9780511895968}}.

\bibitem{ch12-WisIGrC17jaw}
A.~Jankowski, Interactive Granular Computations in Networks and Systems
  Engineering: A Practical Perspective, Vol.~17 of Lecture Notes in Networks
  and Systems, Springer, Heidelberg, 2017.
\newblock \href {https://doi.org/10.1007/978-3-319-57627-5}
  {\path{doi:10.1007/978-3-319-57627-5}}.

\bibitem{ch12-Skow:Szcz09}
A.~Skowron, M.~Szczuka, Toward interactive computations: A rough-granular
  approach, in: J.~Koronacki, Z.~Ra{\'s}, S.~Wierzcho{\'n}, J.~Kacprzyk (Eds.),
  Advances in Machine Learning II: Dedicated to the Memory of Professor Ryszard
  S. Michalski, Vol. 263 of Studies in Computational Intelligence, Springer,
  Heidelberg, 2009, pp. 23--42.
\newblock \href {https://doi.org/10.1007/978-3-642-05179-1_2}
  {\path{doi:10.1007/978-3-642-05179-1_2}}.

\bibitem{ch5-Bazan08TRSIX}
J.~Bazan, Hierarchical classifiers for complex spatio-temporal concepts, in:
  J.~F. Peters, A.~Skowron, H.~Rybi{\'n}ski (Eds.), Transactions on Rough Sets
  IX: Journal Subline, Vol. 5390 of Lecture Notes in Computer Science,
  Springer, Heidelberg, 2008, pp. 474--750.
\newblock \href {https://doi.org/10.1007/978-3-540-89876-4_26}
  {\path{doi:10.1007/978-3-540-89876-4_26}}.

\bibitem{ch10-SON-AS-Memoriambook2013}
H.~S. Nguyen, A.~Skowron, Rough sets: {F}rom rudiments to challenges, in:
  A.~Skowron, Z.~Suraj (Eds.), Rough Sets and Intelligent Systems. Professor
  Zdzislaw Pawlak in Memoriam, Series Intelligent Systems Reference Library,
  Springer, 2013, pp. 75--173.
\newblock \href {https://doi.org/10.1007/978-3-642-30344-9;
  10.1007/978-3-642-30341-8} {\path{doi:10.1007/978-3-642-30344-9;
  10.1007/978-3-642-30341-8}}.

\bibitem{ch10-Sorites16}
A.~Skowron, A.~Jankowski, P.~Wasilewski, Rough sets and sorites paradox,
  Fundamenta Informaticae 157~(4) (2018) 371--384.

\bibitem{ch12-Zadeh-InfoGran97}
L.~A. Zadeh, Toward a theory of fuzzy information granulation and its
  centrality in human reasoning and fuzzy logic, Fuzzy Sets Systems 90~(2)
  (1997) 111--127.
\newblock \href {https://doi.org/10.1016/S0165-0114(97)00077-8}
  {\path{doi:10.1016/S0165-0114(97)00077-8}}.

\bibitem{ch12-Pedrycz-AS-VK-handGC08}
W.~Pedrycz, S.~Skowron, V.~Kreinovich (Eds.), Handbook of Granular Computing,
  John Wiley \&amp; Sons, Hoboken, NJ, 2008.
\newblock \href {https://doi.org/10.1002/9780470724163}
  {\path{doi:10.1002/9780470724163}}.

\bibitem{ch10-Pedrycz-book-Intro-fuzzy2021}
W.~Pedrycz, An Introduction to Computing with Fuzzy Sets. Analysis, Design, and
  Applications, Vol. 190 of Intelligent Systems Reference Library, Springer
  Nature, Cham, 2021.
\newblock \href {https://doi.org/10.1007/978-3-030-52800-3}
  {\path{doi:10.1007/978-3-030-52800-3}}.

\bibitem{ch12-Pedrycz-GrC-Manifesto2018}
W.~Pedrycz, Granular computing for data analytics: a manifesto of human-centric
  computing, IEEE/CAA Journal of Automatica Sinica 5 (2018) 1025--1034.
\newblock \href {https://doi.org/10.1109/jas.2018.7511213}
  {\path{doi:10.1109/jas.2018.7511213}}.

\bibitem{ch12-bargiela03}
A.~Bargiela, W.~Pedrycz, Granular Computing: {A}n Introduction, Kluwer Academic
  Publishers, 2003.

\bibitem{ch12-JarekBook08}
J.~Stepaniuk (Ed.), Rough-Granular Computing in Knowledge Discovery and Data
  Mining, Studies in Computational Intelligence, Springer, Heidelberg, 2008.

