Means-end Relations and a Measure of Efficacy Jesse Hughes 1 Albert - - PowerPoint PPT Presentation

means end relations and a measure of efficacy
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Means-end Relations and a Measure of Efficacy Jesse Hughes 1 Albert - - PowerPoint PPT Presentation

Means-end relations Efficacy via fuzzy logic Means-end Relations and a Measure of Efficacy Jesse Hughes 1 Albert Esterline 2 Bahram Kimiaghalam 2 1 Technical University of Eindhoven 2 North Carolina A&T July 4, 2005 Hughes, Esterline,


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SLIDE 1

Means-end relations Efficacy via fuzzy logic

Means-end Relations and a Measure of Efficacy

Jesse Hughes1 Albert Esterline2 Bahram Kimiaghalam2

1Technical University of Eindhoven 2North Carolina A&T

July 4, 2005

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 2

Means-end relations Efficacy via fuzzy logic

Outline

1

Means-end relations Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 3

Means-end relations Efficacy via fuzzy logic

Outline

1

Means-end relations Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

2

Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 4

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Outline

1

Means-end relations Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

2

Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 5

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 6

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results. Typical practical syllogisms include premises:

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 7

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results. Typical practical syllogisms include premises: an assertion that some end ϕ is desirable,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 8

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results. Typical practical syllogisms include premises: an assertion that some end ϕ is desirable, an assertion that (given ψ), the action α is related to ϕ,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 9

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results. Typical practical syllogisms include premises: an assertion that some end ϕ is desirable, an assertion that (given ψ), the action α is related to ϕ, an assertion that ψ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 10

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results. Typical practical syllogisms include premises: an assertion that some end ϕ is desirable, an assertion that (given ψ), the action α is related to ϕ, an assertion that ψ. The conclusion is an action or an intention.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 11

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Means-end relations in practical syllogisms

Practical reasoning is concerned with actions to attain desired results. Typical practical syllogisms include premises: an assertion that some end ϕ is desirable, an assertion that (given ψ), the action α is related to ϕ, an assertion that ψ. The conclusion is an action or an intention. This premise is a means-end relation.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 12

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 13

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 14

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable. Therefore I must heat the hut.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 15

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable. Therefore I must heat the hut. Expression of an agent’s desire,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 16

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable. Therefore I must heat the hut. Expression of an agent’s desire, A necessary means-end relation,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 17

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable. Therefore I must heat the hut. Expression of an agent’s desire, A necessary means-end relation, Concludes in a necessary action.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 18

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable. Therefore I must heat the hut. Expression of an agent’s desire, A necessary means-end relation, Concludes in a necessary action. Note: distinct premises

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 19

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. Unless I heat the hut, it will not be habitable. Therefore I must heat the hut. Expression of an agent’s desire, A necessary means-end relation, Concludes in a necessary action. Note: distinct premises But necessary means-end relations are a bit tricky.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 20

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

An example from von Wright

I want to make the hut habitable. If I heat the hut, it will be habitable. Therefore, I have reason to heat the hut. An alternative with a sufficient means-end relation.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 21

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.”

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 22

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.”

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 23

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.” “The subroutine ensures that the user is authorized.”

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 24

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.” “The subroutine ensures that the user is authorized.” “The magician’s assistant is for distracting the audience.”

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 25

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.” “The subroutine ensures that the user is authorized.” “The magician’s assistant is for distracting the audience.” We ascribe functions to biological stuff,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 26

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.” “The subroutine ensures that the user is authorized.” “The magician’s assistant is for distracting the audience.” We ascribe functions to biological stuff, artifacts,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 27

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.” “The subroutine ensures that the user is authorized.” “The magician’s assistant is for distracting the audience.” We ascribe functions to biological stuff, artifacts, algorithms,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 28

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Functional ascriptions

“The function of the heart is to pump blood.” “That switch mutes the television.” “The subroutine ensures that the user is authorized.” “The magician’s assistant is for distracting the audience.” We ascribe functions to biological stuff, artifacts, algorithms, personal roles. . .

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 29

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.”

