‐‐ Communicative Intelligence ‐‐ Toyoaki Nishida Kyoto …. Introduction ‐‐Communicative Intelligence ‐‐ Toyoaki Nishida Kyoto University Year AI ICT 1940～ 1936:

Artificial Intelligence Adv., April 8th, 2015

Copyright © 2015, Toyoaki Nishida, Atsushi Nakazawa, Yoshimasa Ohmoto, Yasser Mohammad, At ,Inc. All Rights Reserved.

1. Introduction‐‐ Communicative Intelligence ‐‐

Toyoaki NishidaKyoto University

Year AI ICT

1940～ 1936: Turing Machine, 1947: von Neumann Computer, 1948: Information Theory, by C. Shannon and W. Weaver, 1948: Cybernetics by Wiener

1950～ 1952‐62: Checker program by A.Samuel1956: Dartmouth Conference 1957: FORTRAN by J.Backus

1960～ 1961: Symbolic Integration program SAINT by J.Slagle1962: Perceptron by F.Rosenblatt1966: The ALPAC report against Machine Translation by R. Pierce1967: Formula Manipulation System Macsyma by J.Moses1967: Dendral for Mass Spectrum Analysis by E.Feigenbaum

1961: Mathematical theory of Packet Networks by L. Kleinrock1963: Interactive Computer Graphics by I.Sutherland

1968: Mouse and Bitmap display for oN Line System (NLS) by D.C.Engelbart1969: ARPA‐net

1970～ 1971: Natural Language Dialogue System SHRDLU, by T.Winograd1973: Combinatorial Explosion problem pointed out in The Lighthill report1974: MYCIN by T.ShortliffeMid 1970’s: Prial Sketch and Visual Perceptron by D.Marr1976: Automated Mathematician (AM) by D.Lenat1979: Autonomous Vehicle Stanford Cart by H.Moravec

1970: ALOHAnet1970: Relational Database Theory by E.F.Codd1972: Theory of NP‐completeness by S.Cook and R.KarpMid 1970’s: Alto Machine by A.Kay and A.Goldberg1976: Ethernet1979: Spreadsheet Program Visicalc by D.Bricklin

1980～ 1982: Fifth Generation Computer Project1984: The CYC Project by D.LenatMid 1980’s: Back‐propagation algorithm was widely used1985: the Cybernetic Artist Aaron by H.Cohen1986: Subsumption Architecture by R.Brooks1989: An Autonomous Vehicle ALVINN by D.Pomerleau

1982:TCP/IP Protocol by B.Kahn and V.CerfMid 1980’s: First Wireless Tag Products1987: UUNET started the Commercial UUCP Network Connection Service1988: Internet worm (Morris Worm)1989: World Wide Web by T.Berners‐Lee1989: The number of hosts on the Internet has exceeded 100,000.

1990～ 1990: Genetic Programming by J.R.KozaEarly1990’s: TD‐Gammon by G.TesauroMid 1990’s: Data Mining Technology1997: DeepBlue defeated the World Chess Champion G.Kasparov1997: The First Robocup by H.Kitano1999: Robot pets became commercially available

1992: The number of hosts on the Internet has exceeded 1,000,000.1994: Shopping malls on the Internet1994: W3C was founded by T. Berners‐Lee1997: Google Search1998: XML1.0（eXtensible Markup Language) by W3C1998: PayPal

2000～ 2000: Honda Asimo

2004: The Mars Exploration Rovers (Spirit & Opportunity)

2001: Wikipedia.2003: Skype / iTunes store2004: Facebook2005: YouTube / Google Earth2006: Twitter2007: Google Street View

2010～ 2010: Google Driverless Car / Kinect2011: IBM Watson Jeopardy defeated two of the greatest champions2012: Siri

History of AI research in contrast with ICT

Year AI ICT

1940～ 1936: Turing Machine, 1947: von Neumann Computer, 1948: Information Theory, by C. Shannon and W. Weaver, 1948: Cybernetics by Wiener