\bibitem{ch10-skowron03information}
A.~Skowron, J.~Stepaniuk, Information granules and rough-neural computing, in:
  S.~K. Pal, L.~Polkowski, A.~Skowron (Eds.), Rough-Neural Computing:
  Techniques for Computing with Words, Cognitive Technologies, Springer-Verlag,
  Heidelberg, 2004, pp. 43--84.

\bibitem{ch12-JSAS2023}
J.~Stepaniuk, A.~Skowron, Three--way approximation of decision granules based
  on the rough set approach, International Journal of Approximate Reasoning 155
  (2023) 1--16.
\newblock \href {https://doi.org/10.1016/j.ijar.2023.01.003}
  {\path{doi:10.1016/j.ijar.2023.01.003}}.

\bibitem{ch12-polkowski01rough}
L.~Polkowski, A.~Skowron, Rough mereological calculi of granules: A rough set
  approach to computation, Computational Intelligence: An International Journal
  17~(3) (2001) 472--492.
\newblock \href {https://doi.org/10.1111/0824-7935.00159}
  {\path{doi:10.1111/0824-7935.00159}}.

\bibitem{ch12-polkowski99towards}
L.~Polkowski, A.~Skowron, Towards adaptive calculus of granules, in: L.~A.
  Zadeh, J.~Kacprzyk (Eds.), Computing with Words in Information/Intelligent
  Systems, Physica-Verlag, Heidelberg, 1999, pp. 201--227.
\newblock \href {https://doi.org/10.1007/978-3-7908-1873-4}
  {\path{doi:10.1007/978-3-7908-1873-4}}.

\bibitem{ch12-skowron01toward}
A.~Skowron, Toward intelligent systems: Calculi of information granules,
  Bulletin of the International Rough Set Society 5~(1-2) (2001) 9--30.

\bibitem{ch12-skowron01information}
A.~Skowron, J.~Stepaniuk, Information granules: Towards foundations of granular
  computing, International Journal of Intelligent Systems 16~(1) (2001) 57--86.
\newblock \href
  {https://doi.org/10.1002/1098-111X(200101)16:1&lt;57::AID-INT6&gt;3.0.CO;2-Y}
  {\path{doi:10.1002/1098-111X(200101)16:1&lt;57::AID-INT6&gt;3.0.CO;2-Y}}.

\bibitem{ch12-skowron03information}
A.~Skowron, J.~Stepaniuk, Information granules and rough-neural computing, in:
  S.~K. Pal, L.~Polkowski, A.~Skowron (Eds.), Rough-Neural Computing:
  Techniques for Computing with Words, Cognitive Technologies, Springer-Verlag,
  Heidelberg, 2004, pp. 43--84.
\newblock \href {https://doi.org/10.1007/978-3-642-18859-6}
  {\path{doi:10.1007/978-3-642-18859-6}}.

\bibitem{ch12-bargiela-pedrycz-2003}
A.~Bargiela, W.~Pedrycz, Granular Computing. An Introduction, Vol. 717 of The
  Springer International Series in Engineering and Computer Science (SECS),
  Springer, New York, NY, 2003.
\newblock \href {https://doi.org/10.1007/978-1-4615-1033-8}
  {\path{doi:10.1007/978-1-4615-1033-8}}.

\bibitem{ch12-pedrycz-book-2013}
W.~Pedrycz, Granular Computing. Analysis And Design of Intelligent Systems, CRC
  Press, Taylor \&amp; Francis, 2013.

\bibitem{ch12-Pedrycz-justifable2022}
T.~Jing, C.~Wang, W.~Pedrycz, Z.~Li, G.~Succi, M.~Zhou, Granular models as
  networks of associations of information granules: {A} development scheme via
  augmented principle of justifiable granularity, Applied Soft Computing 115
  (2022) 108062.
\newblock \href {https://doi.org/10.1016/j.asoc.2021.108062}
  {\path{doi:10.1016/j.asoc.2021.108062}}.

\bibitem{ch12-Pedrycz-overview-2023}
J.~Qin, L.~Mart{\'{\i}}nez, W.~Pedrycz, X.~Ma, Y.~Liang, An overview of
  granular computing in decision-making: Extensions, applications, and
  challenges, Information Fusion 98 (2023) 101833.
\newblock \href {https://doi.org/10.1016/j.inffus.2023.101833}
  {\path{doi:10.1016/j.inffus.2023.101833}}.

\bibitem{ch5-ASJS1996}
A.~Skowron, J.~Stepaniuk, Tolerance approximation spaces, Fundam. Informaticae
  27~(2/3) (1996) 245--253.
\newblock \href {https://doi.org/10.3233/FI-1996-272311}
  {\path{doi:10.3233/FI-1996-272311}}.

\bibitem{ch5-RussellNorvig-bookAI}
S.~J. Russell, P.~Norvig, Artificial Intelligence A Modern Approach, Pearson
  Education, Inc., Upper Saddle River, NJ, 2021, 4th edition.

