Chapter 2 : Model of dialogue systems 2.4~2 - NAIST...
Transcript of Chapter 2 : Model of dialogue systems 2.4~2 - NAIST...
Chapter 2 : Model of dialogue systems2.4~2.5
D1 Seitaro Shinagawa
5/25/20152015©Seitaro Shinagawa AHC-Lab, IS, NAIST 1
5/25/2015 2015©Seitaro Shinagawa AHC-Lab, IS, NAIST 2
2.4 Plan-based model
2.4.1 problem solving using plan
2.4.2 Shared plan and collaborative problem solving
2.4.3 Discourse obligation
2.4.4 BDI model
2.5 Background structure of dialogue systems
2.5.1 Joint attention
2.5.2 participation structure
2.5.3 entrainment
Index
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2.4 Plan-based model
2.4.1 problem solving using plan
2.4.2 Shared plan and collaborative problem solving
2.4.3 Discourse obligation
2.4.4 BDI model
2.5 Background structure of dialogue systems
2.5.1 Joint attention
2.5.2 participation structure
2.5.3 entrainment
Index
Plan-based dialogue model
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Plan : A sequence of actions to achieve a goal.
I’d like to go sightseeing...
Ask friends to go
together
Research spots
Research Hotels
Research How to
goLet’s go!
Goal
ex) Plan (not dialogue model)
Plan-based dialogue model
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Plan : A sequence of actions to achieve a goal.
I’d like to go shopping…
ex) Plan (dialogue model)
Is there any supermarkets around here?
Goal
Oh, I see. Thanks.
You get Information about
shops around here.
You can see the back side of that station.
Motivation of plan-based model
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inference model
Code model : old fashion model of dialogue systems
Idea A Idea A Hoge hoge….?
Encoder of Idea A Decoder of Idea A
Spoken language
dialogue will be successful if they have same code.
However, meanings could be
change by situation.
We can’t consider all cases.
Motivation of plan-based model
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Inference model : Not assuming that they have same code.
Idea A Idea A?May beIdea B?
Hoge hoge….?
Encoder of Idea A Decoder of Idea A
Spoken language
Decoder interpret meaning subjectively with implicature.
Implicature :nonverbal information a listener will understand
ex) place, time, occasion, etc.
Plan-based model is computable inference model
Cooperative principle
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In a suggestion, our communication with implicature is
based on cooperative principle.
Grice’s definition (at least)
Maxim of quantity
Give information as much as his demand.
Don’t give more information than he wants.
Maxim of quality (tell him truth)
Don’t tell him a lie you think.
Don’t tell him something you don’t make sure.
Maxim of relation
Maxim of manner (tell easily to understand)
Reject ambiguousness.
Reject various meanings.
Tell briefly.
Tell logically.
2.4.1 problem solving using plan
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I’d like to go fishing with him.
He want to go fishing with me?
“Are you free this weekend?”“I feel to eat fish outside.”
“I found nice spot. ”“Why don’t’ you join me?”
Planning Plan recognition
Actions
Predicate logic as representation of plan
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a
b cg
A initial state as below,
𝐵𝑂𝑋 𝑎 ∧ 𝐵𝑂𝑋 𝑏 ∧ 𝐵𝑂𝑋 𝑐∧ 𝐺𝑅𝑂𝑈𝑁𝐷 𝑔
∧ 𝑂𝑁 𝑎, 𝑏 ∧ 𝑂𝑁 𝑏, 𝑔 ∧ 𝑂𝑁(𝑐, 𝑔)
Predicate logic
b
c
a
g
𝑀𝑂𝑉𝐸 𝑎, 𝑔 , 𝑀𝑂𝑉𝐸 𝑏, 𝑐 ,𝑀𝑂𝑉𝐸 𝑎, 𝑏
Operation
Operator(operations user defined)𝐻𝐸𝐴𝐷𝐸𝑅 ∶ 𝑀𝑂𝑉𝐸𝑃𝑅𝐸𝑅𝐸𝑄𝑈𝐼𝑆𝐼𝑇𝐸 ∶ 𝐵𝑂𝑋 𝑋 ∧ 𝑂𝑁 𝑋,𝑊 ,⋯
Goal state
(ref : text P51)
A example of speech act with predicate logic
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S requests H to accomplish A (Litman & Allen)
𝐻𝐸𝐴𝐷𝐸𝑅 ∶PREREQUISITE ∶
DECOMPOSITION1 ∶DECOMPOSITION2 ∶DECOMPOSITION3 ∶DECOMPOSITION4 ∶
EFFECTS ∶
REQUEST S, H, AWANT S, ASURFACE − REQUEST S, H, ASURFACE − REQUEST S, H, INFORMIF(𝐻, 𝑆, 𝐶𝐴𝑁𝐷𝑂(𝐻, 𝐴)
SURFACE − INFORM S, H, ! CANDO S, A
SURFACE − INFORM S,H,𝑊𝐴𝑁𝑇 S, A
WANT H, A , KNOW(H,WANT S, A )
WANT S, A : S want to do A
DECOMPOSITION1 ∶ S requests H to accomplish A. (conversation)
DECOMPOSITION2 ∶ S requests H to inform S if H can accomplish A. (conversation)
DECOMPOSITION3 ∶ S informs H that A can’t accomplish A. (conversation)
DECOMPOSITION4 ∶ S informs H that S wants to accomplish A. (conversation)
A operator
Discourse plan and domain plan
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Discourse plan : Operators or plan composed of
operators for speech act
Domain plan : A plan related to domain task
(control operator)
𝐻𝐸𝐴𝐷𝐸𝑅 ∶DECOMPOSITION ∶
EFFECTS ∶CONSTRAINTS ∶
A discourse plan : Introduce domain plan
𝐼𝑁𝑇𝑅𝑂𝐷𝑈𝐶𝐸 − 𝑃𝐿𝐴𝑁(𝑆, 𝐻, 𝐴, 𝑃)REQUESTS(S, H, A)
WANT H, P , NEXT(A, P)STEP A, P , AGENT(A,H)
𝐼𝐷𝐸𝑁𝑇𝐼𝐹𝑌 − 𝑃𝐴𝑅𝐴𝑀𝐸𝑇𝐸𝑅 : be concrete𝐶𝑂𝑅𝑅𝐸𝐶𝑇 − 𝑃𝐿𝐴𝑁
2.4.2 Shared plan and collaborative problem solving
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The task two or more people achieve together.
