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Why BICA is Necessary for AGI
Alexei Samsonovich (George Mason University)
Biologically Inspired Cognitive Architecture
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Because we need a human-like universal learner
One that describes human cognition and learning at a higher symbolic level
“Critical mass” includes human-like mental states that can act on each other
Questions Answers
Why BICA is necessary for achieving AGI?
What kind of a BICA?
What are the minimal starting requirements, i.e., the “critical mass”?
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Mental states in GMU-BICA
A mental state in GMU-BICA includes:
Contents of awareness represented by schemas
A token representing an instance of the Self who is aware (labeled I-Now, I-Next, etc.)
Working memory: Active mental states of the Self
I-Imagine:
•Intermediate goal situation
I-Imagine:
•Intermediate goal situation
I-Goal:
•Stimulus satisfaction
I-Goal:
•Stimulus satisfaction
I-Next:
•Scheduled action
•Expectation
I-Next:
•Scheduled action
•Expectation
I-Previous:
•Ideas
•Visual input
I-Previous:
•Ideas
•Visual input
I-Meta:
•Scenario
•Analysis
I-Meta:
•Scenario
•Analysis
I-Past:
•Past experience
•Prospective memories
I-Past:
•Past experience
•Prospective memories
I-Now:
•Ideas
•Intent
I-Now:
•Ideas
•Intent
Episodic memory: Frozen mental states of the Self
I-Past-1I-Past-1
I-Past-2I-Past-2
I-Past-3I-Past-3
I-Past-4I-Past-4
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Mental state dynamics in working memory of GMU-BICA: an example
meI-Now
Working memoryInput-output
Semantic memoryS
PQ
S
S
he
me'
he'
me
I-NextS
he
me
He-NowS
me'
me
He-Next
S R
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Examples of types of mental states in GMU-BICA (a possible snapshot of working memory)
I-NowI-PreviousI-Next
I-Imagined-1
I-GoalI-Imagined-2
I-Meta-1
I-Past
I-Detail-2I-Feel
He-NowShe-Past He-Now-I-Now
I-Subgoal
I-False-Belief
I-Next-Next
I-Past-Revised
I-Meta-2
I-Detail-1
I-Imagined-3
She-Past-Prev
I-Alt-Goal
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Self-regulated learning (SRL) model of problem solving
(based on Zimmerman & Kitsantas, 2006)
“…there is a need to build a unified model of meta-cognition and self-regulated learning that incorporates key aspects of existing models, assumptions, processes, mechanisms, and phases”
(Azevedo and Witherspoon, AAAI BICA-2008)
Object
Level
Meta-
Level
Ground
Level
Doing Reasoning Metareasoning
ActionSelection Control
Perception Monitoring
Model of meta-cognition(Cox & Raja, 2007)
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Result: A Mental-state model of SRL
Forethought
Task analysis Self-beliefs
Performance
Self-control Self-observation
Reflection
Self-judgmentSelf-reactions
Task analysis
Identify goalSelect strategic steps (a plan)
Self-beliefs
Self-efficacyGoal-orientationIntrinsic interest
Self-observation
Self-recording using a worksheet
Self-control
Enact selected steps to solve the problem
Self-evaluation
Compare result to the standard (a template)
Homework task
Problem: ax+b = cGoal: Solve for x, i.e., have a formula x=…
Select strategic steps
Isolate x - use subtraction property - use division property
Enact strategic steps
ax+b = c | -bax = c-b | /ax = (c-b)/a
Result validation
x=(c-b)/a compare tox = …(no x in r.h.s.)There is a match.
Self-reaction
Met standardSkill masteredSelf-reward(Exit) -- OR –Did not meet standard Attribute failure to ineffective strategy selection(Loop reentry)
I-Now
I-Meta
I-Detail-
1
I-NextI-Next-Next
I-Detail-2
I-Meta-Next
I-Goal
HW Problem:Solve for x: ax+b=c
(Samsonovich, De Jong & Kitsantas, to appear in International Journal of Machine Consciousness, 1, June 2009)
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- How to build a universal learner?- Need to bootstrap from “critical mass” ( )- How to build a “critical mass” (suppose we know what)?