\bibitem{ch5-friedman01elements}
T.~Hastie, R.~Tibshirani, J.~H. Friedman, The Elements of Statistical Learning:
  Data Mining, Inference, and Prediction, Springer-Verlag, Heidelberg, 2009.
\newblock \href {https://doi.org/10.1007/b94608} {\path{doi:10.1007/b94608}}.

\bibitem{ch5-pawlak81information}
Z.~Pawlak, Information systems - theoretical foundations, Information Systems 6
  (1981) 205--218.
\newblock \href {https://doi.org/10.1016/0306-4379(81)90023-5}
  {\path{doi:10.1016/0306-4379(81)90023-5}}.

\bibitem{ch5-pawlakSkowron07InfSci}
Z.~Pawlak, A.~Skowron, Rudiments of rough sets, Information Sciences 177~(1)
  (2007) 3--27.
\newblock \href {https://doi.org/10.1016/j.ins.2006.06.003}
  {\path{doi:10.1016/j.ins.2006.06.003}}.

\bibitem{ch10-pawlakSkowron07InfSciC}
Z.~Pawlak, A.~Skowron, Rough sets and boolean reasoning, Information Sciences
  177~(1) (2007) 41--73.

\bibitem{PolkArtj-2015}
L.~Polkowski, P.~Artjemiew (Eds.), Granulation of Knowledge: Similarity Based
  Approach in Information and Decision Systems, Intelligent Systems Reference
  Library, Springer, Cham, 2015.
\newblock \href {https://doi.org/10.1007/978-3-319-12880-1}
  {\path{doi:10.1007/978-3-319-12880-1}}.

\bibitem{ch5-ensemble}
L.~Rokach, Ensemble Learning: Pattern Classification Using Ensemble Methods,
  Vol.~85 of Machine Perception and Artificial Intelligence, World Scientific,
  Singapore, 2019, (2nd edition).

\bibitem{argumentation2025}
R.~Ruiz-Dolz, S.~Heras, A.~Garcia-Fornes, An introduction to computational
  argumentation research from a human argumentation perspective, Autonomous
  Agents and Multi-Agent Systems 39 (2025).
\newblock \href {https://doi.org/doi.org/10.1007/s10458-025-09692-x}
  {\path{doi:doi.org/10.1007/s10458-025-09692-x}}.

\bibitem{ch5-pawlak82rough}
Z.~Pawlak, Rough sets, International Journal of Computer and Information
  Sciences 11 (1982) 341--356.
\newblock \href {https://doi.org/10.1007/BF01001956}
  {\path{doi:10.1007/BF01001956}}.

\bibitem{ch5-pawlak91rough}
Z.~Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Vol.~9 of
  System Theory, Knowledge Engineering and Problem Solving, Kluwer Academic
  Publishers, Dordrecht, The Netherlands, 1991.
\newblock \href {https://doi.org/10.1007/978-94-011-3534-4}
  {\path{doi:10.1007/978-94-011-3534-4}}.

\bibitem{ch12-JS2008}
J.~Stepaniuk, Rough -- Granular Computing in Knowledge Discovery and Data
  Mining, Vol. 152 of Studies in Computational Intelligence, Springer, 2008.
\newblock \href {https://doi.org/10.1007/978-3-540\-70801-8}
  {\path{doi:10.1007/978-3-540\-70801-8}}.

\bibitem{ch10-brown90boolean}
F.~Brown, Boolean Reasoning, Kluwer Academic Publishers, Dordrecht, 1990.

\bibitem{ch10-skowron92discernibility}
A.~Skowron, C.~Rauszer, The discernibility matrices and functions in
  information systems, in: R.~S{\l}owi{\'n}ski (Ed.), Intelligent Decision
  Support - Handbook of Applications and Advances of the Rough Sets Theory,
  Vol.~11 of System Theory, Knowledge Engineering and Problem Solving, Kluwer
  Academic Publishers, Dordrecht, The Netherlands, 1992, pp. 331--362.

\bibitem{ch5-Son06TRSV}
H.~S. Nguyen, Approximate boolean reasoning: Foundations and applications in
  data mining, Transactions on Rough Sets V: Journal Subline 5 (2006) 344--523.
\newblock \href {https://doi.org/:10.1007/11847465_16}
  {\path{doi::10.1007/11847465_16}}.

\bibitem{ch12-stone00layered}
P.~Stone, Layered Learning in Multi-Agent Systems: A Winning Approach to
  Robotic Soccer, The MIT Press, Cambridge, MA, 2000.

