Dialogue Structure and Plan/Agent-based Dialogue … · 2 FS2 Restaurant DSP 1 FS1 Suburb 3 FS3 ......

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Dialogue Structure andPlan/Agent-basedDialogue Models

Slides adapted from Arne JönssonLinköping University, Linköping, Sweden

Topics in Human Computer Interaction

www.monash.edu.au

Dialogue and DiscourseStructure

LN3 Topics in HCI, Summer Semester 2015 3

Discourse Structure (Grosz & Sidner, 1986)Three components • Linguistic structure• Intentional structure• Attentional state

Linguistic Structure• Discourse segments and relationships that can

hold between them• Discourse segment – a sequence of utterances• Indicators of segment boundaries:

– Cue phrases– Intonation– Changes in tense and aspect

Cue Phrases – Examples• By the way• For example,• Bye• Oops, I forgot• First of all, finally• OK• But• Thus

• digression• start elaboration• end dialogue• flashback• enumeration• end topic• introduce subtopic• conclusion

Intentional Structure• A discourse has an overall discourse purpose

(DP)• The discourse purpose gives:

– The reason a linguistic act was performed – The reason the particular content was conveyed

• Every discourse segment has its own discourse segment purpose (DSP)

Intentions• The intentions must be recognized from

DSP or DP• Intend that some agent:

– intend to perform some physical task– believe some fact– believe that one fact supports another– intend to identify an object– know some property of an object

Structural Relations• Dominance (DOM)

– DSP1 DOM DSP2 ifDSP2 contributes to DSP1

• Satisfaction-precedence (SP)– DSP1 SP DSP2 if

DSP1 must be satisfied before DSP2

Attentional State• Abstraction of the participants’ focus of attention

– A property of the discourse, not the participants• Dynamic – records objects, properties and

relations that are salient at each point in the discourse

• Modeled by a set of focus spaces in a stack• Focusing structure – the collection of focus

spaces available at any time• Focusing process – the process of manipulating

focus spaces

Focus Stack – ExampleU: Where can I find a good restaurant?

Type of foodDSP2 FS2

RestaurantDSP1 FS1

SuburbDSP3 FS3

S: What type of food do you want?

U: Thai.S: Ok. Any specific suburb?

DSP1 DOM DSP2DSP1 DOM DSP3

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Complex Dialogue Models

Dialogue Management ApproachesDialogue Grammars

Plan-based and Belief Desire Intention (BDI) models

Statistical Models

Formalism Grammars Plans Conditional probabilities

Processing method

Parser Plan recognition StatisticalInference

Performance + Efficient + Powerful + Efficient

Restrictive Not so efficient Can go wrong

Complex Dialogue Modeling Paradigms• Plan (Task) Based Model: The dialogue

involves interactively constructing a plan• Agent-Based Model: Involves planning and

also executing and monitoring operations in a dynamically changing world

• Statistical Model: Learns the system’s behaviour

www.monash.edu.au

Planning

Planning• Given a goal state

– find a sequence of actions to reach the goal state– specify the bindings of the parameters for the actions

• Plan generation assumes– a world model– an action model– a problem-solving strategy

Key Ideas behind Planning• Use a formal language to describe states,

actions and goals– States and goals are represented by clauses, and

actions by logical descriptions of preconditions and effects

• The planner can add actions to a plan wherever they are needed

• Most parts of the world are independent of other parts

– Divide and conquer approach– But need to detect interactions between sub-parts

ActionsAn action (plan operator) A is defined by• preconditions

– what has to be true in order for someone to do A• body

– how A can be divided into sub-actions• effects

– what is true after A is done> intended effects – the goal of A > side-effects

• constraints on entities involved in A

Planning an Action – Regression PlannerGiven a goal G• Find all operators such that G is on their effect list• Choose an operator O• For each precondition P of O

– If P does not hold, post P as a subgoal to be achieved before doing O

Distinction between preconditions and constraints• Need to check constraints and eliminate operators

whose constraints are not satisfied

Planning Example – Making Coffee (I)

• Initial state – have water, have grinder, be at kitchen, store exists, kitchen exists

• Goal state – have a cup of coffee

• Operators

Operator Precondition Effect

PourCoffee Have brewed coffee

Have cup of coffee

MakeCoffee Have beansHave grinderHave boiling waterBe at kitchen

Have brewed coffee

Buy y Be at storeHave money

Have y

GoPlace x Place x exists Be at xGetMoney Be at bank Have moneyBoilWater Be at kitchen

Have waterHave boiling water

Buy brewed coffee Make coffee

Have beans Have grinder

Pour coffee

Have brewed coffee

Planning Example – Making Coffee (II)