Pollack’s definition of two aspect of shared plan
Data-structure view of plans
Mental phenomenon view of plans
( Operator, Tree )
( representation of mental state
of agent)
Formulation
Sh𝑎𝑟𝑒𝑑 𝑃𝑙𝑎𝑛(𝐺1, 𝐺2, 𝐴) ↔
𝑀𝐵 𝐺1, 𝐺2, 𝐸𝑋𝐸𝐶 𝑎, 𝐺𝑎 ∶
𝑀𝐵 𝐺1, 𝐺2, 𝐺𝐸𝑁 𝑎, 𝐴 ∶
𝑀𝐵 𝐺1, 𝐺2, 𝐼𝑁𝑇 𝐺𝑎 , 𝑎 ∶
𝑀𝐵 𝐺1, 𝐺2, 𝐼𝑁𝑇 𝐺𝑎 , 𝐵𝑌(𝑎, 𝐴) ∶
𝐼𝑁𝑇 𝐺𝑎, 𝑎 ∶𝐼𝑁𝑇 𝐺𝑎, 𝐵𝑌(𝑎, 𝐴) ∶
𝐺1, 𝐺2 ∶ 𝑀𝐸𝑁𝐵𝐸𝑅
𝑎 ∶ 𝐴𝑐𝑡𝑖𝑜𝑛
𝐴 ∶ 𝐽𝑜𝑖𝑛𝑡 𝑎𝑐𝑡𝑖𝑣𝑖𝑡𝑦 (𝑃𝑙𝑎𝑛)
G1,G2 believe G1 or G2 can execute a.
G1,G2 believe a generate A.
G1,G2 believe G1 or G2 has intention of a.
G1,G2 believe G1 or G2 has intention that
A is realized by a.
𝐺𝑎: 𝐺1 𝑜𝑟 𝐺2
G1 or G2 has intention of a
G1 or G2 has intention that A is realized by a.
2.4.3 Discourse obligation
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Assuming that dialogue progresses with adjacency pair.
Addressee does not always give priority to
speaker’s intention compared with theirs.
Intention A Intention B
Relation to intention A
Intention B
Intention A
priority Stack!
(I will tell B later…)Rsponse to intention A
2.4.4 BDI model
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Agents choose their next action using three factors.
Next action
Intention
DesireBelief
Belief : Knowledge of Agents
Recognize situation
Consider what actions are possible
Desire : User’s desire system guesses
Adaptable to various situations
Intention : Stack
Stacked plans created by
belief and desire
A example of BDI model at family restaurant
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_(:3」∠)_
Menu
黒酢あんかけ = healthy
ポン酢かけ = healthy
gratin = not healthy
steak = not healthy etc.
User's satisfaction (%)
0 20 40 60 80 100
黒酢あんかけ ポン酢かけ gratin steak
Belief
Desire
黒酢あんかけ定食 ポン酢かけ定食
Intention
(Decide my goal)
I’d like to eat healthy dishes.
Task : Eat healthy dishes
ヽ(*´∀`)ノ I will eat 黒酢あんかけ定食!
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2.4 Plan-based model
2.4.1 problem solving using plan
2.4.2 Shared plan and collaborative problem solving
2.4.3 Discourse obligation
2.4.4 BDI model
2.5 Background structure of dialogue systems
2.5.1 Joint attention
2.5.2 Participation structure
2.5.3 Entrainment
Index
2.5.1 Joint attention
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attentionattention
Object
It is related to social ability of human.
It is related to acquisition of object name or
grammar.
2.5.2 Participation structure
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Multiparty dialogue
Speaker Addressee Side
participantOver
hearer
Eaves
dropper
Ratified participant
Side participant
Listener
Side participant : May be addressee
Over hearer : Realized by speaker
Eaves dropper : Not realized by speaker
2.5.3 Entrainment (of word meanings)
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In dialogue, we often use a word as which has a specific meaning.
Why don’t you “プレモル”
this night?
Drink Premium malts ! (beer)
Assume that a M1 student enter the dialogue, I say,
2.5.3 Entrainment (of word meanings)
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In dialogue, we often use a word as which has a specific meaning.
Why don’t you “プレモル”
this night?
Drink Premium malts ! (beer)
Assume that a M1 student enter the dialogue, I say,
PRML (Pattern Recognition
and Machine Learning)
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2.5.3 Entrainment (of word meanings)
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In dialogue, we often use a word as which has a specific meaning.
Why don’t you “プレモル”
this night?
Drink Premium malts ! (beer)
Assume that a M1 student enter the dialogue, I say,
PRML (Pattern Recognition
and Machine Learning)
※ Attention
Entrainment is temporary
effect in a dialogue to be easy
to interaction.
Before he/she comes in…
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