A C OMPUTATIONAL M ODEL OF A CCELERATED F UTURE L EARNING THROUGH F EATURE R ECOGNITION Nan Li...
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Transcript of A C OMPUTATIONAL M ODEL OF A CCELERATED F UTURE L EARNING THROUGH F EATURE R ECOGNITION Nan Li...
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.