N4S Research - Story Comparison Adam Amos-Binks 1 1 Department of - - PowerPoint PPT Presentation

N4S Research - Story Comparison

Adam Amos-Binks1

1Department of Computer Science

North Carolina State University

LAS Presentation, Sept 2, 2015

Outline

1

Motivation

2

Background

3

Contribution

4

Validation

5

Summary and Next Steps

6

References

Story Plan Comparison

Plan based representation of a story Given a generative model of story plans, what sense can it generate? Compare two story plans − → quantifying a story solution space

Story Plan Comparison

Plan based representation of a story Given a generative model of story plans, what sense can it generate? Compare two story plans − → quantifying a story solution space

Story Plan Comparison

Plan based representation of a story Given a generative model of story plans, what sense can it generate? Compare two story plans − → quantifying a story solution space

Narrative for Sensemaking (N4S)

Narratology: Narrative Properties

Narrative - cognitive tool[5]

has a causal structure is a problem solving strategy leads to enhanced cognition

Narrative - structure[3]

Story Discourse

Narratology: Narrative Properties

Narrative - cognitive tool[5]

has a causal structure is a problem solving strategy leads to enhanced cognition

Narrative - structure[3]

Story Discourse

Cognitive Psych.: Causal Networks

Figure : Causal network of a narrative[10]

Cognitive Psych.: Causal Networks

Figure : Narrative events with high recall [10]

Cognitive Psych.: Intention

What is Intention?[1]

Intention related to other psych. states: Beliefs and desires Theory of Intention: address problem of Intending to act

◮ Future looking: commit now, act later

Definition of Intention: commensense version tied to plans and planning Humans are rational agents

◮ Capacity 1: form and execute plans ◮ Capacity 2: act purposively ◮ partial plans

Rise of....

AI: Planning

Overview

Intelligent agent action execution World represented in propositional logic Sound and complete planning algorithms:

◮ input: domain and problem ◮ output: solution plans

Figure : Traditional application of planning

AI: Planning

Domain

Predicates Action Schemata

◮ Preconditions ◮ Parameters ◮ Effects

Figure : Dock domain action schemata

AI: Planning

Problem

Objects/literals Initial state Goal state

Figure : Problem Init Figure : Problem Goal

AI: Planning

Solution Plan

Generated by a planning algorithm: partial-order, state-space Has no open pre-conditions or threatened causal links Is part of a solution space (can be huge)

Figure : Dock Solution Plan P

AI: Story Planning

History

Long standing goal of AI to produce coherent narrative Bi-partite model of computational narrative[14] POCL story plans, P:

• S,B,O,L
• ◮ Enables possible story world chronology

◮ Explicitly model action, temporality and causality ◮ Contains a beginning & end ◮ Requires a story domain, problem, solutions

Figure : Story planning and ROI

AI: Story Domain

Figure : Space domain schemata sample

( : types c r e a t u r e landform s h i p − p l a c e ) ( : p r e d i c a t e s ( a l i v e ? c r e a t u r e − c r e a t u r e ) ( stunned ? c r e a t u r e − c r e a t u r e ) ( h a b i t a b l e ? p l a c e − p l a c e ) ( s a f e ? p l a c e − p l a c e ) ( s a f e ? var ) ( s a f e ? c r e a t u r e − c r e a t u r e ) ( e r u p t i n g ? landform − p l a c e ) ( at ? c r e a t u r e − c r e a t u r e ? p l a c e − p l a c e ) ( f i g h t i n g ? c r e a t u r e 1 − c r e a t u r e ? c r e a t u r e 2 − c r e a t u r e ) ( f r i e n d s ? c r e a t u r e 1 − c r e a t u r e ? c r e a t u r e 2 − c r e a t u r e ) ( c a p t a i n ? c r e a t u r e − c r e a t u r e ? s h i p − s h i p ) ( guardian ? c r e a t u r e − c r e a t u r e ? p l a c e − p l a c e ))