\bibitem{ch12-Bazan08TRSIX}
J.~Bazan, Hierarchical classifiers for complex spatio-temporal concepts, in:
  J.~F. Peters, A.~Skowron, H.~Rybi{\'n}ski (Eds.), Transactions on Rough Sets
  {\bf IX}: Journal Subline, Vol. 5390 of Lecture Notes in Computer Science,
  Springer, Heidelberg, 2008, pp. 474--750.
\newblock \href {https://doi.org/10.1007/978-3-540-89876-4_26}
  {\path{doi:10.1007/978-3-540-89876-4_26}}.

\bibitem{ch12-Japkowicz-book-2011}
N.~Japkowicz, M.~Shah, Evaluating Learning Algorithms: A Classification
  Perspective, Cambridge University Press, New York, NY, 2011.

\bibitem{ch12-RiskM-book-2019}
D.~Gu\'{e}gan, B.~K. Hassani, Risk Measurement. From Quantitative Measures to
  Management Decisions, Springer Nature, Cham, 2019.
\newblock \href {https://doi.org/10.1007/978-3-030-02680-6}
  {\path{doi:10.1007/978-3-030-02680-6}}.

\bibitem{ch12-AlgTrad2020-book}
S.~Jansen, Machine Learning for Algorithmic Trading, Packt, Birmingham -
  Mumbai, 2020.

\bibitem{ch12-Drug-Disovery-book-2021}
N.~Brown (Ed.), Artificial Intelligence in Drug Discovery, Vol.~75 of Drug
  Discovery Series, Royal Society of Chemistry, London, 2021.

\bibitem{ch5-rissanen85}
J.~Rissanen, Minimum-description-length principle, in: S.~Kotz, N.~Johnson
  (Eds.), Encyclopedia of Statistical Sciences, John Wiley \&amp; Sons, New York,
  NY, 1985, pp. 523--527.

\bibitem{ch12-Turing-test-mod}
C.~L. {Ortiz Jr.}, Why we need a physically embodied {T}uring test and what it
  might look like, {AI} Magazine 37 (2016) 55--62.
\newblock \href {https://doi.org/10.1609/aimag.v37i1.2645}
  {\path{doi:10.1609/aimag.v37i1.2645}}.

\bibitem{ch12-brooks1995mythical}
F.~P. {Brooks, Jr.}, {The Mythical Man-Month: Essays on Software Engineering,
  Anniversary Edition}, Addison-Wesley, 1995.

\bibitem{ch12-human-centered-AI}
B.~Shneiderman, Human -- Centered {AI}, Oxford University Press, Oxford, UK,
  2022.
\newblock \href {https://doi.org/10.1093/oso/9780192845290.001.0001}
  {\path{doi:10.1093/oso/9780192845290.001.0001}}.

\bibitem{ch12-human-in-the-loop}
R.~M. Monarch, Human-in-the-Loop Machine Learning. Active Learning and
  Annotation for Human-Centered {AI}, MANNING, Shelter Island, NY, 2021.

\bibitem{ch12-Omnici06}
A.~Omicini, A.~Ricci, M.~Viroli, The multidisciplinary patterns of interaction
  from sciences to computer science, in: D.~Goldin, S.~Smolka, P.~Wegner
  (Eds.), Interactive Computation: {T}he New Paradigm, Springer, 2006, pp.
  395--414.
\newblock \href {https://doi.org/10.1007/3-540-34874-3}
  {\path{doi:10.1007/3-540-34874-3}}.

\bibitem{ch12-vapnik98statistical}
V.~Vapnik, Statistical Learning Theory, John Wiley \&amp; Sons, New York, NY, 1998.

\bibitem{ch12-harnad87perception}
S.~Harnad, Categorical Perception: The Groundwork of Cognition, Cambridge
  University Press, New York, NY, 1987.

\bibitem{ch12-symbol-grounding}
S.~Harnad, The symbol grounding problem, Physica D 42 (1990) 335--346.
\newblock \href {https://doi.org/10.1016/0167-2789(90)90087-6}
  {\path{doi:10.1016/0167-2789(90)90087-6}}.

\bibitem{ch12-ACT-R-book2007}
J.~R. Anderson, How Can the Human Mind Occur in the Physical Universe?, Oxford
  University Press, New York, NJ, 2007.
\newblock \href {https://doi.org/10.1093/acprof:oso/9780195324259.001.0001}
  {\path{doi:10.1093/acprof:oso/9780195324259.001.0001}}.

\bibitem{ch12-Gershenson2004}
C.~Gershenson, F.~Heylighen, How can we think the complex?, in: K.~Richardson
  (Ed.), In Managing Organizational Complexity: {P}hilosophy, Theory and
  Application, Information Age Publishing, Greenwich, CT, 2005, pp. 47--61.

