CHS-Soar - AGI conferences
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Transcript CHS-Soar - AGI conferences
Introducing Constrained Heuristic Search
to the Soar Cognitive Architecture
(demonstrating domain independent learning in Soar)
The Second Conference on Artificial General Intelligence, AGI-09
Sean A. Bittle
Mark S. Fox
March 7th, 2009
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The Problem
General problem solving and learning are central goals of AI research
on cognitive architectures
However, there are few examples of domain independent learning in
cognitive architectures
The Goal
Demonstrate Soar can learn and apply domain independent knowledge
But to achieve this goal we need to augment the Soar’s problem-solving
paradigm (CHS-Soar)
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Soar Cognitive Architecture
Developed by Newell, Laird and Rosenbloom at
CMU, 1983
Symbolic Cognitive Architecture where all long
term knowledge is encoded as productions
rules.
Based on the hypothesis that all goal-oriented
behavior can be cast as the selection and
application of operators to a state in a problem
space
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Constrained Heuristic Search (CHS)
Developed by Fox, Sadeh and Bayken, 1989
CHS is a problem solving approach that combination of constraint satisfaction
and heuristic search where the definition of the problem space is refined to
include:
Problem Topology
Problem Textures
Problem Objective
Constraint Graph
Va
Ci
Vc
Cii
Vb
CP/CHS allows us to employ a generalized problem representation
(CSP) and utilize generic, yet powerful problem solving techniques
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CHS-Soar
“What
are we trying to learn?”
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CHS-Soar
What are “Texture Measures?”
Textures are
measures of the
problem topology
•Minimum Remaining Values (MRV) – variable selection
•Degree (DEG) – variable selection
•Least Constraining Value (LCV) – value selection
Constraint Graph
External Agent
Problem
Data
Var
Dom
Actual
MRV
Soar Agent
Normalized
DEG
MRV
DEG
Pruned
Num
MRV
DEG
WA
R,G,B 3
2
0.50
0.40
1
0.50
0.00
NT
R,G,B 3
3
0.50
0.60
2
-
0.40
SA
R,G,B 3
5
0.50
1.00
3
-
0.60
Q
R,G,B 3
3
0.50
0.60
4
-
1.00
NSW
R,G,B 3
3
0.50
0.60
V
R,G,B 3
2
0.50
0.40
T
R,G,B 3
0
0.50
0.00
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CHS-Soar
“How Do We Select a “Good” Texture Measure?”
MRV
DEG
DEG
DEG
DEG
0.50
0.00
0.40
0.60
1.00
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CHS-Soar
“What Do We Learn...Again?”
“Chunk” decoupled
from problem type
Traditional Soar Agent Chunks tend
to include domain specific
knowledge
Standard Soar Chunk (Water Jugs)
sp {chunk-54*d150*tie*2
:chunk
(state <s1> ^name water-jug ^operator <o1> +
^problem-space <p1>
^desired <d1> ^jug <j1> ^jug <j2>)
(<o1> ^name fill ^jug <j1>)
(<p1> ^name water-jug)
(<j1> ^contents 0 ^volume 3)
(<j2> ^contents 0 ^volume 5)
(<d1> ^jug <j3>)
(<j3> ^contents 1 ^volume 3)
-->
(<s1> ^operator <o1> >) }
Hyper-heuristics:
heuristics to choose heuristic
measures
CHS-Soar Chunk
sp {chunk-128*d351*tie*2
:chunk
(state <s1> ^phase |SelectVariableTexture|
^top-state <s1> ^name |CHS-Soar|
^operator <o1> + ^operator { <o2> <> <o1> } +
^problem-space <p1> ^desired <d1>)
(<o1> ^texture |DEG| ^value 0.66 ^name |VariableTexture|)
(<o2> ^texture |MRV| ^value 1.
^name |VariableTexture|)
(<p1> ^name |CHS-Soar|)
-->
(<s1> ^operator <o1> > <o2>) }
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Experiments
Three experiments conducted to investigate:
1.
2.
3.
Integration of rule and constraint based reasoning
Domain Independent Learning
Scalability of externally learned chunks
Problem types being considered:
•
•
•
•
•
•
•
Job Shop Scheduling (JSS)
Map Colouring
Radio Frequency Assignment Problem (RFAP)
N-Queens, Sudoku, Latin Square
Towers of Hanoi, Water Jugs
Configuration Problems
Random CSPs
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Results: Domain Independent Learning
Decisions
100
Map
Colouring
(n = 11)
Benchmark
Internal
External
90
80
70
Benchmark (HardCoded)
MapCol (n = 11)
JSS (n = 15)
RFAP (n = 200)
NQueens (n = 16)
75
Decisions
Job Shop
Scheduling
(n = 15)
65
55
45
Benchmark (HardCoded)
JSS (n = 15)
MapCol (n = 11)
RFAP (n = 200)
NQueens (n = 16)
Benchmark (HardCoded)
RFAP (n = 200)
MapCol (n = 11)
JSS (n = 15)
NQueens (n = 16)
RFAP
(n = 200)
Decisions
3700
3600
3500
3400
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Conclusions
Demonstrated integration of rule and constraint based reasoning
Demonstrated the ability to learn rules while solving one problem type that
can be successfully applied in solving another problem type
Demonstrated ability to discover, learn and use multi-textured “hyper-heuristics”
leading to improved solutions over traditional unary heuristics
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