Have cup of coffee

Buy beans

Have money At store

www.monash.edu.au

Plan-based Dialogue Models

Plan-based Dialogue ManagementKey idea: “language as action”• Need to recognize intent of an utterance• The task to be performed has subgoals or

preconditions – Actions performed to satisfy them include dialogue acts

• Effects of dialogue acts include changing the belief state of the hearer

Planning and Recognizing Dialogue ActsUtterance generation as planning• Given a communicative goal G and a set of

dialogue act plan operators, generate a plan to achieve G

Dialogue act recognition as plan recognition• Given an utterance U by speaker S, find a

communicative goal G and a plan P to achieve G such that U is a part of P, and S might plausibly have the goal G

Plan-based Theory of Speech Acts (Cohen & Perrault, 1979)

• Based on beliefs, wants, and the interaction between them

• Beliefs – should represent that AGT1 believes that AGT2 believes whether proposition P is true, without AGT1 having to know whether P or ~P is true

– Basic operator: BELIEVE(A,P)– Nesting: BELIEVE(AGT1,BELIEVE(AGT2,P))

• Goals –– WANT(AGT1,BELIEVE(AGT2,P))– BELIEVE(AGT1,WANT(AGT2,P))

Models of Plans• Operators – transform the planner’s model of

the world• Form of operators:

– Effects – propositions to be added to the model of the world

– Preconditions –> can.pr – must be true in the world model for the

operator to be applicable> want.pr – the agent must want to do the action

• INFORM(S,H,P)want.precond: BELIEVE(S,WANT(S,INFORM(S,H,P)))can.precond: BELIEVE(S,P)effect: BELIEVE(H,BELIEVE(S,P))

• CONVINCE(S,H,P)can.precond: BELIEVE(H,BELIEVE(S,P))effect: BELIEVE(H,P)

SPEAKER

Sample Speech Acts as Plan OperatorsHEARER

Example: A Plan to InformS BELIEVE S WANT:

BELIEVE(H,P)

CONVINCE(S,H,P)effect

INFORM(S,H,P)effect

BELIEVE(S,P)can.precond

BELIEVE(H,BELIEVE(S,P))can.precond

Allen and Perrault (1980)• Incorporated knowledge about beliefs• Applied to

– discourse interpretation (recognizing intentions from utterances)

– response generation (related to discourse planning)

Knowledge about Belief• Three types of knowledge about belief

– S believes that A knows that P is trueBELIEVE(S,(P & BELIEVE(A,P))), i.e.,BELIEVE(S, A KNOW P), where

A KNOW P = P & BELIEVE(A,P)– A knows whether P is true

A KNOWIF P = (P & BELIEVE(A,P)) ν (~P & BELIEVE(A,~P))

– A knows the value of a description ix:D(x)A KNOWREF D =

)),(:i BELIEVE(A, &)),(:i( yxDxyxDxy

Additional Speech Acts (I)• INFORM(S,H,P)

want.precond: WANT(S, INFORM(S,H,P))can.precond: S KNOW Peffect: H KNOW PE.g., “HCI is offered at 4 pm”

INFORM(S,H,Offered(HCI,4pm))

• REQUEST(S,H,action)effect: WANT(H, DO(H,action))E.g., “Get me a coffee’’

REQUEST(S,H, Get(H,S,coffee))

Additional Speech Acts (II)• INFORMIF(S,H,P) – inform if P is true or false

want.precond: WANT(S, INFORMIF(S,H,P))can.precond: S KNOWIF Peffect: H KNOWIF PE.g., “Is HCI offered at 3 pm?”

REQUEST(S,H,INFORMIF(H,S,Offered(HCI,3pm))• INFORMREF(S,H,description(P)) – inform a value that

makes P truewant.precond: WANT(S, INFORMREF(S,H,description(P)))can.precond: S KNOWREF description(P)effect: H KNOWREF description(P)E.g., “When is HCI offered?”

REQUEST(S,H,INFORMREF(H,S, time t such thatOffered(HCI,t))

Notation: SBAW(X) –i/c SBAW(Y)• Rules concerning actions• Rules concerning knowledge• Rules concerning planning by others

Plan Inference Rules

Rules Concerning Actions• Precondition-Action Rule

SBAW(P) –i SBAW(ACT)if P is a precondition of action ACT

• Body-Action RuleSBAW(B) –i SBAW(ACT)if B is part of the body of action ACT

• Action-Effect RuleSBAW(ACT) –i SBAW(E)if E is an effect of action ACT

• Want-Action RuleSBAW(nW(ACT)) –i SBAW(ACT)if n is the agent of action ACT

Rules Concerning Knowledge• Know-positive Rule

SBAW(A KNOWIF P) –i SBAW(P)

• Know-negative RuleSBAW(A KNOWIF P) –i SBAW(~P)

• Know-value RuleSBAW(A KNOWIF P(a)) –i SBAW(A KNOWREF ix:P(x))

• Know-term RuleSBAW(A KNOWREF ix:D(x)) –i SBAW(P(ix:D(x)))

