Impact of deception information on negotiation
dialog management: A case study on
doctor-patient conversations
Nguyen The Tung*, Koichiro Yoshino, Sakriani Sakti, Satoshi Nakamura
Augmented Human Communication Laboratory
Nara Institute of Science and Technology, Japan

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Negotiation and negotiation system

BackgroundHow about I will do laundry and you will washthe dishes and clean the kitchen?
Hmm, I’m a little busy.
Can you do them all?
I’ve already washed thedishes yesterday. You
should at least clean thekitchen.

Okay, I’ll do it.

System (or agent) that persuades their interlocutor (a human user oranother agent) to agree on the system’s preferences by using the mostappropriate types of arguments – Georgilla & Traum (2011)
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Negotiation and deception

Background• Deception is a common strategy used in negotiation.
• Vourliotakis et.al 2014: an agent that can tell lies negotiates with arule-based adversary in a trading game scenario.
• Chance of winning the game of the agent will be lowered if theadversary can tell when the agent is lying.
 Deception information can be useful for the negotiation task.

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BackgroundDoctor  Patient conversation

UserPatient:• Perspectives• Opinions
Doctor:

System• Experience• Medicalknowledge

NEGO
TI
A
T
E

Treatment plan

Botelho (1992), Heaton(1981) dialog system can be used for Doctor-Patient conversation
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Problem with existing negotiation system

Existing negotiation systems assume thatthe user only speak the truth.

Problems &Contribution

Wrong!

Surveys* showed that about 23-38% of patienttell lies to doctor.
 Proposal: Dialog system knows when patient is lying and provides
appropriate reaction.
* Wall Street Journal (2009), Zocdoc (2015), WebMD (2004)…
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Problems &Contribution

Problems and Solutions

Problems

My solution

1. How can system detect user’s lie? 1. Detect the lie using facial andacoustic clues.
2. How does system react to user’slie?

2. Design system behavior to react touser’s lies.

3. How does system learn thisbehavior?

3. Modeled with POMDP and train byQ-learning

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Dialog domain: changing living habits
Working domain: • Sleepinguser’s living habits • Eating

General medical domain:• Many topics• Require medical knowledge• System’s goal: convince user to adopt a new living habit.
• User’s goal: keep the current habit.
running 3 times/week• Working• Exercising• Social media• Leisure activities

running 0 times/week

2 times/week

User’s goal

System’s goal
More health benefit
More difficult
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Less health benefit

Agreed habit

Less difficult

2018©Nguyen The Tung AHC-lab NAIST

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System’s general architecture

User

Input
utterances(verbal)

NLU*

non-verbal
information

Deception
detection

Output

NLG**

Policymanager

System* Natural Language Understanding** Natural Language Generation
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Dialogstate
tracker

2018©Nguyen The Tung AHC-lab NAIST

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1. Deception detection: multi-modal

Methods &Solution

pitch (F0)energyHirschberg et.al (2005)

classification

lie or truth?

face AU* values
head direction
head position
Pérez-Rosas et.al (2016)
5/16/2018* face AU: Face Action Unit(Eckman, 1978)2018©Nguyen The Tung AHC-lab NAIST

91. Classification using Multi Layer Perceptron (MLP)

Methods &Solution• MLP with single hidden layer, SGD, default Chainer parameters.
• 3 different models to combine facial and acoustic features: featureslevel, decision-level and hierachical (Tian et.al 2016).
• Hierarchical model accuracy is a bit higher than the others.
Combination model

Features-level

Decision-level

HierachicalAccuracy

61.75%

58.83%

64.71% Cannot make an exact conclusion about user’s deception.

5/16/20182018©Nguyen The Tung AHC-lab NAIST

10Methods &Solution

2. Baseline system’s behavior
Start

Framing:- Give information- Show benefits- Explain consequences

Offer

user’saction?

Hesitate
Offer

Framing

Question
Framing

AcceptEnd

RejectOffer

End

Normal negotiation system reacts based on user’s action only
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Methods &Solution

2. Proposed system’s behavior
Start

Offer
user’saction?

Hesitate
Offer

Question
Framing

Yes

RejectAcceptLie?

FramingNo
Framing

EndYes

Lie?
NoEnd

OfferSystem reacts based on user’s action + user’s deception.
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3. Learning using POMDP+Reinforcement
LearningMethods &Solution• System can only make estimation of user’s deception.
• System reacts based on user dialog state, which consists of bothuser’s action and user’s deception.
 Partially Observable Markov Decision Process was chosen.
• System learns by Reinforcement Learning (RL), used in previous work
(Georgilla & Traum, 2011.)• Q-learning with grid-based Iteration method by Bonet (2002).
• State transition:
s: use’s dialog act
d: user’s deception

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13Methods &Solution

3. Reward definition
rewardaSystem

Users ,dReinforcement Learning

Dialog state
UserDeception
action (s)(d) *Accept0
1
Reject0
1Question
0Hesitate0Offer-10-10+10-10-10+10Action a
Framing-10+10-10+10+10+10End+100-100-100-100-100-100*: 0 – honest
1 – deceptive

Using RL, system can learn the designed strategy while trying to successfully
persuade the user.
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Evaluation

