Cognitive Model Comparisons:

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23 Φεβ 2014 (πριν από 3 χρόνια και 4 μήνες)

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Cognitive Model Comparisons:

The Road to Artificial General Intelligence?

Christian Lebiere (
cl@cmu.edu
)

Cleotilde Gonzalez (
coty@cmu.edu
)

Carnegie Mellon University

Walter Warwick (
wwarwick@alionscience.com
)

Alion Science & Technology

Challenges in AI & Cognitive Science


Both fields have similar history of challenge problems
despite compatible ends but different means


Artificial Intelligence:
maximize task performance


Started with ambitious but poorly defined test (Turing Test)


Evolved narrow, precise, overspecialized challenges (Chess)


Recently attempted broader tests (Robocup, Grand Challenge)


Cognitive Science:
fit human capabilities (design guide)


Started with ambitious, ill
-
defined capacities list (Newell Test)


Organized a series of complex task comparisons (AMBR, HEM)


Is taking on broader but integrated challenges (DSF?)




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Artificial General Intelligence Conference

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Cognitive Challenge Pitfalls


Challenge is fundamentally about the task, not cognition


Too much task analysis and KE, too little cognitive theory


Task is too narrow; too much data available


Reduces to data fitting


favors parameterization over
principle


Task is too specialized (typical cognitive psychology)


Single cognitive aspect


misses generality, integration


Lack of common simulation environment


Each framework/theory only tackles what they do well


Lack of comparable human data


Emphasizes functionality


loses cognitive constraints

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Artificial General Intelligence Conference

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Desirable Challenge Attributes


Lightweight


Limit integration overhead and task analysis/knowledge eng.


Fast


Rapid model development and collection of monte carlo runs


Open
-
ended and dynamic


Less parameterization, generalization to emergent behavior


Simple and tractable


Direct relation from cognitive mechanisms to behavioral data


Integrated


Toward integrated agent capturing architectural interactions

3/7/09

Artificial General Intelligence Conference

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DSF Challenge Comparison


Dynamic Stocks and Flows


Instance of Dynamic Decision Making


Control a dynamic system given unexpected environmental fluctuations


Simple version of real
-
world situations (financial, ecological, technical,
game)


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Artificial General Intelligence Conference

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Integrated
tasks


Anticipate events


Control system


Cognitive
functions


Sequence learning
-

PC


Action selection
-

BG


Implementation


VB on Windows


Text socket protocol

Generalization Scenarios


Humans
learn
to control system over time for simple functions


Highly variable but
quantifiable
performance over learning process


Complexity of task
scalable
along a number of cognitive dimensions


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Artificial General Intelligence Conference

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Environmental i/o


Complex sequences


Stochastic noise


Multiple variables


System dynamics


Feedback delay


Non
-
linear effects


Real
-
time control


Multi
-
agent system


Other controllers


Payoff manipulations

DSF Comparison Schedule


Official announcement expected March 15


Task environment with socket connection for model,
data and documentation available on web site


Symposium April 1
st

at BRIMS conference (Sundance)


Model submission by May 15


Best entries invited to symposium at European
cognitive modeling conference (travel supported)


Email
DSFChallenge@gmail.com

to be added to
distribution list for official announcements/updates

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Artificial General Intelligence Conference

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