1950～ 1952‐62: Checker program by A.Samuel1956: Dartmouth Conference 1957: FORTRAN by J.Backus

1960～ 1961: Symbolic Integration program SAINT by J.Slagle1962: Perceptron by F.Rosenblatt1966: The ALPAC report against Machine Translation by R. Pierce1967: Formula Manipulation System Macsyma by J.Moses1967: Dendral for Mass Spectrum Analysis by E.Feigenbaum

1961: Mathematical theory of Packet Networks by L. Kleinrock1963: Interactive Computer Graphics by I.Sutherland

1968: Mouse and Bitmap display for oN Line System (NLS) by D.C.Engelbart1969: ARPA‐net

1970～ 1971: Natural Language Dialogue System SHRDLU, by T.Winograd1973: Combinatorial Explosion problem pointed out in The Lighthill report1974: MYCIN by T.ShortliffeMid 1970’s: Prial Sketch and Visual Perceptron by D.Marr1976: Automated Mathematician (AM) by D.Lenat1979: Autonomous Vehicle Stanford Cart by H.Moravec

1970: ALOHAnet1970: Relational Database Theory by E.F.Codd1972: Theory of NP‐completeness by S.Cook and R.KarpMid 1970’s: Alto Machine by A.Kay and A.Goldberg1976: Ethernet1979: Spreadsheet Program Visicalc by D.Bricklin

1980～ 1982: Fifth Generation Computer Project1984: The CYC Project by D.LenatMid 1980’s: Back‐propagation algorithm was widely used1985: the Cybernetic Artist Aaron by H.Cohen1986: Subsumption Architecture by R.Brooks1989: An Autonomous Vehicle ALVINN by D.Pomerleau

1982:TCP/IP Protocol by B.Kahn and V.CerfMid 1980’s: First Wireless Tag Products1987: UUNET started the Commercial UUCP Network Connection Service1988: Internet worm (Morris Worm)1989: World Wide Web by T.Berners‐Lee1989: The number of hosts on the Internet has exceeded 100,000.

1990～ 1990: Genetic Programming by J.R.KozaEarly1990’s: TD‐Gammon by G.TesauroMid 1990’s: Data Mining Technology1997: DeepBlue defeated the World Chess Champion G.Kasparov1997: The First Robocup by H.Kitano1999: Robot pets became commercially available

1992: The number of hosts on the Internet has exceeded 1,000,000.1994: Shopping malls on the Internet1994: W3C was founded by T. Berners‐Lee1997: Google Search1998: XML1.0（eXtensible Markup Language) by W3C1998: PayPal

2000～ 2000: Honda Asimo

2004: The Mars Exploration Rovers (Spirit & Opportunity)

2001: Wikipedia.2003: Skype / iTunes store2004: Facebook2005: YouTube / Google Earth2006: Twitter2007: Google Street View

2010～ 2010: Google Driverless Car / Kinect2011: IBM Watson Jeopardy defeated two of the greatest champions2012: Siri

History of AI research in contrast with ICT

Exponential Growth‐Moore’s Law‐ Expanding Internet World

Universal Turing Machine‐ from Hardware to Software

Generic Computing Engines‐ from Software to Data

Service Copying‐Machine Learning / Data Mining

Symbolic Computing‐ Knowledge‐based Systems

Heuristics‐ Intelligence as Clever Computing

Embodied & Network Intelligence‐ Like human and society

Communicative Intelligence for Bridging People and SI

[Nishida‐Nakazawa‐Ohmoto‐Mohammad 2014]

Communicative Intelligence

Super Intelligence People

Challenge: A robot that can participate in conversation

Long-term goal

Eye gaze

Hand gesture PostureFacial expression

AskingNegotiating

Proposing

ConvivialitySocial networksTrust

Conversation is a complex business

Application

Platform Evaluation

Content production Model building

Analysis

Theory

Measurement

Conversational interactions

Conversational Informatics


Applied Intelligence Information Processing GroupCognitive DesignWe aim at designing the expressions, functions, control, and interactions of artifacts based on a physio‐cognitive approach.

