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The Selection Space. John E. Laird 32 nd Soar Workshop. Overview One-step Look-ahead Using Selection Problem Space. Tie Impasse. A. (on A Table) (on B Table) (on C A). move(C, B). B. Prefer move(C, Table). C. move(C, Table). Goal. C. move(B, C). A. B. Evaluation = 1.

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the selection space

The Selection Space

John E. Laird

32nd Soar Workshop

overview one step look ahead using selection problem space
Overview One-step Look-ahead Using Selection Problem Space

Tie Impasse

A

(on A Table)

(on B Table)

(on C A)

move(C, B)

B

Prefer move(C, Table)

C

move(C, Table)

Goal

C

move(B, C)

A

B

Evaluation = 1

Evaluation = 0

Evaluation = 0

Evaluate-operator(move(C, Table))

Evaluate-operator(move(C, B))

Evaluate-operator(move(B,C))

copy

Evaluation = 1

(on A Table)

(on B Table)

(on C Table)

(on A Table)

(on B Table)

(on C A)

move(C, Table)

C

A

B

selection space
Selection Space
  • Important state structures created by Soar
    • ^impasse tie, ^item 01 02 …
  • Evaluate-operator
    • Instantiated with every item (every tied operator) that has not been evaluated

(<s> ^operator <o>)

(<o> ^name evaluate-operator

^superoperator <so>)

    • Usually randomly select between them (some exceptions)
    • Create ^evaluation structure on selection state
evaluate state structure
Evaluate State Structure
  • When evaluate-operator is selected, create:
    • (<s> ^evaluation <e>)
    • (<e> ^superoperator <i>)
    • (<o> ^evaluation <e> # on evaluate-operator
    • ^superstate <ss> # task state
    • ^superproblem space <ps>)
  • Evaluate-operator terminates when a value is created on the associate evaluation
    • (<e> ^value true)
evaluate operator substate
Evaluate-operator Substate
  • Create a copy of the task state
    • Includes ^name, ^desired
    • ^problem-space determines how to create copy
      • Many flags to control what to copy and how deep
      • ^default-state-copy yes is default
  • If don’t create copy, original state will change
evaluate operator processing
Evaluate-operator Processing
  • Force selection of a copy of the operator being evaluated
  • Operator application rule should fire and generate new state
    • Requires action model: operator application rule for simulating operator
    • If doesn’t, will eventually get impasses that lead to a failed evaluation.
  • If there is state evaluation knowledge, it adds augmentation to state
    • ^numeric-value, ^symbolic-value, ^expected-value
    • Copied up to the evaluation structure in the selection space
    • Leads to evaluate-operator terminating
  • By default, elaboration rules aggressively convert evaluations to preferences.
    • Evaluates only as many operators as necessary to generate preferences to break the tie.
  • Chunks are learned for computing evaluations and preferences
overview one step look ahead using selection problem space1
Overview One-step Look-ahead Using Selection Problem Space

Tie Impasse

A

(on A Table)

(on B Table)

(on C A)

move(C, B)

B

move(C, Table) > move(C, B)

move(C, Table) > move(B, C)

move(B, C) = move(C, B)

C

move(C, Table)

Goal

C

move(B, C)

A

B

Evaluation = 1

Evaluation = 0

Evaluation = 0

Evaluate-operator(move(C, Table))

Evaluate-operator(move(C, B))

Evaluate-operator(move(B,C))

copy

Evaluation = 1

(on A Table)

(on B Table)

(on C Table)

(on A Table)

(on B Table)

(on C A)

move(C, Table)

C

A

B

requirements to use selection space
Requirements to Use Selection Space
  • Source in selection.soar!
    • Explains the following requirements
  • Have a ^problem-space structure on the state
  • Have a ^desired structure on the state
  • Include rules that compute failure/success/evaluation.
  • Have rules that simulate action of operators
    • This is an action model
    • Only apply when in state with