\bibitem{ch12-Jank:Skow09b}
A.~Jankowski, A.~Skowron, Wisdom technology: A rough-granular approach, in:
  M.~Marciniak, A.~Mykowiecka (Eds.), Bolc Festschrift, Vol. 5070 of Lectures
  Notes in Computer Science, Springer, Heidelberg, 2009, pp. 3--41.
\newblock \href {https://doi.org/10.1007/978-3-642-04735-0_1}
  {\path{doi:10.1007/978-3-642-04735-0_1}}.

\bibitem{ch12-Aritotle-attention}
{Aristotle}, On interpretation,
  \url{http://classics.mit.edu//Aristotle/interpretation.html}, accessed:
  2024.09.20 (2024).

\bibitem{ch12-physical-comp-Deutsch-art2000}
D.~Deutsch, A.~Ekert, R.~Lupacchini, Machines, logic and quantum physics,
  Neural Computation 6 (2000) 265--283.
\newblock \href {https://doi.org/10.2307/421056} {\path{doi:10.2307/421056}}.

\bibitem{ch12-AS-DS-IJCRS2023}
A.~Skowron, D.~{\'S}l\k{e}zak, Rough sets in interactive granular computing:
  {T}oward foundations for intelligent systems interacting with human experts
  and complex phenomena, in: Proceedings of the International Joint Conference
  on Rough Sets {(IJCRS 2023)}, Cracow, October 5-8, 2023, Vol. 14481 of
  Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2023, pp.
  xv--xxxvii.
\newblock \href {https://doi.org/doi.org/10.1007/978-3-031-50959-9}
  {\path{doi:doi.org/10.1007/978-3-031-50959-9}}.

\bibitem{ch12-Physics-informed-ML2021}
G.~E. Karniadakis, I.~G. Kevrekidis, L.~Lu, P.~Perdikaris, S.~Wang, L.~Yang,
  Physics-informed machine learning, Nature Reviews Physics 3 (2021) 422--440.
\newblock \href {https://doi.org/10.1038/s42254-021-00314-5}
  {\path{doi:10.1038/s42254-021-00314-5}}.

\bibitem{ch12-AI-meets-physics-2024}
L.~Jiao, X.~Song, C.~You, X.~Liu, L.~Li, P.~Chen, X.~Tang, Z.~Feng, F.~Liu,
  Y.~Guo, S.~Yang, Y.~Li, X.~Zhang, W.~Ma, S.~Wang, J.~Bai, B.~Hou, {AI} meets
  physics: a comprehensive survey, Artiicial Intelligence Review 57~(9) (2024)
  256.
\newblock \href {https://doi.org/10.1007/S10462-024-10874-4}
  {\path{doi:10.1007/S10462-024-10874-4}}.

\bibitem{ch10-IGrCSpr16}
A.~Skowron, A.~Jankowski, S.~Dutta, Interactive granular computing, in:
  Granular Computing, Springer, Heidelberg, 2016, pp. 95--113, (DOI:
  10.1007/s41066-015-0002-1).

\bibitem{ch12-skowron2016rough}
A.~Skowron, A.~Jankowski, {Rough Sets and Interactive Granular Computing},
  Fundamenta Informaticae 147~(2-3) (2016) 371--385.
\newblock \href {https://doi.org/10.3233/FI-2016-1413}
  {\path{doi:10.3233/FI-2016-1413}}.

\bibitem{ch12-Durgapur2021}
S.~Dutta, A.~Skowron, Interactive granular computing model for intelligent
  systems, in: Z.~Shi, M.~K. Chakraborty, S.~Kar (Eds.), Intelligence Science
  {III:} 4th {IFIP} {TC} 12 International Conference, {ICIS} 2020, Durgapur,
  India, February 24-27, 2021, Revised Selected Papers, Vol. 623 of {IFIP}
  Advances in Information and Communication Technology, Springer International
  Publishing, 2020, pp. 37--48.
\newblock \href {https://doi.org/10.1007/978-3-030-74826-5_4}
  {\path{doi:10.1007/978-3-030-74826-5_4}}.

\bibitem{ch12-ICCCI2021}
S.~Dutta, A.~Skowron, Toward a computing model dealing with complex phenomena:
  Interactive granular computing, in: N.~T. Nguyen, L.~Iliadis,
  I.~Maglogiannis, B.~Trawinski (Eds.), Computational Collective Intelligence -
  13th International Conference, {ICCCI} 2021, Rhodes, Greece, September 29 -
  October 1, 2021, Proceedings, Vol. 12876 of Lecture Notes in Computer
  Science, Springer, 2021, pp. 199--214.
\newblock \href {https://doi.org/10.1007/978-3-030-88081-1_15}
  {\path{doi:10.1007/978-3-030-88081-1_15}}.

\bibitem{ch10-WisIGrC17jaw}
A.~Jankowski, Interactive Granular Computations in Networks and Systems
  Engineering: A Practical Perspective, Vol.~17 of Lecture Notes in Networks
  and Systems, Springer, Heidelberg, 2017.