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 30

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.” ⇓ One can use the switch to mute the television.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 31

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.” ⇓ One can use the switch to mute the television. ⇓ Some action involving the switch will cause the television to be muted.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-32
SLIDE 32

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.” ⇓ One can use the switch to mute the television. ⇓ Some action involving the switch will cause the television to be muted. Functions imply means-end relations.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 33

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.” ⇓ One can use the switch to mute the television. ⇓ Some action involving the switch will cause the television to be muted. Functions imply means-end relations. Doesn’t imply desirability of the end.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 34

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.” ⇓ One can use the switch to mute the television. ⇓ Some action involving the switch will cause the television to be muted. Functions imply means-end relations. Doesn’t imply desirability of the end. Needed: means-end semantics

distinct of desirability

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 35

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

How functions relate to means and ends

“That switch mutes the television.” ⇓ One can use the switch to mute the television. ⇓ Some action involving the switch will cause the television to be muted. Functions imply means-end relations. Doesn’t imply desirability of the end. Needed: means-end semantics

distinct of desirability distinct from theory of practical reasoning

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 36

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Initial analysis of means-end relations

An end is some desirable condition – a proposition.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 37

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Initial analysis of means-end relations

An end is some desirable condition – a proposition. A means is a way of making the end true.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 38

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Initial analysis of means-end relations

An end is some desirable condition – a proposition. A means is a way of making the end true. Means change things: means are actions.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-39
SLIDE 39

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Initial analysis of means-end relations

An end is some desirable condition – a proposition. A means is a way of making the end true. Means change things: means are actions. Some controversies: Ends-in-themselves?

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 40

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Initial analysis of means-end relations

An end is some desirable condition – a proposition. A means is a way of making the end true. Means change things: means are actions. Some controversies: Ends-in-themselves? Objects as means?

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-41
SLIDE 41

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-42
SLIDE 42

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions. Basic types: a set act of actions,

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-43
SLIDE 43

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions. Basic types: a set act of actions,

Closed under:

sequential composition α; β non-deterministic choice α ∪ β test ϕ? iteration α∗

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-44
SLIDE 44

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions. Basic types: a set act of actions,

Closed under:

sequential composition α; β non-deterministic choice α ∪ β test ϕ? iteration α∗

a set prop of propositions.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-45
SLIDE 45

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions. Basic types: a set act of actions,

Closed under:

sequential composition α; β non-deterministic choice α ∪ β test ϕ? iteration α∗

a set prop of propositions.

Closed under:

boolean connectives, dynamic operators [α]ϕ, αϕ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-46
SLIDE 46

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions. Basic types: a set act of actions,

Closed under:

sequential composition α; β non-deterministic choice α ∪ β test ϕ? iteration α∗

a set prop of propositions.

Closed under:

boolean connectives, dynamic operators [α]ϕ, αϕ.

Intuitions: [α]ϕ: after doing α, ϕ will hold.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-47
SLIDE 47

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL syntax

Propositional Dynamic Logic is a logic of actions. Basic types: a set act of actions,

Closed under:

sequential composition α; β non-deterministic choice α ∪ β test ϕ? iteration α∗

a set prop of propositions.

Closed under:

boolean connectives, dynamic operators [α]ϕ, αϕ.

Intuitions: [α]ϕ: after doing α, ϕ will hold. αϕ: after doing α, ϕ might hold.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-48
SLIDE 48

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL semantics

α α α α

Possible world semantics with transition systems for each action α.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-49
SLIDE 49

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL semantics

α α α α

Possible world semantics with transition systems for each action α. w

α

w′ means:

  • ne can reach w′ by doing α in w.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-50
SLIDE 50

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL semantics

P [ α ] P

α α α α

Possible world semantics with transition systems for each action α. w

α

w′ means:

  • ne can reach w′ by doing α in w.

w | = [α]ϕ iff ∀ w

α

w′ . w′ |

= ϕ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-51
SLIDE 51

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

PDL semantics

Q

  • β
  • Q

β β β

Possible world semantics with transition systems for each action α. w

α

w′ means:

  • ne can reach w′ by doing α in w.

w | = [α]ϕ iff ∀ w

α

w′ . w′ |

= ϕ. w | = αϕ iff ∃ w

α

w′ . w′ |

= ϕ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 52

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Weak and strong means-end relations

A means is an action α that can realize one’s end ϕ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-53
SLIDE 53

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Weak and strong means-end relations

A means is an action α that can realize one’s end ϕ. Two interpretations: ϕ

α α

Weak: α might realize ϕ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-54
SLIDE 54

