Abstract
Artificial intelligence (AI) and machine learning (ML) research has traditionally focused most energy on constructing systems that can learn from data and/or environment interactions. This paper considers the parallel science of teaching: Artificial Pedagogy (AP). Teaching provides us with a method—aside from programming—for imparting our knowledge to AI systems, and it facilitates cumulative, online learning—which is especially important in cases where the combinatorics of sub-tasks preclude enumeration or a-priori modeling, or where unforeseeable novelty is inherent and unavoidable in the learner’s assignments. Teaching is a complex process not currently very well understood, and pedagogical theories proposed so far have exclusively targeted human learners. What is needed is a framework that relates the many facets of teaching, in a way that works for a range of learners including machines.
We present the Pedagogical Pentagon—a conceptual framework that identifies five core concepts of AP: learners, task-environments, testing, training and teaching. We describe these concepts, their interactions, and what we would need to know about them in the context of AP. The pentagon is meant to facilitate research in this complex new area by encouraging a structured and systematic approach organized around its five corners.
This work was sponsored in part by the School of Computer Science at Reykjavik University and by a Centers of Excellence Grant from the Science and Technology Policy Council of Iceland.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We use “knowledge” to refer to all kinds of knowledge, including beliefs (declarative), skills (procedural) and priorities (structural); cf. Sect. 3.2;.
- 2.
Note that the term “teaching” does not necessarily imply a mirroring of the human teacher-student setup—it is quite conceivable for an AI to have a built-in “automatic teaching mechanism”. That would not, however, change the need for a theory of teaching. While teaching does not change the inherent capabilities of AI systems in principle, it allows them to reach more of their potential more efficiently.
- 3.
The formulation of an intelligent system (or agent) interacting with the world (or environment) is most commonly used in control theory and reinforcement learning. However, it is a fully general formulation, that also covers traditional cases of e.g. supervised and unsupervised learning. Here the environment simply presents a (training) datum at each time step, the agent responds with a classification or prediction, and—in the case of supervised learning—the environment replies with the target outcome or an error signal.
- 4.
Generally speaking, there could be multiple learners and teachers, but here we focus on the one-on-one situation.
- 5.
Note that if the teacher is in the learner’s task-environment, every policy change alters the task-environment in some way.
References
Bieger, J.: Artificial pedagogy: a proposal. In: HLAI Doctoral Consortium (2016)
Bieger, J., Thórisson, K.R., Garrett, D.: Raising AI: tutoring matters. In: Goertzel, B., Orseau, L., Snaider, J. (eds.) AGI 2014. LNCS (LNAI), vol. 8598, pp. 1–10. Springer, Cham (2014). doi:10.1007/978-3-319-09274-4_1
Goertzel, B., Pennachin, C., Geisweiller, N.: AGI Preschool. In: Engineering General Intelligence, Part 1, pp. 337–354. Atlantis Press, Amsterdam (2014)
Hernández-Orallo, J.: The Measure of All Minds: Evaluating Natural and Artificial Intelligence. Cambridge University Press, Cambridge (2016)
Bieger, J., Thórisson, K.R., Steunebrink, B.R.: Evaluation of general-purpose artificial intelligence: why, what & how. In: Evaluating General-Purpose AI 2016. The Hague, Netherlands (2016)
Kotseruba, I., Gonzalez, O.J.A., Tsotsos, J.K.: A review of 40 years of cognitive architecture research: focus on perception, attention, learning and applications [cs] (2016). arxiv:1610.08602
Krathwohl, D.R.: A revision of Bloom’s taxonomy: an overview. Theor. Pract. 41(4), 212–218 (2016)
Legg, S., Hutter, M.: A collection of definitions of intelligence. Adv. AGI: concepts, architectures and algorithms 157, 17–24 (2007)
Marcus, G., Rossi, F., Veloso, M. (eds.): Beyond the Turing test. AI Mag. 37(1) (2016). 1st edn. AAAI
Piaget, J.: Piaget’s theory. In: Inhelder, B., Chipman, H.H., Zwingmann, C. (eds.) Piaget and His School. Springer, Heidelberg (1976). Springer Study Edition
Rosa, M., Feyereisl, J., Collective, T.G.: A Framework for Searching for General Artificial Intelligence. GoodAI, Prague, Czech Republic, Technical report (2016)
Steunebrink, B.R., Thórisson, K.R., Schmidhuber, J.: Growing recursive self-improvers. In: Steunebrink, B., Wang, P., Goertzel, B. (eds.) AGI -2016. LNCS, vol. 9782, pp. 129–139. Springer, Cham (2016). doi:10.1007/978-3-319-41649-6_13
Thórisson, K.R., Bieger, J., Thorarensen, T., Sigurdardóttir, J.S., Steunebrink, B.R.: Why artificial intelligence needs a task theory. In: Steunebrink, B., Wang, P., Goertzel, B. (eds.) AGI -2016. LNCS (LNAI), vol. 9782, pp. 118–128. Springer, Cham (2016). doi:10.1007/978-3-319-41649-6_12
Vygotsky, L.S.: Interaction between learning and development. In: Cole, M., John-Steiner, V., Scribner, S., Souberman, E. (eds.) Mind in Society: The Development of Higher Psychological Processes, pp. 79–91. Harvard University Press, Cambridge (1978)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Bieger, J., Thórisson, K.R., Steunebrink, B.R. (2017). The Pedagogical Pentagon: A Conceptual Framework for Artificial Pedagogy. In: Everitt, T., Goertzel, B., Potapov, A. (eds) Artificial General Intelligence. AGI 2017. Lecture Notes in Computer Science(), vol 10414. Springer, Cham. https://doi.org/10.1007/978-3-319-63703-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-63703-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63702-0
Online ISBN: 978-3-319-63703-7
eBook Packages: Computer ScienceComputer Science (R0)