\bibitem{ch12-dutta2021interactive}
S.~Dutta, A.~Skowron, \href{ceur-ws.org/Vol-2951/paper18.pdf}{{Interactive
  Granular Computing Connecting Abstract and Physical Worlds: An Example}}, in:
  H.~Schlingloff, T.~Vogel (Eds.), Proceedings of the 29th International
  Workshop on Concurrency, Specification and Programming (CS{\&amp;}P 2021),
  Berlin, Germany, September 27-28, 2021, Vol. 2951 of {CEUR} Workshop
  Proceedings, CEUR-WS.org, 2021, pp. 46--59.
\newline\urlprefix\url{ceur-ws.org/Vol-2951/paper18.pdf}

\bibitem{ch12-IJCRS24}
A.~Skowron, S.~Dutta, Information system in the light of interactive granular
  computing, in: M.~Hu, C.~Cornelis, Y.~Zhang, P.~Lingras, D.~{\'S}l\k{e}zak,
  J.~Yao (Eds.), Rough Sets - International Joint Conference, {IJCRS} 2024,
  Halifax, Canada, May 17-20, 2024, Proceedings, Part I, Vol. 14839 of Lecture
  Notes in Computer Science, Springer, Cham, 2024, pp. 223--237, (in print).
\newblock \href {https://doi.org/10.1007/978-3-031-65665-1_14}
  {\path{doi:10.1007/978-3-031-65665-1_14}}.

\bibitem{ch12-Thiele-judgment-book}
L.~P. Thiele, The Heart of Judgment: Practical Wisdom, Neuroscience, and
  Narrative, Cambridge University Press, Cambridge, UK, 2010.
\newblock \href {https://doi.org/10.1017/CBO9780511498718}
  {\path{doi:10.1017/CBO9780511498718}}.

\bibitem{ch10-TheoriesJudgment-book-2006}
W.~M. Martin (Ed.), Theories of Judgment. Psychology, Logic, Phenomenology,
  Cambridge University Press, New York, 2006.

\bibitem{ch12-Mitchell2019-AIbook}
M.~Mitchell, Artificial Intelligence: A Guide for Thinking Humans, Farrar,
  Straus, Giroux, New York, NY, 2019.

\bibitem{ch12-Povalej2009}
P.~Povalej, M.~Verlic, G.~Stiglic, Discovery systems, in: R.~A. Meyers (Ed.),
  Encyclopedia of Complexity and Systems Science, Springer New York, New York,
  NY, 2009, pp. 1982--2002.
\newblock \href {https://doi.org/10.1007/978-0-387-30440-3_125}
  {\path{doi:10.1007/978-0-387-30440-3_125}}.

\bibitem{ch12-Comp-Creativity-book-2019}
T.~Veale, F.~A. Cardoso (Eds.), Computational Creativity. The Philosophy and
  Engineering of Autonomously Creative Systems, Computational Synthesis and
  Creative Systems, Springer, Cham, 2019.
\newblock \href {https://doi.org/10.1007/978-3-319-43610-4}
  {\path{doi:10.1007/978-3-319-43610-4}}.

\bibitem{ch5-human-centered-AI}
B.~Shneiderman, Human -- Centered {AI}, Oxford University Press, Oxford, UK,
  2022.
\newblock \href {https://doi.org/10.1093/oso/9780192845290.001.0001}
  {\path{doi:10.1093/oso/9780192845290.001.0001}}.

\bibitem{ch5-human-in-the-loop}
R.~M. Monarch, Human-in-the-Loop Machine Learning. Active Learning and
  Annotation for Human-Centered {AI}, MANNING, Shelter Island, NY, 2021.

\bibitem{Human-in-the-Loop24}
J.~C. Santos, M.~S. Santos, P.~H. Abreu, An interpretable human-in-the-loop
  process to improve medical image classification, in: I.~Miliou,
  N.~Piatkowski, P.~Papapetrou (Eds.), Advances in Intelligent Data Analysis
  {XXII} - 22nd International Symposium on Intelligent Data Analysis, {IDA}
  2024, Stockholm, Sweden, April 24-26, 2024, Proceedings, Part {I}, Vol. 14641
  of Lecture Notes in Computer Science, Springer, 2024, pp. 179--190.
\newblock \href {https://doi.org/10.1007/978-3-031-58547-0_15}
  {\path{doi:10.1007/978-3-031-58547-0_15}}.

\bibitem{ch12-chatbots-book24}
R.~Ciesla, The Book of Chatbots. From ELIZA to ChatGPT, Springer, Cham, 2024.
\newblock \href {https://doi.org/10.1007/978-3-031-51004-5}
  {\path{doi:10.1007/978-3-031-51004-5}}.