Rules Concerning Planning by Others• Action-Precondition Rule

XW(ACT) –c XW(P)if P is a precondition of action ACT

• Action-Body RuleXW(ACT) –c XW(B)if B is part of the body of action ACT

• Effect-Action RuleXW(E) –c XW(ACT)if E is an effect of action ACT

• Know RuleXW(P) –c XW(X KNOWIF P)

Controlling Plan Inference• Main elements

– Expectations – possible goals in the domain– Alternatives – plans emerging from the observations

• Plan specification– Infer – suggest inference rules that apply– Expand – apply rules to modify the plan

• Accept a plan – an alternative meets an expectation

– Heuristics are applied to rate a plan

Example – The BOARD Plan

SBAW(WANT(S,Informref(S,A,i(x:time): DEPART.TIME(train1,x)))) Want-action

SBAW(Informref(S,A,i(x:time): DEPART.TIME(train1,x))) effect (Speech Act)

SBAW(A KNOWREF i(x:time): DEPART.TIME(train1,x))

Know-termSBAW(P?(i(x:time): DEPART.TIME(train1,x)))

match with expectationAT(…,ix:DEPART.TIME(train1,x))

BOARD(A,train1,Toronto)|

AT(A,il:DEPART.LOC(train1,l),it:DEPART.TIME(train1,t))

INFER-EXPAND

ACCEPT

Composite DAs as Plan OperatorsConvinceByInform(Speaker, Hearer, Prop)

Constraints: Agent(Speaker), Agent(Hearer), Proposition(Prop), Bel(Speaker, Prop)

Preconditions: At(Speaker, Loc(Hearer))Effects: Bel(Hearer, Prop)

MotivateByRequest(Speaker, Hearer, Act)Constraints: Agent(Speaker), Agent(Hearer), Action(Act)Preconditions: At(Speaker, Loc(Hearer))Effects: Intend(Hearer, Act)

Trains – Example I (Allen et al., 1995) U: There's been an ice storm off Lake Ontario. Rochester and

Sodus are without power. We need to get emergency crews there as soon as possible.

C: <fig> There are power crews available at Jamestown and Ithaca. And reserve crews without trucks at Dansville. How many do you need?

Trains – Example II (Allen et al., 1994) U: There's been an ice storm off Lake Ontario. Rochester and

Sodus are without power. We need to get emergency crews there as soon as possible.

C: <fig> There are power crews available at Jamestown and Ithaca. And reserve crews without trucks at Dansville. How many do you need?

U: Well, all of them. C: OK. You'll need extra equipment then, which is at the depot

in Bath. U: OK, can you schedule the transport? C: No, The storm's affected Mount Morris as well. It's unlikely

we can get trains through there. OK. We can route it through Sodus instead.

U: No. Go via Avon. I want to get crews to Rochester as soon as possible.

Trains – Domain Plan Example

Repair_power(X)¬Power(X) Power(X)

Power_Crew(P) Equipment(E)At(X,P)

Transport(E,X,Y)

Precond Effect

Precond

Effect

Precond

At(Y,E) Clear_road(R,Y,X)

Effect

Precond

Transport(P,Y,X)

At(Y,P)Clear_road(R,Y,X)

Precond

At(X,E)ConstraintsConstraints

ConstraintsConstraints

Trains – Example IIIU: No. Go via Avon. I want to get crews to

Rochester as soon as possible.

MotivateByRequest(U, C,Transport(Crews, Avon, Rochester))

Constraints: Agent(U), Agent(C), Action(Transport) Preconditions: At(U, Loc(C))Effects: Intend(C, Transport(Crews, Avon, Rochester))

Plan-based Dialogue Management • Advantages

– Tight integration between task performance and dialogue interaction

– Complex dialogue strategies can be implemented as generic operations

– Task-dependent dialogue strategies can be added• Disadvantage

– Large knowledge engineering effort

www.monash.edu.au

Agent-basedDialogue Models

The BDI Model – A Plan-based ModelThree components:• Beliefs about the world – how things are

perceived by an agent• Desires – how an agent wants things to be

(goals)• Intentions – an agent’s commitment to its

desires (goals) and to the plans selected to achieve these goals

Example – BDI Dialogue Agents

Multi-agent conversation in the marketplace

Reading Material• Towards conversational human-computer interaction, Allen

et al. (2001)• Attention, Intentions and the structure of discourse, Grosz

and Sidner (1986)• Elements of a Plan-Based Theory of Speech Acts, Cohen

and Perrault (1979)• Analyzing intention in utterances, Allen and Perrault (1980)• The TRAINS Project: A case study in building a

conversational planning agent, Allen et al. (1995)• Can I finish? Learning when to respond to incremental

interpretation results in interactive dialogue, DeVault, Sagae and Traum (2009)

Dialogue Structure and Plan/Agent-based Dialogue … · 2 FS2 Restaurant DSP 1 FS1 Suburb 3 FS3 ......

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