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Weak and strong means-end relations

A means is an action α that can realize one’s end ϕ. Two interpretations: ϕ

α α

ϕ

α α

Weak: α might realize ϕ. Strong: α will realize ϕ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-55
SLIDE 55

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Weak and strong means-end relations

A means is an action α that can realize one’s end ϕ. Two interpretations: ϕ

α α

ϕ

α α

Weak: α might realize ϕ. Strong: α will realize ϕ. w | = αϕ w | = [α]ϕ ∧ α⊤

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-56
SLIDE 56

Means-end relations Efficacy via fuzzy logic Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

Weak and strong means-end relations

A means is an action α that can realize one’s end ϕ. Two interpretations: ϕ

α α

ϕ

α α

Weak: α might realize ϕ. Strong: α will realize ϕ. w | = αϕ w | = [α]ϕ ∧ α⊤ α can be done.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-57
SLIDE 57

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Outline

1

Means-end relations Interest I: Practical syllogisms Interest II: Functional ascriptions Propositional Dynamic Logic

2

Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-58
SLIDE 58

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Means distinguished by efficacy

Different means to a common end have different degrees of reliability.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-59
SLIDE 59

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Means distinguished by efficacy

Different means to a common end have different degrees of reliability. End: Get 12 points with one dart.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-60
SLIDE 60

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Means distinguished by efficacy

Different means to a common end have different degrees of reliability. End: Get 12 points with one dart. Three different means: Throw for 12.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-61
SLIDE 61

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Means distinguished by efficacy

Different means to a common end have different degrees of reliability. End: Get 12 points with one dart. Three different means: Throw for 12. Throw for double 6.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-62
SLIDE 62

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Means distinguished by efficacy

Different means to a common end have different degrees of reliability. End: Get 12 points with one dart. Three different means: Throw for 12. Throw for double 6. Throw for triple 4.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-63
SLIDE 63

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Means distinguished by efficacy

Different means to a common end have different degrees of reliability. End: Get 12 points with one dart. Three different means: Throw for 12. Throw for double 6. Throw for triple 4. Efficacy: The degree of reliability of a means to an end.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-64
SLIDE 64

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α α β β

Efficacy is a measure of likelihoods.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-65
SLIDE 65

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α α β β

Efficacy is a measure of likelihoods. PDL includes non-determinism, not probabilities.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-66
SLIDE 66

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Efficacy is a measure of likelihoods. PDL includes non-determinism, not probabilities. Fix (semantic): use probabilistic transition structures.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-67
SLIDE 67

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Efficacy is a measure of likelihoods. PDL includes non-determinism, not probabilities. Fix (semantic): use probabilistic transition structures. w

α x

w′ means that

doing α in w has probability x

  • f resulting in w′.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-68
SLIDE 68

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Efficacy is a measure of likelihoods. PDL includes non-determinism, not probabilities. Fix (semantic): use probabilistic transition structures. w

α x

w′ means that

doing α in w has probability x

  • f resulting in w′.

Write: P( w

α

w′ ) = x.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-69
SLIDE 69

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix?

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-70
SLIDE 70

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-71
SLIDE 71

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-72
SLIDE 72

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x. Nesting requires picking x’s.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-73
SLIDE 73

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x. Nesting requires picking x’s.

Probabilistic PDL?

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-74
SLIDE 74

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x. Nesting requires picking x’s.

Probabilistic PDL?

Truth functional.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-75
SLIDE 75

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x. Nesting requires picking x’s.

Probabilistic PDL?

Truth functional. Assigns values in [0, 1] to world-formula pairs.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-76
SLIDE 76

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x. Nesting requires picking x’s.

Probabilistic PDL?

Truth functional. Assigns values in [0, 1] to world-formula pairs. Logic in loose sense.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-77
SLIDE 77

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

From non-determinism to probabilities

Q

α , . 2 α 0.8 β , . 9 β 0.1

Syntactic fix? Probabilistic Computation Tree Logic (pCTL)?

Index dynamic operators, like [α]≥x, α≥x. Nesting requires picking x’s.

Probabilistic PDL?

Truth functional. Assigns values in [0, 1] to world-formula pairs. Logic in loose sense.

