A COMPUTATIONAL MODEL OF ACCELERATED FUTURE LEARNING THROUGH FEATURE RECOGNITIONNan LiComputer Science DepartmentCarnegie Mellon University

Building an intelligent agent that simulates human-level learning using machine learning techniques

ACCELERATED FUTURE LEARNING Accelerated Future Learning

Learning more effectively because of prior learning

Has been observed a lot How?

Expert vs Novice Expert Deep functional feature (e.g. -3x -3) Novice Shallow perceptual feature (e.g. -3x 3)

A COMPUTATIONAL MODEL Model Accelerated Future Learning Use Machine Learning Techniques Acquire Deep Feature Integrated into a Machine-Learning Agent

AN EXAMPLE IN ALGEBRA

FEATURE RECOGNITION ASPCFG INDUCTION Under lying structure in the problem

Grammar Feature Intermediate symbol in a grammar

rule Feature learning task Grammar induction Error Incorrect parsing

PROBLEM STATEMENT Input is a set of feature recognition records

consisting of An original problem (e.g. -3x) The feature to be recognized (e.g. -3 in -3x)

Output A PCFG An intermediate symbol in a grammar rule

ACCELERATED FUTURE LEARNING THROUGH FEATURE RECOGNITION Extended a PCFG Learning Algorithm (Li et

al., 2009) Feature Learning Stronger Prior Knowledge:

Transfer Learning Using Prior Knowledge Better Learning Strategy:

Effective Learning Using Bracketing Constraint

A TWO-STEP ALGORITHM• Greedy Structure

Hypothesizer: Hypothesizes the

schema structure

• Viterbi Training Phase: Refines schema

probabilities Removes redundant

schemas

Generalizes Inside-Outside Algorithm (Lary & Young, 1990)

GREEDY STRUCTURE HYPOTHESIZER Structure learning Bottom-up Prefer recursive to non-recursive

EM PHASE Step One:

Plan parse tree computation

Most probable parse tree

Step Two: Selection

probabilities update s: ai p, aj ak

FEATURE LEARNING Build Most Probable

Parse Trees For all observation

sequences Select an

Intermediate Symbol that Matches the most

training records as the target feature

TRANSFER LEARNING USING PRIOR KNOWLEDGE GSH Phase:

Build parse trees based on previously acquired grammar

Then call the original GSH

Viterbi Training: Add rule frequency

in previous task to the current task

0.66

0.330.50.5

EFFECTIVE LEARNING USING BRACKETING CONSTRAINT Force to generate a

feature symbol Learn a subgrammar

for feature Learn a grammar for

whole trace Combine two

grammars

EXPERIMENT DESIGN IN ALGEBRA

EXPERIMENT RESULT IN ALGEBRA

Fig.2. Curriculum one Fig.3. Curriculum two Fig.4. Curriculum three

Both stronger prior knowledge and a better learning strategy can yield accelerated future learning

Strong prior knowledge produces faster learning outcomes L00 generated human-like errors

LEARNING SPEED INSYNTHETIC DOMAINS

Both stronger prior knowledge and a better learning strategy yield faster learning

Strong prior knowledge produces faster learning outcomes with small amount of training data, but not with large amount of data

Learning with subtask transfer shows larger difference, 1) training process; 2) low level symbols

SCORE WITH INCREASING DOMAIN SIZES

The base learner, L00, shows the fastest drop Average time spent per training record

Less than 1 millisecond except for L10 (266 milliseconds) L10: Need to maintain previous knowledge, does not separate trace

into small traces Conciseness: Transfer learning doubled the size of the schema.

INTEGRATING ACCELERATED FUTURE LEARNING IN SIMSTUDENT

Tutor LuckyNext Problem Quiz Lucky

Prepare Lucky for Quiz Level 3 !

Curriculum Browser

Level 1:[+] One-Step Linear Equation

Level 2:[+] Two-Step Linear Equation

Level 3:[-] Equation with Similar Terms

OverviewIn this unit, you will solve equations with integer or decimal coefficients, as well as equations involving more than one variable.

More…

Lucky

x+5

• A machine-learning agent that• Acquires

production rules from

• Examples and problem solving experience

• Integrate the acquired grammar into production rules Requires weak

operators (non-domain specific knowledge)

Less number of operators

CONCLUDING REMARKS Presented a computational model of human

learning that yields accelerated future learning.

Showed Both stronger prior knowledge and a better

learning strategy improve learning efficiency. Stronger prior knowledge produced faster

learning outcomes than a better learning strategy.

Some model generated human-like errors, while others did no make any mistake.