Figure : Space domain predicates

AI: Story Problem

( d e f i n e ( problem e x p l o r e ) ( : o b j e c t s zoe − c r e a t u r e l i z a r d − c r e a t u r e s h i p − s h i p cave − p l ace s u r f a c e − landform ) ( : i n i t ( h a b i t a b l e s h i p )( h a b i t a b l e cave )( h a b i t a b l e s u r f a c e ) ( s a f e s h i p )( s a f e s u r f a c e )( s a f e cave )( s a f e zoe )( s a f e l i z a ( a l i v e zoe )( a l i v e l i z a r d ) ( at zoe s h i p )( at l i z a r d cave ) ( captain zoe s h i p ) ( guardian l i z a r d s u r f a c e ) ( i n t e n d s zoe ( f r i e n d s zoe l i z a r d )) ( i n t e n d s zoe ( s a f e zoe )) ( i n t e n d s zoe ( a l i v e zoe )) ( i n t e n d s l i z a r d ( s a f e l i z a r d )) ( i n t e n d s l i z a r d ( a l i v e l i z a r d ) ) ) ( : goal ( not ( h a b i t a b l e s u r f a c e ) ) ) )

Figure : Space Exploration Problem

AI: Story Plan Solution

Semantic Properties

Character intentions (IPOCL)[8]

◮ POCL story plan P:

• S,B,O,L,I
• ◮ Intention Frame:
• c,g,m,σ,T
• ◮

zoe,safe,begin −erupt,teleport,{2,3}

• Conflict (CPOCL)[11]

◮ non-executed steps represent foiled plans ◮

zoe,safe,begin −erupt,teleport,{2,3}

• in conflict with
• zoe,friends,init,teleport,{0,4,5}
• Figure : Space Exploration Solution

AI: “Everyone has plan.......”

Approach: Story Plan Comparison

Plan Comparison

Domain independent vs specific[2] Set theoretic - Jaccard similarity[9]

◮ δA(p,p′) = 1− |S(p)∩S(p′)|

|S(p)∪S(p′)|

◮ δC(p,p′) = 1− |L(p)∩L(p′)|

|L(p)∪L(p′)|

Figure : Jaccard similarity

Approach: Story Plan Comparison

Story Comparison

Causal structure [4] Know exemplars apriori, TTD-MDP[6] Levenshtein edit distance[7]

Story Plan Comparison

Story vs plan comparison Domain specific distance metric (all stories) Need to summarize story semantics!

Approach: Story Plan Comparison

Story Comparison

Causal structure [4] Know exemplars apriori, TTD-MDP[6] Levenshtein edit distance[7]

Story Plan Comparison

Story vs plan comparison Domain specific distance metric (all stories) Need to summarize story semantics!

Approach: Story Plan Summary

Important Steps in Story Plans[13]

Identify causal chain Identify highest causal degree: preconditions + effects (used) E(P)

Figure : Important events of solution plan P

Approach: Story Plan Summary

Intention Frame Summary

• c,g,m,σ
• ◮

zoe,friends,init,teleport,{0,4,5}

• −

→

• zoe,friends,init,teleport
• ◮

zoe,safe,begin −erupt,teleport,{2,3}

• −

→

• zoe,safe,begin −erupt,teleport
• J(P)

Story Plan Summary

ψISIF(P) =

• E,J
• (1)

Approach: Story Plan Summary

Intention Frame Summary

• c,g,m,σ
• ◮

zoe,friends,init,teleport,{0,4,5}

• −

→

• zoe,friends,init,teleport
• ◮

zoe,safe,begin −erupt,teleport,{2,3}

• −

→

• zoe,safe,begin −erupt,teleport
• J(P)

Story Plan Summary

ψISIF(P) =

• E,J
• (1)

Approach: Story Plan Distance Metric

ISIF Distance Metric

δISIF (ψISIF

1

,ψISIF

2

) = 1−

• E(ψISIF

1

)∩E(ψISIF

2

)

• +
• J(ψISIF

1

)∩J(ψISIF

2

)