Fuzzy PDL.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 78

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

But probability = fuzziness. . .

Slogan: Probabilities and fuzziness are different.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 79

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

But probability = fuzziness. . .

Slogan: Probabilities and fuzziness are different. But one can use probabilities to define fuzzy predicates.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 80

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

But probability = fuzziness. . .

Slogan: Probabilities and fuzziness are different. But one can use probabilities to define fuzzy predicates. Hajek, et al., uses distributions on propositional formulas to define “Probably ϕ”.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 81

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

But probability = fuzziness. . .

Slogan: Probabilities and fuzziness are different. But one can use probabilities to define fuzzy predicates. Hajek, et al., uses distributions on propositional formulas to define “Probably ϕ”. Truth degree of “Probably ϕ” = P(ϕ).

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-82
SLIDE 82

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α α α

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 83

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α α α

In PDL: αϕ ⇔ α will possibly realize ϕ

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-84
SLIDE 84

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α 1 α 0.5 α . 5

In PDL: αϕ ⇔ α will possibly realize ϕ In fuzzy PDL: αϕ ⇔ α will probably realize ϕ

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-85
SLIDE 85

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α 1 α 0.5 α . 5

In PDL: αϕ ⇔ α will possibly realize ϕ In fuzzy PDL: αϕ ⇔ α will probably realize ϕ ⇔ α reliably realizes ϕ

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-86
SLIDE 86

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α 1 α 0.5 α . 5

In PDL: αϕ ⇔ α will possibly realize ϕ In fuzzy PDL: αϕ ⇔ α will probably realize ϕ ⇔ α reliably realizes ϕ αϕ(w) =

  • w′∈W

P(w

α

− → w′) · ϕ(w′).

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-87
SLIDE 87

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α 1 α 0.5 α . 5

In PDL: αϕ ⇔ α will possibly realize ϕ In fuzzy PDL: αϕ ⇔ α will probably realize ϕ ⇔ α reliably realizes ϕ αϕ(w) =

  • w′∈W

P(w

α

− → w′) · ϕ(w′). Like decision theory, we use means for expected outcomes.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-88
SLIDE 88

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α 1 α 0.5 α . 5

In PDL: αϕ ⇔ α will possibly realize ϕ In fuzzy PDL: αϕ ⇔ α will probably realize ϕ ⇔ α reliably realizes ϕ αϕ(w) =

  • w′∈W

P(w

α

− → w′) · ϕ(w′). Like decision theory, we use means for expected outcomes. Unlike decision theory, there are no utilities involved.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-89
SLIDE 89

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Reliability as a fuzzy proposition

“Reliably”, like “Probably”, is a vague operator.

  • α
  • Q

Q

α 1 α 0.5 α . 5

In PDL: αϕ ⇔ α will possibly realize ϕ In fuzzy PDL: αϕ ⇔ α will probably realize ϕ ⇔ α reliably realizes ϕ αϕ(w) =

  • w′∈W

P(w

α

− → w′) · ϕ(w′). Like decision theory, we use means for expected outcomes. Unlike decision theory, there are no utilities involved. Elegant treatment of complex ends, like αϕ ∧ βψ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-90
SLIDE 90

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Fuzzy ends

An accidental advantage

Weapons are for causing harm.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-91
SLIDE 91

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Fuzzy ends

An accidental advantage

Weapons are for causing harm. Examples: slingshot, nuke

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-92
SLIDE 92

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Fuzzy ends

An accidental advantage

H a r m Weapons are for causing harm. Examples: slingshot, nuke This end is fuzzy.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-93
SLIDE 93

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Fuzzy ends

An accidental advantage

H a r m

s l i n g . 5 sling 0.5 n u k e 1

Weapons are for causing harm. Examples: slingshot, nuke This end is fuzzy. Fuzzy PDL allows for fuzzy ends. A nuke is more effective in causing harm than a slingshot. (Duh.)

