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A COMPUTATIONAL MODEL OF ACCELERATED FUTURE LEARNING THROUGH FEATURE RECOGNITIONNan Li

Computer Science Department

Carnegie 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

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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.