^name evaluate-operator

depth first search in soar
Depth-First Search in Soar
  • If no evaluation of the state, continues in substate
    • If sufficient knowledge, selects and applies operator
    • If insufficient knowledge, get a tie impasse and recursively get depth-first search.
  • The state “open” list is represented as the stack of substates.
  • Elaboration rules pass success up the stack to avoid extra search.
  • No guarantee of finding shortest path.
  • Chunking is necessary to avoid repeated search.
overview one step look ahead using selection problem space2
Overview One-step Look-ahead Using Selection Problem Space

Tie Impasse

Tie Impasse

A

(on A Table)

(on B Table)

(on C A)

move(C, B)

move(C, B)

B

C

move(C, Table)

move(A, B)

Goal

C

move(B, C)

move(B, C)

A

B

Evaluate-operator(move(C, Table))

copy

move(C, Table)

(on A Table)

(on B Table)

(on C Table)

(on A Table)

(on B Table)

(on C A)

C

A

B

iterative deepening
Iterative Deepening
  • Include an evaluation-depth in the selection space
  • Evaluate all of the task operators to that depth
    • Start with depth = 1
    • In each recursive selection substate, decrement depth
  • Terminate if achieve goal
  • Increment depth when all task operators have been evaluated
deep search in soar iterative a
Deep Search in Soar: Iterative A*
  • Assumes task state structure
    • Graph structure of ^waypoints, with a ^current-location
  • Every evaluation maintains
    • Path-cost: g(x)
    • Estimated-cost: h(x)
    • Total-estimated-cost: f(x) = g(x) + h(x)
  • Prefer an evaluate-operator to another
    • If it doesn’t have an estimated-cost # get initial values
    • If its total-estimated-cost is less than the others # pursue best
  • Final-cost for an operator is when estimated cost is 0
  • Create a preference if final-cost(o1) < total-estimated-cost(o2)
  • Complex rules and operators combine estimates from substates
    • Add operators: compute-evaluations, compare-evaluations, compute-best-total-estimate
slide13

2: Path: 1.4; Estimated : 2.3; Total 3.7

1

3, 5

1.4

2

4

3

2, 4

3, 4

4, 4

2.3

6

5

4,3

3, 4

slide14

2: Path: 1.4; Estimated : 2.3; Total 3.7

3: Path: 1; Estimated: 1.4; Total 2.4

1

3, 5

1.0

2

4

3

4, 4

2, 4

3, 4

1.4

6

5

4,3

3, 4

slide15

2: Path: 1.4; Estimated : 2.3; Total 3.7

3: Path: 1; Estimated: 1.4; Total 2.4

4: Path: 1.4; Estimated: 1.0; Total 2.4

1

3, 5

1.4

2

4

3

4, 4

2, 4

3, 4

1.0

6

5

4,3

3, 4

slide16

2: Path: 1.4; Estimated : 2.3; Total 3.7

3: Path: 1; Estimated: 1.4; Total 2.4

4: Path: 1.4; Estimated: 1.0; Total 2.4

4: Path: 2.8; Estimated: 1.0 Total 3.8

1

3, 5

1.4

2

4

3

4, 4

2, 4

3, 4

1.4

6

5

4,3

3, 4

1.0

slide17

2: Path: 1.4; Estimated : 2.3; Total 3.7

3: Path: 1; Estimated: 1.4; Total 2.4

4: Path: 2.8; Estimated: 1.0 Total 3.8

3: Path: 3.3; Estimated: 1.0 Total 4.3

1

3, 5

1.0

2

4

3

4, 4

2, 4

3, 4

2.3

6

5

4,3

3, 4

1.0

slide18

2: Path: 1.4; Estimated : 2.3; Total 3.7

4: Path: 2.8; Estimated: 1.0 Total 3.8

3: Path: 3.3; Estimated: 1.0 Total 4.3

2: Path 3.7; Estimated: 0.0 Final: 3.7

1

3, 5

1.4

2

4

3

4, 4

2, 4

3, 4

2.3

2.3

6

5

4,3

3, 4

nuggets and coal
Nuggets and Coal
  • Nuggets:
    • Provides task-independent knowledge for controlling deliberate operator evaluation
    • Plays well with chunking
  • Coal
    • Requires some knowledge of conventions
    • More advanced methods are pretty complex