\bibitem{ch12-Kahneman-PrizeLecture2002}
D.~Kahneman, Maps of bounded rationality: {P}sychology for behavioral
  economics, The American Economic Review 93 (2002) 1449--1475.
\newblock \href {https://doi.org/10.1257/000282803322655392}
  {\path{doi:10.1257/000282803322655392}}.

\bibitem{ch12-Robot-design-PNAS}
D.~Matthews, A.~Spielberg, D.~Rus, S.~Kriegman, J.~Bongard, Efficient automatic
  design of robots, PNAS 120 (2023) 1--7.
\newblock \href {https://doi.org/10.1073/pnas.2305180120}
  {\path{doi:10.1073/pnas.2305180120}}.

\bibitem{ch12-Maslov-hier1943}
A.~H. Maslov, A theory of human motivation, Psychological Review 50 (1943)
  370--396.
\newblock \href {https://doi.org/10.1037/h0054346}
  {\path{doi:10.1037/h0054346}}.

\bibitem{ch10-stone00layered}
P.~Stone, Layered Learning in Multi-Agent Systems: A Winning Approach to
  Robotic Soccer, The MIT Press, Cambridge, MA, 2000.

\bibitem{ch10-Pearl}
J.~Pearl, Causal inference in statistics: An overview, Statistics Surveys 3
  (2009) 96--146.
\newblock \href {https://doi.org/10.1214/09-SS057}
  {\path{doi:10.1214/09-SS057}}.

\bibitem{ch12-Pedrycz2021}
W.~Pedrycz, An Introduction to Computing with Fuzzy Sets: Analysis, Design, and
  Applications, Vol. 190, Springer International Publishing, Cham, 2021, Ch.
  Granulation-Degranulation Processes, pp. 161--173.
\newblock \href {https://doi.org/10.1007/978-3-030-52800-3_11}
  {\path{doi:10.1007/978-3-030-52800-3_11}}.

\bibitem{ch12-Zadeh-RNC-book}
L.~A. Zadeh, Foreword, in: S.~K. Pal, L.~Polkowski, A.~Skowron (Eds.),
  Rough-Neural Computing: Techniques for Computing with Words, Cognitive
  Technologies, Springer-Verlag, Heidelberg, 2004, pp. IX--XI.
\newblock \href {https://doi.org/doi.org/10.1007/978-3-642-18859-6}
  {\path{doi:doi.org/10.1007/978-3-642-18859-6}}.

\bibitem{ch10-zadeh01new}
L.~A. Zadeh, A new direction in {AI}: Toward a computational theory of
  perceptions, AI Magazine 22~(1) (2001) 73--84.
\newblock \href {https://doi.org/10.1609/aimag.v22i1.1545}
  {\path{doi:10.1609/aimag.v22i1.1545}}.

\bibitem{ch12-LLM-book2024}
K.~Thakur, H.~G. Barker, A.-S.~K. Pathan (Eds.), Artificial Intelligence and
  Large Language Models: An Introduction to the Technological Future, Chapman
  and Hall/CRC, Boca Raton, FL, 2024.
\newblock \href {https://doi.org/10.1201/9781003474173}
  {\path{doi:10.1201/9781003474173}}.

\bibitem{ch12-LLM-DeepDive-book2024}
U.~Kamath, K.~Keenan, G.~Somers, S.~Sorenson, Large Language Models: A Deep
  Dive. Bridging Theory and Practice, Springer, Cham, 2024.
\newblock \href {https://doi.org/10.1007/978-3-031-65647-7}
  {\path{doi:10.1007/978-3-031-65647-7}}.

\bibitem{ch5-Barwise97book}
J.~Barwise, J.~Seligman, Information Flow: The Logic of Distributed Systems,
  Cambridge University Press, 1997.
\newblock \href {https://doi.org/10.1023/A:1008350722315}
  {\path{doi:10.1023/A:1008350722315}}.

\bibitem{ch12-InfFlow-GradedCons2019}
S.~Dutta, A.~Skowron, M.~K. Chakraborty, Information flow in logic for
  distributed systems: Extending graded consequence, Information Sciences 491
  (2019) 232--250.
\newblock \href {https://doi.org/10.1016/J.INS.2019.03.057}
  {\path{doi:10.1016/J.INS.2019.03.057}}.

\bibitem{ch12-Kosko-1986}
B.~Kosko, Fuzzy cognitive maps, International journal of Man-machine Studies 24
  (1986) 65--75.
\newblock \href {https://doi.org/10.1016/S0020-7373(86)80040-2}
  {\path{doi:10.1016/S0020-7373(86)80040-2}}.

