Center for Analysis and Design of Intelligent Agents

T-720-ATAI-2020

Learning / Teaching

The Pedagogical Pentagon: A Framework for (Artificial) Pedagogy

What is Needed	We need a universal theory of learning – and in fact of teaching, training, task-environments, and evaluation. Anything short of having complete theories for all of these means that experimentation, exploration, and blind search are the only ways to answer questions about performance, curriculum design, training requirements, etc., from which we can never get more than partial, limited answers.
That Said…	The Pedagogical Pentagon captures the five pillars of education: Learning, Teaching, Training, Environments, and Testing. It's not a theory, but rather, a conceptual framework for capturing all key aspects of education.

The Pedagogical Pentagon (left) captures the five main pillars of any learning/teaching situation. The relationships between its contents can be seen from various perspectives: (a) As information flow between processes. (b) As relations between systems. (c ) As dependencies between (largely missing!) theories. REF
Tasks	Learning systems adjust their knowledge as a result of interactions with a task- environment. Defined by (possibly a variety of) objective functions, as well as (possibly) instructions (i.e. knowledge provided at the start of the task, e.g. as a “seed”, or continuously or intermittently throughout its duration). Since tasks can only be defined w.r.t. some environment, we often refer to the combination of a task and its environment as a single unit: the task-environment.
Teacher	The goal of the teacher is to help a learner learn. This is done by influencing the learner’s task-environment in such a way that progress towards the learning goals is facilitated. Teaching, as opposed to training, typically involves information about the What, Why & How: - What to pay attention to. - Relationships between observables (causal, part-whole, etc.). - Sub-goals, negative goals and their relationships (strategy). - Background-foreground separation.
Environment & Task	The learner and the teacher each interact with their own view of the world (i.e. their own “environments”) which are typically different, but overlapping to some degree.
Training	Viewed from a teacher’s and intentional learner’s point of view, “training” means the actions taken (repeatedly) over time with the goal of becoming better at some task, by avoiding learning erroneous skills/things and avoid forgetting or unlearning desirable skills/things.
Test	Testing - or evaluation - is meant to obtain information about the structural, epistemic and emergent properties of learners, as they progress on a learning task. Testing can be done for different purposes: e.g. to ensure that a learner has good-enough performance on a range of tasks, to identify strengths and weaknesses for an AI designer to improve or an adversary to exploit, or to ensure that a learner has understood a certain concept so that we can trust it will use it correctly in the future.
Source	The Pedagogical Pentagon: A Conceptual Framework for Artificial Pedagogy by Bieger et al.

Artificial Pedagogy

What it is	The science about how to teach artificial learners. - Focus on teaching rather than learning. - Aimed at full spectrum of learning system. - Emphasis towards AGI-aspiring systems.
Key Question	Given information about a learner, teaching goal and constraints - What is the best way to teach? - What teaching methods are there, and when are they applicable? - How can we evaluate the learner and the teacher?
State of the Art	No good scientific theory of teaching exists.
Why Artificial Pedagogy?	- Current machine teaching is ad hoc. - Sophisticated teaching needed in complex domains. - Sufficiently advanced learners now exist. - Relevance will increase as AI field advances.
Programming vs. Teaching	Programming: – minimal seed knowledge required – precise Teaching: – natural – adaptive – on-the-fly – can’t program everything
Teaching Methods	- Heuristic Rewarding - Decomposition - Simplification - Situation Selection - Teleoperation - Demonstration - Coaching - Explanation - Cooperation - Socratic method

Artificial Pedagogy Tutoring Methods

Heuristic Rewards	Giving the learner intermediate feedback about performance Related: Reward shaping, Gamification, Heuristics in e.g. minimax game playing RL example: Different reward for positive/negative step
Decomposition	Decomposition of whole, complex tasks into smaller components Related: Whole-task vs. part-task training, Curriculum learning, (Catastrophic interference), (Transferlearning), (Multitask learning). RL example: Sliding puzzle at goal location on grid.
Situation Selection	Selecting situations (or data) for the learner to focs on, e.g. simpler or more difficult situations. Related: Boosting, ML application development, big data, active learning. RL Example: Start (or stop) in problematic states.
Teleoperation	Temporarily taking control of the learner’s actions so they can experience them. Applications: Tennis/golf/chess, Robot ping-pong, artificial tutor. RL Example: Force good or random moves.
Demonstration	Showing the learner how to accomplish a task. Requirements: Desire to imitate, ability to map tutor's actions onto own actions, generalization ability. Related: Apprenticeship learning, inverse reinforcement learning, imitation learning. RL Example: Nonexistent.
Coaching	Giving the learner instructions of what action to take during the task. Requirements: Ability to map language-based instruction onto actions, generalization ability. Related: Supervised learning. RL Example: Add input that specifies correct output.
Explanation	Explaining to the learner how to approach certain situations before the starts (a new instance of) the task. Requirements: Language capability, generalization ability. Related: Imperative programming, analogies. RL Example: Nonexistent.
Cooperation	Doing a task together with the learner to facilitate other tutoring techniques.
Socratic Method	Asking questions to encourage critical thinking and guide the learner towards its own conclusions. Related: Shaping, chaining. RL Example: Nonexistent. NARS Example: > <dog –> mammal>. > «$x –> mammal> –> <$x –> [breaths]». > <{Spike} –> dog>. > <{Spike} –> [breaths]>? main question > <{Spike} –> mammal>? helping question

Table of Contents

T-720-ATAI-2020

Learning / Teaching

The Pedagogical Pentagon: A Framework for (Artificial) Pedagogy

Artificial Pedagogy

Artificial Pedagogy Tutoring Methods