Dialog corpus
Recorded using “changing living habits” scenario.
Train

TestParticipants

7 participants
4 played system
6 played userDuration#of dialogs
Avg. turns per dialog

3 hours 20 minutes
29 dialogs
5.72 turns/dialog

2 hrs 35 minutes
30 dialogs
4.73 turns/dialog

Record set up

Wizard-of-Oz

Direct conversation• Trained using simulated user (Yoshino & Kawahara, 2015)• Simulated user: generates dialog act and deception using probabilities
calculated from Train data.
• Q-learning with learning rate: 0.1, discount factor: 0.9, exploration: 0.2  0.01;
100,000 dialogs.
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Experiment #1: Against simulated user
Dialog system

Evaluation% Success dialogs Avg. offer per succeeded
dialogW/o deception
21.83%
2.472(baseline)
With deception
29.82%
2.447(proposed)• Systems interacted with SU created from test data.

• % success dialogs: higher is better.
• Avg. offer per succeeded dialog: lower is better.

 proposed system behavior performs better.
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Experiment #2: System DA selection
Dialog system

System DA selection
accuracy

Deception
Handling *Baseline
68.15%
35.00%
Proposed system
80.45%
80.00%(annotated deception)
Proposed system
79.32%
55.00%(predicted deception)• Evaluate turn-by-turn• Human expert selects best reaction in each dialog turn (based onannotated user’s action and user’s deception)• Accuracy is measure by comparing system’s choice with humanchoice for each dialog turn.
 proposed system performs better.
5/16/20182018©Nguyen The Tung AHC-lab NAISTEvaluation* System DA selection
accuracy on the turns
when user is lying

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Conclusion &Discussion

Conclusion

Conclusion:• In this work, we investigated the problem of lying in negotiation using theDoctor-Patient conversation.
• Proposed a dialog system that detects user’s lies and uses this information for
dialog management.
• Evaluation results shows that proposed system outperforms normal
negotiation system by 8% in term of negotiation success rate and 11% in termof system dialog act selection.
Future works:
 Use ASR (speech recognition) and NLG (sentence generation) technology.
 Evaluation with human user.
5/16/20182018©Nguyen The Tung AHC-lab NAIST

18References• Georgila, Kallirroi, and David Traum. "Reinforcement learning of argumentation dialogue policies innegotiation." Twelfth Annual Conference of the International Speech Communication Association. 2011.
• Botelho, Richard J. "A negotiation model for the doctor-patient relationship." Family Practice 9.2 (1992): 210-218.
• Heaton, P. B. "Negotiation as an integral part of the physician's clinical reasoning." The Journal of family practice 13.6(1981): 845-848.
• Iihara N, Tsukamoto T, Morita S, Myoshi C, Takabatake K, Kurosaki Y, Beliefs of chronically ill Japanese patients thatlead to intentional nonadherence to medication. Journal of Clinical Pharmacy and Therapeutics 2004; 29: 417-424.
• Simmons MS, Nides MA, Rand CS, et al. Unpredictability of deception in compliance with physician-prescribedbronchodilator inhaler use in a clinical trial. Chest. 2000;118(2):290–295.
• Horne, Rob. "Compliance, adherence, and concordance: implications for asthma treatment." Chest 130.1 (2006):65S-72S.

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References• Ekman, Paul, and Erika L. Rosenberg, eds. What the face reveals: Basic and applied studies of spontaneous
expression using the Facial Action Coding System (FACS). Oxford University Press, USA, 1997.
• Tian, Leimin, Johanna Moore, and Catherine Lai. "Recognizing emotions in spoken dialogue withhierarchically fused acoustic and lexical features." Spoken Language Technology Workshop (SLT), 2016 IEEE.
IEEE, 2016.
• Kjellgren, Karin I., Johan Ahlner, and Roger Säljö. "Taking antihypertensive medication—controlling or cooperating with patients?." International journal of cardiology 47.3 (1995): 257-268.
• Hirschberg, Julia, et al. "Distinguishing deceptive from non-deceptive speech." Ninth European Conferenceon Speech Communication and Technology. 2005.

• Pérez-Rosas, Verónica, et al. "Deception detection using real-life trial data." Proceedings of the 2015 ACM onInternational Conference on Multimodal Interaction. ACM, 2015.

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3. Grid-based iteration (appendix)

Methods &Solution• Infinite number of belief states which makes the problem intractable.
• Grid-based Iteration method by Bonet (2002):• The probabilities are quantized into {0.0, 0.1 … 1.0}𝑏 = ( 𝐴: 0.147, 𝐻: 0.386, 𝑄: 0.235, 𝑅: 0.232 , 𝐿: 0.735, 𝑇: 0.265 ) 𝑏 ′ = ( 𝐴: 0.2, 𝐻: 0.4, 𝑄: 0.2, 𝑅: 0.2 , 𝐿: 0.7, 𝑇: 0.3 )5/16/20182018©Nguyen The Tung AHC-lab NAIST

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Methods &Solution

1. Classification using Multi Layer Perceptron (appendix)
facial…
facial +acoustic

…

…

…

…

…
acoustic…

…

features-levelcombination

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facialdecision-levelcombination2018©Nguyen The Tung AHC-lab NAIST

acoustic…

hierachical combination
Tian et.al (2016)22