\bibitem{ch12-Rugh-Fuzz-Cog-Net-2016}
G.~N{\'{a}}poles, I.~Grau, E.~Papageorgiou, R.~Bello, K.~Vanhoof, Rough
  cognitive networks, Knowledge Based Systems 91 (2016) 46--61.
\newblock \href {https://doi.org/10.1016/J.KNOSYS.2015.10.015}
  {\path{doi:10.1016/J.KNOSYS.2015.10.015}}.

\bibitem{ch12-NAPOLES201779}
G.~N\'{a}poles, R.~Falcon, E.~Papageorgiou, R.~Bello, K.~Vanhoof, Rough
  cognitive ensembles, International Journal of Approximate Reasoning 85 (2017)
  79--96.
\newblock \href {https://doi.org/https://doi.org/10.1016/j.ijar.2017.03.011}
  {\path{doi:https://doi.org/10.1016/j.ijar.2017.03.011}}.

\bibitem{ch12-CognMaps-book2024}
P.~J. Giabbanelli, G.~N\'{a}poles (Eds.), {Fuzzy Cognitive Maps: Best Practices
  and Modern Methods}, Springer, Cham, 2024.
\newblock \href {https://doi.org/10.1007/978-3-031-48963-1}
  {\path{doi:10.1007/978-3-031-48963-1}}.

\bibitem{ch5-IoT-book2020}
M.~Alam, K.~A. Shakil, S.~Khan (Eds.), Internet of Things ({IoT}): {C}oncepts
  and Applications, Springer, Cham, Switzerland, 2020.
\newblock \href {https://doi.org/10.1007/978-3-030-37468-6}
  {\path{doi:10.1007/978-3-030-37468-6}}.

\bibitem{ch12-FedLearn-book2023}
S.~Krishnan, A.~J. Anand, R.~Srinivasan, R.~Kavitha, S.~Suresh (Eds.),
  {Handbook on Federated Learning: Advances, Applications and Opportunities},
  CRC Press,Taylor \&amp; Francis Group, Boca Raton, FL, 2024.
\newblock \href {https://doi.org/10.1201/9781003384854}
  {\path{doi:10.1201/9781003384854}}.

\bibitem{ch5-skowron01information}
A.~Skowron, J.~Stepaniuk, Information granules: Towards foundations of granular
  computing, International Journal of Intelligent Systems 16~(1) (2001) 57--86.
\newblock \href
  {https://doi.org/10.1002/1098-111X(200101)16:1&lt;57::AID-INT6&gt;3.0.CO;2-Y}
  {\path{doi:10.1002/1098-111X(200101)16:1&lt;57::AID-INT6&gt;3.0.CO;2-Y}}.

\bibitem{ch12-Pedrycz-principle-of-just-ration2013}
W.~Pedrycz, W.~Homenda, Building the fundamentals of granular computing: a
  principle of justifiable granularity, Applied Soft Computing 13 (2013)
  4209--4218.
\newblock \href {https://doi.org/10.1016/j.asoc.2013.06.017}
  {\path{doi:10.1016/j.asoc.2013.06.017}}.

\bibitem{ch10-zadeh65fuzzy}
L.~A. Zadeh, Fuzzy sets, Information and Control 8 (1965) 338--353.
\newblock \href {https://doi.org/10.1016/S0019-9958(65)90241-X}
  {\path{doi:10.1016/S0019-9958(65)90241-X}}.

\bibitem{ch12-Bower-book}
J.~M. Bower, H.~Bolouri (Eds.), Computational Modeling of Genetic and
  Biochemical Networks, MIT Press, 2001.

\bibitem{ch12-RussellNorvig-bookAI}
S.~J. Russell, P.~Norvig, Artificial Intelligence A Modern Approach, Pearson
  Education, Inc., Upper Saddle River, NJ, 2021, 4th edition.

\bibitem{ch10-SankarPal-2021-RS-DeepLearning2021}
S.~Pal, {Rough Set and Deep Learning: Some Concepts}, Academia Letters
  {}~({Article 1849}) ({July 2021}) 1--6.
\newblock \href {https://doi.org/10.20935/AL1849} {\path{doi:10.20935/AL1849}}.

\bibitem{ch12-LifelongML-2018}
Z.~Chen, B.~Liu, Lifelong Machine Learning, Vol.~38 of Synthesis Lectures on
  Artificial Intelligence and Machine Learning, Springer Nature, Cham, 2018.
\newblock \href {https://doi.org/10.1007/978-3-031-01581-6}
  {\path{doi:10.1007/978-3-031-01581-6}}.

\bibitem{Vopson-info-mass-2019}
M.~M. Vopson, The mass-energy-information equivalence principle, AIP Advances 9
  (2019) 095206.
\newblock \href {https://doi.org/doi.org/10.1063/1.5123794}
  {\path{doi:doi.org/10.1063/1.5123794}}.

\end{thebibliography}
</pre></body></html>