• E(ψISIF

1

)∪E(ψISIF

2

)

• +
• J(ψISIF

1

)∪J(ψISIF

2

)

• (2)

Preliminary Results: Solution Space Diversity

Figure : Glaive[12]−10 000 POCL solution plans, Π

Preliminary Results: Solution Space Properties

(a) Distinct steps (b) Distinct causal links (c) Distinct important

steps

(d) Distinct intention

frames

Figure : Distinct plan elements

Preliminary Results: Comparing two plans

(a) Plan π1 (b) Plan π2 Figure : Example story plan structures

Preliminary Results: Comparing two plans

πi S(πi) L(πi) I(πi) E(πi) π1 8 (6 executed, 2 non-executed) 35 2 2 π2 8 (5 executed, 3 non-executed) 34 1 1

Table : Story plan properties

πi,πk δA(πi,πk) δC(πi,πk) δISIF(πi,πk) π1,π2 0.11 0.03 0.25

Table : Distance metric results

Summary

Story plan representation Story plan summary ISIF story plan distance metric

Figure : generative model vs classification model

Research Next Steps

More extensive evaluation Alternate story plan distance metrics Quantifying solution space diversity

Figure : Story planning and ROI

Questions and DO 6 Discussion

Bardic integration? Author cyber domain?

Figure : Cyber attack lifecycle Figure : Anticipatory event metric

References I

Michael E Bratman. Intentions in Communication. In Philip R. Cohen, Jerry Morgan, and Martha E. Pollack, editors, Intentions in Communication, chapter What Is In, pages 15–30. The MIT Press, 2003. Alexandra Coman and Héctor Muñoz avila. Generating Diverse Plans Using Quantitative and Qualitative Plan Distance Metrics. In AAAI Conference on Artificial Intelligence, volume 18015, pages 946–951, 2011. Tom Conley and Seymour Chatman. Story and Discourse, volume 8. 1979.

References II

Bernhard Fisseni and Benedikt Löwe. Which dimensions of narrative are relevant for human judgments of story equivalence? Computational Models of Narrative Workshop, pages 114–118, 2012. David Herman. Narrative theory and the cognitive sciences. Center for the Study of Language and Information, 2003. Joshua Jones and Charles L Isbell. Story Similarity Measures for Drama Management with TTD-MDP. In International Conference on Autonomous Agents and Multi-agent Systems, pages 77–84, 2014.

References III

Julie Porteous, Fred Charles, and Marc Cavazza. NetworkING : Using Character Relationships for Interactive Narrative Generation. In International Conference on Autonomous Agents and Multi-agent Systems, pages 595–602, 2013. Mark O. Riedl and R. Michael Young. Narrative Planning : Balancing Plot and Character. Journal of Artificial Intelligence Research, 39:217–267, 2010. Biplav Srivastava, TA Nguyen, and A Gerevini. Domain Independent Approaches for Finding Diverse Plans. In International Joint Conference on Artificial Intelligence, pages 2016–2022, 2007. Tom Trabasso and Linda L Sperry. Causal Relatedness and Importance of Story Events. Journal of Memory and Language, 24:595–611, 1985.

References IV

Stephen G. Ware and R. Michael Young. Glaive: A State-Space Narrative Planner Supporting Intentionality and Conflict. In International Conference on Artificial Intelligence and Interactive Digital Entertainment, 2014. Stephen G Ware, R. Michael Young, Brent Harrison, and David L Roberts. A Computational Model of Plan-based Narrative Conflict at the Fabula Level. IEEE Transactions on Computational Intelligence and AI in Games, 6(3):271–288, 2014.

• R. Michael Young.

Using Grice’s Maxim of Quantity to Select the Content of Plan Descriptions. Artificial Intelligence, 115(2):215–256, 1999.

References V

R Michael Young. Story and discourse: A bipartite model of narrative generation in virtual worlds. Interaction Studies, 8(2):177–208, 2007.

Extra Slides

Extra Slides

Figure : Space domain schemata