Interaction Measurement, Analysis and Modeling We aim at uncovering principles of verbal and nonverbal interactions that people engage everyday as a part of intellectual activities and developing a suite of technologies to help people benefit from conversational interactions.

Augmented Conversation EnvironmentWe aim at building a smart environment that integrates conversation in physical and virtual spaces.

Conversation in the physical environment Conversation in the virtual environment

Learning by imitation

Designing high‐level cognitive interaction

Reconstructing 3D conversation scene

ICIE: Immersive Collaborative Interaction Environment

Multiparty conversation recorder

Eye tracker

Eye tracker (wearable)

Polygraph Optical motion capture systems

Cluster computer

IMADE: Interaction Measurement, Analysis, and Design Environment

SmartInFill

Annotation tool

Conversation corpus Virtualization of physical space

ICIE for the shared virtual world

[Lala 2013]https://www.youtube.com/watch?v=V‐9SKpcMrzk


ICIE – immersive collaborative interaction environment

Immersive WOZ environment

Feedback generationMotion mappingUser motion sensingHead recognitionGesture recognitionFace modelHuman body model

WOZ operating environment

WOZ operatorTele-operated robot

The conversation place

[Ohmoto 2013a]

Projecting the real world into the virtual world

[Ohmoto 2011]http://www.youtube.com/watch?v=68UrJv65HvY

IMADE: Interaction Measurement, Analysis, and Design Environment


iCorpusStudio

Collaborative annotation system


[Yano 2012]

3D conversation capture – over the shoulder

Corneal Imaging CameraScene cameraEye camera

• Lightweight and versatile system • Appl.: Google Glass like HMD, unconstrained setups

Corneal Image Feature MatchingProblem:Local feature correspondence + RANSAC does not work due to large noise in eye images

Approach:1. Formulate problem as registration of 3D

spherical light maps of eye and scene image2. Single point algorithm for robust alignment

Non‐intrusive Eye Gaze Tracking (EGT) by corneal imaging

Eye images with GRP

Scene images with PoG

↑ Aligned results (from eye images)

Peripheral vision map overlaid to scene imagePeripheral vision map in eye image

Gaze Reflection Point Mapping

Application 1: Non‐intrusive and uncalibrated PoG estimation

Application 2: Peripheral vision estimation

Gaze trajectory in static scene image

Mouse（67.37pixel/mm）

66.3×37.3cm2560×1600pixel

Head mount eye camera

Eyeball display distance50cm

IR filter

InfraredLED

140 Lux at eyes

Display

Quantitative analysis of pupil dilations against the difficulty of the task

Estimate how Pupil dilations is related to the difficulty of the irritating maze game.

[Tange, Nakazawa 2015]

Pupil dilations in the irritating maze game


110

115

120

125

130

135

140

145

150

0 10000 20000 30000 40000 50000 60000 70000 80000 90000IN OUT

5pixel 10pixel10pixel

Expand before the narrow conduit

Delay before contraction

Elapse time（ms）

Pupil size

（pixel）

Pupil dilations in the irritating maze game


Tacit social signals

Intentions are dynamic and tacit


Facilitative agent that can track dynamic and tacit intentions

The first half

The second half

Agent Control

Estimation of emphasizing points

Switch or not between divergent and convergent

Next proposal

Agent behavior

ResultsEvaluating estimation process

Results

Select next proposal

Next proposal

Agent behavior

Results

Select next proposal

Facilitative agent Estimation agent

Divergent

Social signals

Social signals

Estimation of emphasizing points

[Ohmoto 2013b]

Results of questionnaires

• Facilitative agent is significantly more preferred in all aspects we have investigated.

