AUGMENTED TRANSITION NETWORKS (ATNs) FOR DIALOGUE CONTROL: A LONGITUDINAL STUDY

Curry Guinn

Rob Hubal (RTI)

Outline

Architecture Applications Experiments Results

Virtual Human Architecture

Applications

Question-Answer Kiosks (Tradeshow for Tradeshows, Space Congress, John Deere, ASTD, APHA)

Training Applications» Bank Teller Training» Field Interviewer Trainer » Phone Interviewer Trainer» Informed Consent Trainer» Law Enforcement Training for Encounters with the Mentally Ill» Pediatrics Trainer

Profiler» Youth risk-assessment profiler» Prison inmate risk assessment profiler

Applications

(a) John Deere Kiosk(b) Bank Teller

Trainer(c) NIDA Assessment Tool

(d) JUSTTALK Officer Training

(e) Pediatrics Trainer(f) WTC – Informed

Consent

Augmented Transition Network Data Structure

Augmented Transition Network is a Finite State Machine with Variables» Nodes» Transitions» Variables

A B

C

If x < 2

If x >=2

Structure of a NodeName Unique identifier

Type Grammars to load

Grammar Response to say

Response Response to say

VR Command Message to send to VR world

Action Actions to perform (variable settings

Transition Name of node to go to if following conditional is true

Transition Action Actions to perform if transition is taken

Transition Condition Boolean expression

Augmented Transition Network Data Structure

wait_on_input /* Name */Normal /* Type */Grammar: "intro.gram default.gram"Response: "inform(offer_assistance)"VRString: "raise(eyebrows)"Action: "MENTOR = 0.8"Transition: proc_commandTransitionAction: "INPUT = command"Conditional: "command(CONTENT)"Transition: proc_queryTransitionAction: "INPUT = query"Conditional: "query(CONTENT)"

ATN Statistics In Order of Creation of Each System

Application SemanticsNetworkNodes

Normal(Speech)

Nodes

Average Transitions

Per Node

State Variables

Space Congress (Kiosk) 38 107 2 4.401869 35

Bank Teller 56 522 29 4.32567 98

ASTD (Kiosk) 51 216 3 4.430556 36

Door-to-door Survey 71 446 39 3.091928 119

Deere (Kiosk) 34 171 1 4.140351 38

Virtual Asthma Patient 52 355 20 2.309859 100

Telephone Survey 23 109 2 4.229358 58

NIDA StolenGoods 11 39 2 1.948718 56

NIDA Smoking 15 41 2 1.878049 56

JUSTTALK 81 284 2 4.169014 120

Pediatrics Teen 97 586 2 2.1843 120

WTC Informed Consent 66 227 2 4.911894 69

Semantic Complexity vs. Network Complexity

Semantics vs. Nodes

0

100

200

300

400

500

600

700

0 20 40 60 80 100 120

# of Semantics

Node

s

Variables Used

The linear increase is encouraging» Why? » You might expect that as

the number of semantics increase the network expands exponentially

» What’s holding this number down?

The use of variables

Application SemanticsState

Variables

Space Congress (Kiosk) 38 35

Bank Teller 56 98

ASTD (Kiosk) 51 36

Door-to-door Survey 71 119

Deere (Kiosk) 34 38

Virtual Asthma Patient 52 100

Telephone Survey 23 58

NIDA StolenGoods 11 56

NIDA Smoking 15 56

JUSTTALK 81 120

Pediatrics Teen 97 120

WTC Informed Consent 66 69

Decrease in the Number of Normal Nodes

The replication of normal (“speech”) nodes resulted in more repetitive networks

Used variables to store which grammars needed to be loaded next

Greatly decreases the complexity of the network

05

1015202530354045

The number of transitions as a measure of complexity

Over time, we tended to move towards reducing the inter-node connections

Why?» Easier to trace and

debug» Easier to maintain

This at the expense of large number of nodes

Application SemanticsNetworkNodes

Average Transitions Per Node

Space Congress (Kiosk)

38 107 4.401869

Bank Teller 56 522 4.32567

ASTD (Kiosk) 51 216 4.430556

Door-to-door Survey 71 446 3.091928

Deere (Kiosk) 34 171 4.140351

Virtual Asthma Patient

52 355 2.309859

Telephone Survey 23 109 4.229358

NIDA StolenGoods 11 39 1.948718

NIDA Smoking 15 41 1.878049

JUSTTALK 81 284 4.169014

Pediatrics Teen 97 586 2.1843

WTC Informed Consent

66 227 4.911894

Future Work

How do these empirical measurements correspond to “true” costs?

– Time to development– Reliability– Maintenance