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-94
SLIDE 94

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-95
SLIDE 95

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado. J knows that L will either do m in order to realize P or

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-96
SLIDE 96

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado. J knows that L will either do m in order to realize P or n in order to realize Q.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-97
SLIDE 97

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado. J knows that L will either do m in order to realize P or n in order to realize Q. He wants to ensure that L will succeed, whichever she chooses.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-98
SLIDE 98

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado. J knows that L will either do m in order to realize P or n in order to realize Q. He wants to ensure that L will succeed, whichever she chooses. End: mP ∧ nQ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-99
SLIDE 99

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado. J knows that L will either do m in order to realize P or n in order to realize Q. He wants to ensure that L will succeed, whichever she chooses. End: mP ∧ nQ. Aim: maximize min{mP(w), nQ(w)}.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-100
SLIDE 100

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Extending the logic to other connectives

Suppose J and L are cooperative but incommunicado. J knows that L will either do m in order to realize P or n in order to realize Q. He wants to ensure that L will succeed, whichever she chooses. End: mP ∧ nQ. Aim: maximize min{mP(w), nQ(w)}. ϕ ∧ ψ(w) = min

  • ϕ(w), ψ(w)
  • Hughes, Esterline, Kimiaghalam

Means-end Relations and a Measure of Efficacy

slide-101
SLIDE 101

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On formulas αϕ(w) =

w′∈W P( w α

w′ ) · ϕ(w′)

ϕ ∧ ψ(w) = min{ϕ(w), ψ(w)} ϕ ∨ ψ(w) = max{ϕ(w), ψ(w)} ¬ϕ(w) = 1 − ϕ(w) ϕ → ψ(w) =

  • 1

if ϕ(w) ≤ ψ(w), ψ(w) else;

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-102
SLIDE 102

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On formulas αϕ(w) =

w′∈W P( w α

w′ ) · ϕ(w′)

ϕ ∧ ψ(w) = min{ϕ(w), ψ(w)} ϕ ∨ ψ(w) = max{ϕ(w), ψ(w)} ¬ϕ(w) = 1 − ϕ(w) ϕ → ψ(w) =

  • 1

if ϕ(w) ≤ ψ(w), ψ(w) else;

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-103
SLIDE 103

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On formulas αϕ(w) =

w′∈W P( w α

w′ ) · ϕ(w′)

ϕ ∧ ψ(w) = min{ϕ(w), ψ(w)} ϕ ∨ ψ(w) = max{ϕ(w), ψ(w)} ¬ϕ(w) = 1 − ϕ(w) ϕ → ψ(w) =

  • 1

if ϕ(w) ≤ ψ(w), ψ(w) else;

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-104
SLIDE 104

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On formulas αϕ(w) =

w′∈W P( w α

w′ ) · ϕ(w′)

ϕ ∧ ψ(w) = min{ϕ(w), ψ(w)} ϕ ∨ ψ(w) = max{ϕ(w), ψ(w)} ¬ϕ(w) = 1 − ϕ(w) ϕ → ψ(w) =

  • 1

if ϕ(w) ≤ ψ(w), ψ(w) else;

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-105
SLIDE 105

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On formulas αϕ(w) =

w′∈W P( w α

w′ ) · ϕ(w′)

ϕ ∧ ψ(w) = min{ϕ(w), ψ(w)} ϕ ∨ ψ(w) = max{ϕ(w), ψ(w)} ¬ϕ(w) = 1 − ϕ(w) ϕ → ψ(w) =

  • 1

if ϕ(w) ≤ ψ(w), ψ(w) else;

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-106
SLIDE 106

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On formulas αϕ(w) =

w′∈W P( w α

w′ ) · ϕ(w′)

ϕ ∧ ψ(w) = min{ϕ(w), ψ(w)} = ϕ ∩ ψ ϕ ∨ ψ(w) = max{ϕ(w), ψ(w)} = ϕ ∪ ψ ¬ϕ(w) = 1 − ϕ(w) = W \ ϕ ϕ → ψ(w) =

  • 1

if ϕ(w) ≤ ψ(w), ψ(w) else; = ϕ → ψ

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-107
SLIDE 107

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On actions α; β(w)(w′) =

w′′∈W P( w α w′′ ) · P( w′′ β

w′ )

ϕ?(w)(w′) =

  • ϕ(w)

if w = w′; else.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-108
SLIDE 108

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On actions α; β(w)(w′) =

w′′∈W P( w α w′′ ) · P( w′′ β

w′ )

ϕ?(w)(w′) =

  • ϕ(w)

if w = w′; else.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-109
SLIDE 109