[Ohmoto 2013b]

Inducing intentional stance toward agent players

Q: How can we induce an intentional stance toward NPCs?

H: Presenting goal‐oriented trial‐and‐error process with multimodal behaviors.

[Ohmoto 2015]


[Ohmoto 2015]

Video analysis

Questionnaire analysis

Trial‐and‐error agent:Presents goal‐oriented trial‐and‐error process by multimodal behavior (e.g., hand gesture, body orientation, etc that only ambiguously display mental states).

Text display agent:Presents agent’s behavioral intention by unambiguous text.

# of comm. acts

score

Intentional stance

Design stance


Q: How can we induce an intentional stance toward NPCs?H: Demonstrating strategy change.

[Suyama, Ohmoto 2015]

Player #1 in ICIE #1

Red hat

Player #2 in ICIE #2

Green hat

Player #3 (agent)

Blue hat

PurposeEvaluating the physiological sensing method for estimating user’s concentration and appropriate advice giving during exercise.

MethodVR exercise game as the task.Using SCR and LF/HF.Controlling the condition of advice giving.

Findings

Estimating user’s concentration during exercise

Deliberating but not reacting Deliberating and reacting

SCR

LF/HF

+

+

‐

‐

Hide from the chaser

Not concentrated

Hide in the place the chaser checked previously.

Simply moving aroundadvice

No advice


[Mohammad 2009]

Constrained Motif Discovery: • Given a time series X(t)

find recurring patterns of length between L1 and L2 using distance function D subject to the constraint P(t), where P(t) is an estimation of the probability that a motif occurrence exists near time step t.

[Mohammad 2009]

P(t)

unlikely

likely

Change point discovery


Future

Change angle

GH

Past Futuret

;...; 1H t seq t n seq t 1 ;...;G t seq t seq t n

1

1

1

ˆ

f

f

Ti i i

l

i ii

l

ii

s t t t

csx

c

t

TtVtStUtH )()()()( Find optimal lP

ggT uutGtG )()(Find optimal lF

11and,)( jjjFg

ii liut

fTll

Tll

i litUUtUUt ,)()()(

)()()()()(ˆ)(~ tttttxtx PFPF


Robust Singular Spectrum Transform

[Mohammad 2009]


[Mohammad 2009]

Fluid Learning

[Mohammad 2015]

Synthetic Evidential Study

Synthetic evidential study (SES) combines dramatic role play and group discussion to help people spin stories by bringing together partial thoughts and evidence.

Componentize

Reuse

SES session Interpretation archive

Structured collection of {story, background, critique}Agent Play

Dramatic role play

Group discussions

At the beginning of the 18th century, a feudal lord named Asano Takumi-no-kami Naganori was in charge of a reception for envoys from the Imperial Court in Kyoto. Another feudal lord, Kira Kozuke-no-suke Yoshinaka, was appointed to instruct Asano in the ceremonies. On the day of the reception, while Kira was talking with Yoriteru Kajikawa, a lesser official, at “Matsu no Roka” (“Hallway of Pine Trees”) in Edo Castle, Asano came up to them screaming “This is for revenge!!” and slashed Kira twice with a short sword. Soon after the incident, Kajikawa restrained Asano, who was then imprisoned. The reason for the attack was not known, though it was widely believed that Kira had somehow humiliated Asano. Ultimately Asano was sentenced to commit seppuku, a ritual suicide, but Kira went without punishment.

Hallway of Pine Trees (from Chushingura)

Kira Kozuke-no-suke Yoshinaka

Asano Takumi-no-kami Naganori

Yoriteru Kajikawa

Why was it possible?

How did it happen?

What did each think?

