Acknowledgements

may turn out to be necessary, when inductive modeling, i.e., learning proceeds. Therefore, the word "data understanding" in the CRISP model (see Figure 1) is considered inappropriate and, hence, substituted by "data analysis" in the approach shown in Figure 3. This figure is intended to visualize both the dynamics of the data and of the model spaces. Hypothetical data

When speaking about logics and its algorithmic use, it is strictly advisable to stay within the limits of first-order predicate calculus [71]. The selection or the design of a logic means to decide about the signature of the language and about axiom sets of background knowledge. Under the assumption of a given logic, business understanding and data analysis underpin an impression of what the current analysis process is about. To say it more practically, what might be typical statements arrived at by the end of the data mining process? In the authors' digital game case study, by way of illustration, typical statements explain a human player's action under conditions of a play state [4, 5]. In their business intelligence application [6–8], formulas relate business data and temporal information of largely varying granularity. As soon as the type of expected formulas becomes clear, the next design task is to specify an indexed family of

Within the authors' framework, a crucial step is the modification of a space of hypotheses. There are heuristics discussed in [8] that shall be briefly surveyed. An automation may require,

The human user's activities are syntactically analyzed. In case there occur terms that have no corresponding sort, constant, function, or predicate names in the formulas of the current space of hypotheses, a limitation of the terminology is detected. The system is "unable to speak about what the user is doing." A case discussed in [8], p. 234, is "retracement of business volume." Retracement is interpreted as inequality with a (large) factor in it, and some sequence

Methodologies, guidelines, and process models aiming at (logical) model space construction

The authors' present approach to mining human-computer interaction data works well in applications that provide larger amounts of data [3–6]. The novel dynamic approach to the generation of model spaces exceeds the power of preceding approaches significantly [6, 73]. However successful in the cited prototypical applications, the approach may fail under conditions of small amounts of data. Consequently, it seems inappropriate to applications such as recommender systems. Perhaps, the authors' approach would work when applied to accumulated data of larger numbers of users. If so, the particular outcome would be something like a

understanding is seen as the preliminary result of data mining.

60 Data Mining

logical formulas. This forms the first space of hypothetical models.

of such formulas of properly increasing strength is automatically generated.

are worth much more future research work and practical exploration.

theory of mind of a user stereotype. Related questions are still open.

to some extent, natural language processing.

6. Summary, conclusion, and outlook

After the second author—inspired by some fascinating results in behavioral sciences has introduced the concept and coined the term of theory of mind modeling and induction in 2012, the two authors' student Bernd Schmidt has undertaken the endeavor to provide the first theory of mind modeling and induction application. The authors are grateful to him for his engaged and excellent work and for his continuous willingness to meet whatsoever requirements.

Working on an internship, Rosalie Schnappauf, then a student of the University of Rostock, took part in a series of experiments demonstrating that Bernd Schmidt's implementation of identification by enumeration does really work and allows for the fully computerized induction of a human game player's goals and intentions—a very first case of, so to speak, mining HCI data for theory of mind induction.

[3] Jantke KP. Patterns of game playing behavior as indicators of mastery. In: Ifenthaler D, Eseryel D, Ge X, editors. Assessment in Game-Based Learning: Foundations, Innovations, and Perspectives. New York, Heidelberg, Dordrecht, London: Springer; 2012. pp. 85-103

Mining HCI Data for Theory of Mind Induction http://dx.doi.org/10.5772/intechopen.74400 63

[4] Schmidt B. Theory of Mind Player Modeling [Bachelor Thesis]. Erfurt, Germany: Univer-

[5] Jantke KP, Schmidt B, Schnappauf R. Next generation learner modeling by theory of mind model induction. In: Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016), 21–23 April 2016, Rome, Italy. Sétubal: SCITEPRESS,

[6] Arnold O, Drefahl S, Fujima J, Jantke KP. Co-operative knowledge discovery based on meme media, natural language processing and theory of mind modeling and induction. In: Proceedings of the International Conference on e-Society; 20–22 April 2017; Budapest,

[7] Jantke KP, Fujima J. Analysis, visualization and exploration scenarios: Formal methods for systematic meta studies of big data applications. In: Grand E, Kotzinos D, Laurent D, Spyratos N, Tanaka Y, editors. Information Search, Integration, and Personalization, 10th International Workshop (ISIP 2016), 10–12 October 2015, Grand Forks, ND, USA, Revised Selected Papers. Heidelberg, Dordrecht, London, New York: Springer; 2016. pp. 107-127

[8] Fujima J, Arnold O, Jantke KP, Schmidt B. Interaction semantics vs. interaction syntax in data visualization and exploration. Design, implementation and utilization of meme media. In: Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (FLAIRS-30), 22–24 May. Vol. 2017. Marco Island, FL, USA. Palo Alto,

[10] Popper KR. Conjectures and Refutations. London: Routledge & Kegan Paul; 1963

room/ PressAnnouncements/ucm584933.htm [Accessed: November 22, 2017]

tions, and Use. Hershey, PA, USA: Idea Group Inc.; 2011. pp. vii-viii

[11] FDA. FDA approves pill with sensor that digitally tracks if patients have ingested their medication [Internet]. 2017. Available from: https://www.fda.gov/NewsEvents/News-

[12] Jantke KP. Foreword. In Kreuzberger G, Lunzer A, Kaschek RH, editors. Interdisciplinary Advances in Adaptive and Intelligent Assistant Systems: Concepts, Techniques, Applica-

[13] Kaschek RH, editor. Intelligent Assistant Systems: Concepts, Techniques and Technolo-

[14] Kreuzberger G, Lunzer A, Kaschek RH, editors. Interdisciplinary Advances in Adaptive and Intelligent Assistant Systems: Concepts, Techniques, Applications, and Use. Hershey,

sity of Applied Sciences; 2014

CA: AAAI; 2017. pp. 231-234

Hungary. Sétubal: IADIS Press; 2017. pp. 27-38

[9] Popper KR. Logik der Forschung. Tübingen; 1934

gies. Hershey, PA, USA: Idea Group Inc.; 2007

London, Melbourne, Singapore: Idea Group Inc.; 2011

p. 499-506

Rosalie's and Bernd's success encouraged the authors to attack harder application problems and to develop the generalized approach to dynamic identification by enumeration.

Part of the work reported in this chapter has been supported by the German Federal Ministry for Education and Research (BMBF) within the joint research project ODIN aiming at Open Data INovation. The authors' subprojects KVASIR (Erfurt University of Applied Sciences) and BALDUR (ADICOM Software) are currently administrated by the agency Projektträger Jülich (PtJ, see www.ptj.de) under the contracts 03PSWKPA and 03PSWKPB, respectively.