2. Brittle rapid prototype-demo
1. Incremental bottom-up engineering
3. SRL assistant (finessing lower levels by students!)
Thank you.
Without a good stimulus will take forever
Useless toy(BICA Phase I)
Feasible and practically useful stepping stone
There are at least three approaches to building a “critical mass”:
Watch for AAAI 2009 Fall Symposia (BICA, SRL-metacog)
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Introducing two notions of a semantic cognitive map (SCM): “Strong” SCM with a
dissimilarity metric
A is closer to B than C A is more similar to B than C
“Weak” SCM that captures both synonym and antonym relationsA CB
A and B are synonyms, A and C are antonyms. Don’t care about unrelated.
AB C
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Background: Method of building an SCM
1. Represent symbols (words, documents, etc.) as vectors in Rn
2. Optimize vector coordinates to minimize H
3. Do truncated SVD of the resultant distribution
cSS
QQAS
QAS
QSA
yxyxHd
xxyxyxHc
xxyyxHb
xxyxyHa
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2422
42
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exp)(
)(
)(
)(
dot product
x, y Q – vectors in Rn
A – antonym pairsS – synonym pairs
(Samsonovich & Ascoli, Proceedings of AGI-2007)
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Example: color map Sample N = 10,000 points on
a sphere (A) declare some pairs of points
‘synonyms’ (some of those that are close to each other)
declare some other pairs of points ‘antonyms’ (some of those that are separated far apart)
assign random coordinates to points in 10-dimensional space (B)
apply an optimization procedure to the set of 10,000 random vectors in order to minimize the following energy function:
The result is the reconstructed spatial distribution of colors (C)
A
B
C
xSxyAxy
xxyxyH 4
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Geometric properties of the reconstructed color map are robust with respect to variation of model parameters
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Semantic characteristics of the SCM
synonyms
antonyms
Synonym pairs and antonym pairs, if mixed together, can be separated with 99% accuracy based on the angle between vectors:
acute synonyms, obtuse antonyms
Semantics of the first 3 dimensions are more general than any words, yet clearly identifiable:
PC#1: success, positive, clear, makes good sense
PC#2: exciting, does not go easy
PC#3: beginning, source, origin, release, liberation, exposure
*
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Sentiment analysis: 7 utterances automatically allocated on SCM
1. Please, chill out and be quiet. I am bored and want you to relax. Sit back and listen to me.
2. Excuse me, sorry, but I cannot follow you and am falling asleep. Can we pause? I've got tired and need a break.
3. I hate you, stupid idiot! You irritate me! Get disappeared, or I will hit you!
4. What you are telling me is terrible. I am very upset and curious: what's next?
5. Wow, this is really exciting! You are very smart and brilliant, aren't you?
6. I like very much every word that you say. Please, please, continue. I feel like I am falling in love with you.
7. We have finally found the solution. It looks easy after we found it. I feel completely satisfied and free to go home.
(Samsonovich & Ascoli, in Proc. of AAAI 2008 Workshop on Preference Handling)
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Acquired 40+ reviews for each of three movies: Iron Man, Superhero and Prom Night, from the site www.mrqe.com
For each review, computed the average map coordinate of all identified indexed words and phrases.
RESULT: Statistics for PC#1 are consistent with grades given to the movies in the reviews.
Iron Man: (1.95, 0.52), Superhero: (1.49, 0.36), Prom Night: (1.17, 0.42)All differences are significant except PC#2 of Superhero vs. Prom Night
Sentiment analysis:Mapping movie reviews as ‘bags of words’
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Weak SCM is low-dimensional, yet distinguishes almost all synonym-antonym pairs
SCM dimensions have clearly identifiable semantics that make sense virtually in all domains of knowledge
The map semantics and geometrical characteristics are consistent across corpora and across languages
Therefore, SCM can be used as a metric system for semantics (at least for the most general part of semantics)
SCM can be used to guide the process of thinking in symbolic cognitive architectures
Other potential applications include sentiment analysis, semantic twisting, document search, validation of translation
CONCLUSIONS
Thank you.Credits to Giorgio A. Ascoli, Rebecca F. Goldin, Thomas T. Sheehan