Dramatic Role Play

Group play capture

Agent play

Discussion phase

Asano

Kira

Kajikawa

Third person view First person view

Discussions

History of conversational systems development

t1990 2000 201019801970

Natural language dialogue systems

Speech dialogue systems

Multi‐modal dialogue systems

Embodied Conversational Agents / Intelligent Virtual Human

Story Understanding systems

Conversational Systems

Transactional systems

Interactional systems

Affective Computing

Cognitive systems

Natural language question answering systems

The Knowledge Navigator

Early Natural Language Dialogue Systems

[Green 1961]

Spec[ification] list:

“Where did the Red Sox play on July 7?”‐> Place = ?

Team = Red SoxMonth = JulyDay = 7

“What teams won 10 games in July?”‐> Team(winning) = ?

Game(number of) = 10Month = July

Dictionary:“team” ‐> meaning: Team=(blank)“Red Sox” ‐> meaning: Team=Red Sox“who” ‐> meaning: Team=?“winning” ‐> meaning: subroutine

Data:Month = July

Place= BostonDay = 7Game Serial No. = 96(Team = Red Sox, Score= 5)(Team = Yankees, Score = 3)

Baseball

• One of the earliest natural language question answering system.

• Answers such questions about baseball games.


[Weizenbaum 1966]

The syntax routine determines whether the verb is active or passive and locates its subject and object.

The syntax analysis checks to see if any of the words is marked as a question word. If not, a signal is set to indicate that the question requires a yes/no answer

4. Content AnalysisThe content analysis uses the dictionary meanings and the results of the syntactic analysis to set up a specification list for the processing program. E.g.,

“each team”: Team=(blank) ‐> Team=each“what team” :Team=(blank)‐> Team=?“Who beat the Yankees on July 4”: Team=(blank)‐> Team=?, Team(winning)=? Team(losing)=Yankees“six games” : Game=(blank)‐>Game(number of)=6“how many games”: Game=(blank)‐>Game(number of)=?“Who was the winning team…”: Team=? and Team(winning)=(blank) ‐> Team(winning)=?

5. ProcessingThe specification list indicates to the processor what part of the stored data is relevant for answering the input question. The processor extracts the matching information from the data and procedures, for the responder, the answer to the question in the form of a list structure.

Modules of Baseball

1. Question Read‐in2. Dictionary Look‐up3. Syntax(1) scan for ambiguities in part of speech: in some

cases, resolved by looking at adjoining words; in other cases, resolved by inspecting the entire question.

(2) locates and brackets the noun phrases, [], and the prepositional and adverbial phrases, (). The verb is left unbracketed. E.g.,

“How many games did the Yankees play in July?”‐> [How many games] did [the Yankees] play (in [July])?

Any unbracketed preposition is attached to the first noun phrase in the sentence, and prepositional brackets added. E.g.,

“Who did the Red Sox lose to on July 5?”‐> (To [who]) did [the Red Sox] lose (on [July 5])?


[Woods 1973]

LUNAR

Prototype natural language question answering system that helps lunar geologists access chemical analysis data on lunar rock and soil composition.

(i)Syntactic analysis using heuristic/semantic information to choose the most likely parsing (Augmented Transition Network Grammar was used)

(ii) Semantic interpretation: produce a formal representation for queries

(iii) Execution of this formal expression in the retrieval


[Woods 1973]

Augmented Transition Network Grammar

S:NP V NP

AUX V PP

NP:

DET NOUN

ADJ

PRON

PREP NPPP:


Winograd, Terry, Understanding Natural Language, New York: Academic Press, 1972. http://hci.stanford.edu/~winograd/shrdlu/

SHRDLU

• Natural language understanding system working on the “Blocks” world.

• Based on the belief that “a computer cannot deal with language unless it can understand the subject it is discussing”

• Answers questions, executes commands, and accepts information in normal English dialog.

• Uses semantic information and context to understand discourse and disambiguate sentences.