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

The semantics of fuzzy PDL

On actions α; β(w)(w′) =

w′′∈W P( w α w′′ ) · P( w′′ β

w′ )

ϕ?(w)(w′) =

  • ϕ(w)

if w = w′; else. ϕ ∪ ψ(w)(w′) ϕ∗(w)(w′)

  • undefined.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-110
SLIDE 110

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms)

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-111
SLIDE 111

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms) Composition: [α; β]ϕ ↔ [α][β]ϕ

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-112
SLIDE 112

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms) Composition: [α; β]ϕ ↔ [α][β]ϕ

Rules:

Modus ponens, cut

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-113
SLIDE 113

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms) Composition: [α; β]ϕ ↔ [α][β]ϕ

Rules:

Modus ponens, cut Necessitation: ϕ/[α]ϕ

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-114
SLIDE 114

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms) Composition: [α; β]ϕ ↔ [α][β]ϕ

Rules:

Modus ponens, cut Necessitation: ϕ/[α]ϕ

Negative results: Axioms:

K: [α](ϕ → ψ) → ([α]ϕ → [ψ])

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-115
SLIDE 115

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms) Composition: [α; β]ϕ ↔ [α][β]ϕ

Rules:

Modus ponens, cut Necessitation: ϕ/[α]ϕ

Negative results: Axioms:

K: [α](ϕ → ψ) → ([α]ϕ → [ψ]) Distributivity: [α](ϕ ∧ ψ) ↔ ([α]ϕ ∧ [α]ψ)

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-116
SLIDE 116

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Validity and Soundness

Positive results: Axioms:

Usual axioms for this fuzzy logic (De Morgan and Implication axioms) Composition: [α; β]ϕ ↔ [α][β]ϕ

Rules:

Modus ponens, cut Necessitation: ϕ/[α]ϕ

Negative results: Axioms:

K: [α](ϕ → ψ) → ([α]ϕ → [ψ]) Distributivity: [α](ϕ ∧ ψ) ↔ ([α]ϕ ∧ [α]ψ) Test: [ψ?]ϕ ↔ (ψ → ϕ)

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-117
SLIDE 117

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Completeness

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-118
SLIDE 118

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Completeness

I wish.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

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SLIDE 119

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Logical properties

Completeness

I wish.

But not with these semantics. Ongoing work. . .

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-120
SLIDE 120

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Concluding remarks

Include non-deterministic features (in paper).

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-121
SLIDE 121

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Concluding remarks

Include non-deterministic features (in paper). Add to formalization of functions (SPT 2005).

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-122
SLIDE 122

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Concluding remarks

Include non-deterministic features (in paper). Add to formalization of functions (SPT 2005). Investigate better behaved semantics.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-123
SLIDE 123

Means-end relations Efficacy via fuzzy logic Reliability as a fuzzy operator The resulting fuzzy logic

Concluding remarks

Include non-deterministic features (in paper). Add to formalization of functions (SPT 2005). Investigate better behaved semantics.

Thank you.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-124
SLIDE 124

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-125
SLIDE 125

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions. Secondary: Fuzzy ends (like “causing harm”).

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-126
SLIDE 126

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions. Secondary: Fuzzy ends (like “causing harm”). Aims: Keep PDL as language for means-end relations.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-127
SLIDE 127

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions. Secondary: Fuzzy ends (like “causing harm”). Aims: Keep PDL as language for means-end relations. Minimal semantic changes.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-128
SLIDE 128

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions. Secondary: Fuzzy ends (like “causing harm”). Aims: Keep PDL as language for means-end relations. Minimal semantic changes. Truth-functional semantics.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-129
SLIDE 129

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions. Secondary: Fuzzy ends (like “causing harm”). Aims: Keep PDL as language for means-end relations. Minimal semantic changes. Truth-functional semantics. Include complex ends like αϕ ∧ βψ.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy

slide-130
SLIDE 130

Adding efficacy to PDL

Concerns: Primary: Adding probabilities to transitions. Secondary: Fuzzy ends (like “causing harm”). Aims: Keep PDL as language for means-end relations. Minimal semantic changes. Truth-functional semantics. Include complex ends like αϕ ∧ βψ. Proposal: Interpret PDL as fuzzy logic.

Hughes, Esterline, Kimiaghalam Means-end Relations and a Measure of Efficacy