• Procedural knowledge representation <‐> declarative KR


Winograd, Terry, Understanding Natural Language, New York: Academic Press, 1972. http://hci.stanford.edu/~winograd/shrdlu/

USER: “Pick up a big red block”SHRDLU: “OK”

simulate robot arm

Syntactic analysis

Semantic analysis

Planning

Graphics

Questioning answering

SHRDLU


[Weizenbaum 1966]

Example:

“You are very helpful”‐> “I are very helpful”

by a simple substitution rule‐> “What makes you think I am very helpful”

by a decomposition template: (0 I are 0)by a reassembly rule:

(What makes you think I am 4)

After Ri,j was applied, an index in the transformation list is inserted to prevent the same reassembly rule from being applied in a row.

(How = (What)) allows the transformation rule for “how” to be equally applied to “what”

(MOTHER DLIST (/NOUN FAMILY))allows the keyword “MOTHER” to be identified as a noun and as a member of the class “family”. As a result, “MOTHER” will match “(/FAMILY)” in a decomposition rule.

Transformation rules: collection of key lists.

where, K: keyword, Di: decomposition template, Ri,j: Reassembly rule.

Example of a decomposition template and a reassembly rule:D: (0 YOU 0 ME) R: (WHAT MAKES YOU THINK I 3 YOU)

“It seems that you hate me”‐> “What makes you think I hate you”

Substitution rule. E.g.,

(YOURSELF = MYSELF)(MY YOUR 5 (transformation rules))

)))()()()((

))()()()((

))()()()(((

,2,1,

,22,21,22

,12,11,11

2

1

nmnnnn

m

m

RRRD

RRRD

RRRDK

ELIZA: Natural language dialogue system without explicit knowledge about the discourse domain

Early Speech Dialogue Systems

[Erman 1980]

The Hearsay‐II Speech‐Understanding System

Integrates multiple levels of information processing by knowledge sources coordinated by the blackboard model: • Parameter• Segment• Syllable• Word• Word‐sequence• Phrase• Data base interface

Combining top‐down (hypothesis driven) and bottom‐up (data‐driven) processing.

Selective attention to allocate limited computing resources.

[Erman 1980]

The blackboard architecture

| ARE | ANY | BY | FEIGENBAUM | AND | FELDMAN |

(g) Phrases

(f) Word sequences

(e) Words ‐ 1

(d) Words – 2

(c) Syllable classes

(b) Segments

(a) ParametersSEG

POM

MOW

WORD‐CTL

VERIFY

PARSE

SEMANT

WORD‐SEQ

VERIFY

WORD‐SEQ‐CTL

PREDICT STOP

CONTACT

RPOL

[Erman 1980]

The blackboard architecture

KS1

Level2

Levelk

Level1

Blackboard

Schedulingqueues

KSn

Focus‐of‐controlDatabase

BlackboardMonitor

Scheduler

…

…

… …Program modules Databases Data flow Control flow

Early Speech Dialogue Systems

PUT‐THAT‐THERE

The conjoint use of voice‐input and gesture recognition to command events on a large format raster‐scan graphic display in a “Media Room”

Commands

“Create a blue square there.”“Move the blue triangle to the right of the green square”“Move that to the right of the green square.” (with pointing)“Put that there” (indicated by gesture)“Make that smaller” (with pointing gesture)“Make that (indicating some item) like that (indicating some other item)”“Delete that” (pointing to some item)“Call that … the calendar” (with pointing)

[Bolt 1980]

“Phil”: concrete image of a personal assistant.

The Knowledge Navigator

[Apple Computer 1987]

Phil, the agent

CASA: Computers Are Social Actors

Social responses to computers

- not: conscious beliefs that computers are human or human‐like.

- not: from user’s ignorance or psychological or social dysfunctions

- not from: a belief that subjects are interacting with programmers

- rather: human‐computer relationship is fundamentally social.

Nass 1994]

- Support interactive give and takeAssistants will need not only respond to questions but also ask questions

- Recognize the costs of interaction and delayIt is inappropriate to require the user’s confirmation of every decision made while carrying out a task.

- Manage interruptions effectivelyWhen it is necessary to initiate an interaction with the user, the assistant needs to do so carefully, recognizing the likelihood that the user is already occupied to some degree.

- Acknowledge the social and emotional aspects of interactionTo become a comfortable working partner, a computer assistant will need to vary its behavior depending on such the task, the time of day, and the boss’s mood.

Requirements

=> The PERSONA project @ Microsoft Research[Ball 1997]

From Tool-based Computing to Assistive Interface

[Ball 1997]

The architecture of Peedy

Spoken Language Interpretation

Character Animation

Sound Generation

Background and Object Animation

Dialogue Management

MicrophoneWhisper Speech Recognition

NamesProper Name Substitution

NLPLanguage Analysis

SemanticTemplate Matching & Object Descriptions

DialogueContext & Conversation State

ApplicationCD Changes

Speech Controller

Player/ReActorAnimation Engine

Character

Sound

CD Player

[Ball 1997]

Names Database Action Templates Database

Object Database (CDs)

Dialogue Rules Database

Speech & Animation Database

The architecture of Peedy

stGotCDevOK / do

stSuggested

Peedy: “I have The Bonnie Raitt Collection, would you like to hear something from that?”

User: “Sure”

pePickTrack; genTracks; doSelect; Say <!PDSays(how About)>

pePickTrack: causes Peedy to look down at the CD (note) that he’s holding as if considering a choice.genTracks: expands the description of the current CD into a list of the songs it contains.doSelect: select one or two tracks, based on the parameters given in the interaction. Say: verbally offer the selected song with the appropriate beak‐sync.

A state transition machine model with 5 conversational states and 17 input events is used.

[Ball 1997]

Dialogue management

[Bates 1994]

Believable agents

provides the illusion of life, and permits the audience’s suspension of disbelief. … critical in theater, film, animation, radio drama, …

Questions

i. What makes characters believable? What makes agents believable? Are agents different from characters?

ii. What is the nature of current integrated architectures and situated agents? How well do they support believability? Are there clear areas demanding study?

iii. How much breadth of capability is necessary to produce a believable agent? How much depth (competence) is necessary?

iv. What are the respective role of movement and language in achieving believability?

v. What is the role of context in establishing expectations in the user and thus is simplifying the task? For instance, how can the setting of an interface agent or the theme of a story help limit the technical requirements on agents?

vi. How can we measure believability and progress toward believability? Why don’t artists use “scientifically valid” techniques to evaluate the believability of their characters?

Believability

[Mateas 1997]

Drama = Character + Story + Presentation

Interactive Drama：Interactive drama concerns itself with building dramatically interesting virtual worlds inhabited by computer‐controlled characters, within which the user (the player) experiences a story from a first person perspective. … (Bates 1992)

Oz project

[Hayes‐Roth 1998]

Jennifer James

Text

Multi-modal dialogue engine

Graphics

Sound

Cloud

[Mateas 1997]

Oz project

PersonalityRich personality should infuse everything that a character does. What makes characters interesting are their unique ways doing things.

EmotionCharacters exhibit their own emotions and respond to the emotions of others in personality‐specific ways.

Self‐motivationCharacters have their own internal drives and desires which they pursue whether or not others are interacting with them.

Change Characters grow and change with time, in a manner consistent with their personality.

Social relationshipsCharacters engage in interactions with others in a manner consistent with their relationship. In turn, these relationships change as a result of the interaction.

Illusion of lifePursuing multiple, simultaneous goals and actions, having broad capabilities, and reacting quickly to stimuli in the environment.

Rea

[Cassell 1999]

Implements the social, linguistic, and psychological conventions of conversation.

‐ Has a human‐like body. Uses eye gaze, body posture, hand gestures, and facial displays to organize and regulate the conversation.

‐ The conversational model relies on the function of non‐verbal behaviors as well as speech.

‐ A full symmetry between input and output modalities: not only respond to visual, audio and speech cues (such as speech, shifts in gaze, gesture, and non‐speech audio but also generate these cues.

Rea

[Cassell 1999]

Discourse Model

Knowledge Base Word timing

Language Tagging

Behavior Scheduling

Text Input

Animation

Behavior Suggestion

Behavior Selection

Generator Set Filter Set

Behavior Generation

Translator

BEAT (Behavior Expression Animation Toolkit)

[Cassell 2004]

"Mary socked John.""Mary punched John.""Mary hit John with her fist.""John was socked by Mary.""Marie a donne un coup de poing a Jean.""Maria pego a Juan."

Story understanding and generation

Primitive Meaning

ATRANS Transfer of an abstract relationship (i.e. give)

PTRANS Transfer of the physical location of an object (e.g., go)

PROPEL Application of physical force to an object (e.g. push)

MOVE Movement of a body part by its owner (e.g. kick)

GRASP Grasping of an object by an action (e.g. throw)

INGEST Ingesting of an object by an animal (e.g. eat)

EXPEL Expulsion of something from the body of an animal (e.g. cry)

MTRANS Transfer of mental information (e.g. tell)

MBUILD Building new information out of old (e.g decide)

SPEAK Producing of sounds (e.g. say)

ATTEND Focusing of a sense organ toward a stimulus (e.g. listen)

[Schank 1975]

Action: PROPELActor: MaryObject: FistFrom: MaryTo: John

Margie


SAM (Script Applier Mechanism)[Cullingford 1981] Cullingford, Richard E: SAM and Micro SAM. In Roger C. Schank, & Christopher K. Riesbeck (Eds.), Inside computer understanding. Hillsdale, NJ: Erlbaum, 1981

FRUMP [Dejong 1979] DeJong, Gerald F.: Skimming stories in real time: An experiment in integrated understanding (Technical Report YALE/DCS/tr158). New Haven, CT: Computer Science Department, Yale University, 1979

Script‐based understanding


Plan‐based understandingPAM[Wilensky 1978] Wilensky, Robert: Understanding goal‐based stories (Technical Report YALE/DCS/tr140). New Haven, CT: Computer Science Department, Yale University, 1978.

POLITICS[Carbonell 1978] Carbonell, Jaime: Subjective understanding: Computer models of belief systems (Technical Report YALE/DCS/tr150). New Haven, CT: Computer Science Department, Yale University, 1978.

Dynamic MemoryIPP [Lebowitz 1980] Lebowitz, Michael : ¥Generalization and memory in an integrated understanding system (Technical Report YALE/DCS/tr186). New Haven, CT: Computer Science Department, Yale University, 1980.

BORIS[Lehnert 1983] Lehnert, Wendy G., Dyer, Michael G., Johnson, Peter N., Yang, C. J., Harley, Steve: BORIS ‐‐ An experiment in in‐depth understanding of narratives. Artificial Intelligence, 20(1), 15‐62., 1983.

CYRUS[Kolodner 1984] Kolodner, Janet L.: Retrieval and organizational strategies in conceptual memory: A computer model. Hillsdale, NJ: Erlbaum, 1984.

Story tellingTALE‐SPIN[Meehan 1981] Meehan, James: TALE‐SPIN and Micro TALE‐SPIN. In Roger C. Schank, & Christopher K. Riesbeck (Eds.), Inside computer understanding. Hillsdale, NJ: Erlbaum, 1981.

Summary

1. Conversational systems have a long history of development.2. Conversational systems evolved from natural language dialogue

systems. Multiple modality and embodiment have been incorporated, then.

3. The Knowledge Navigator gave a significant impact on the concept of intelligent virtual agents.

4. Personality, social interactions and lifelikeness are key features of intelligent virtual agents.

5. In order to create natural conversational behaviors, conversational artifacts need to coordinate verbal and nonverbal behaviors.

6. Story understanding and generation techniques are necessary for processing content.

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