An Online ADR System Using a Tool for
Animated Agents
Masahide Yuasa, Takahiro Tanaka, Yoshiaki Yasumura, Katsumi Nitta
Department of Computational Intelligence and Systems Science,
Tokyo Institute of Technology, Japan{yuasa, takat, yasumura, nitta}@ntt.dis.titech.ac.jp
Abstract: We describe an online ADR (Alternative Disputation Resolution) System using a toolfor generating facial expressions of animated agents. In this system, a participant of ADR disputes
against an opponent through a computer network. The participant agent searches similar casesof arguments and supports the participant by showing the similar cases. The participant edits
the next proposal by referring to the cases. And the agent learns the participant’s tendency toselect facial expressions, based on an emotional model, and generates the facial expression. The
participant can reduce the cost of the dispute by using the participant agent.
Keywords: animated agent, human interface, facial expression, Alternative Disputation Resolution, case based reasoning

1. Introduction
An ADR is a method of resolving disputes without trial.
A mediation, an arbitration and a conciliation are typical ADRs. As an ADR can resolve a disputation fasterand cheaper than a trial, the demand for ADR increases.
Recently, online ADR systems have been developed inorder to deal with more demands. These systems receive
consultations by e-mails, and disputations are solved byexchanging e-mails between a consultant and a client.
Sometimes consultations go into the mediation where
two clients (participants) and the consultant (mediator)
exchanges emails until agreement is achieved.
Current online ADRs have several problems. At first,
the participants are not used to online discussions. Online discussions are performed by exchanging only texts,
which is different from face-to-face discussions. Usually,
it takes much time to reach agreement because participants don’t have non verbal information.
Secondly, participants don’t have enough information
about the trouble. If they have knowledge about legal
rules or commercial customs, the disputation will besolved faster.
To deal with these problems, we developed an online ADR system, AMAS (agent mediated ADR supportsystem) which is navigated by agents. This system consists of two parts, an online discussion environment, anda participate agent. The discussion environment has auser interface where non verbal information is transferred by the facial expression of an animated agent. A
participant agent helps the participant to make arguments by referring to old cases. This agent controls thefacial expression of the animated agent instead of theparticipant in detail.
Generally, people use two kinds of facial expressions
in negotiation 1) . The one is an unintentional facialexpression, which reflects a human’s emotional state directly. The other is an intentional facial expression,

Fig. 1: Overview of AMAS
which is controlled intentionally according to some negotiation strategy. We assume that the facial expressions depend on each personality of a human. Therefore, we developed TAA (a Tool for Animated Agent),
which learns the personality of the participants by
Bayesian network, and generates unintentional facial expressions automatically.
In Section 2, we describe an overview of our online
ADR system. Section 3 presents a participant agent,
which assists participants. The emotional model in thearchitecture and experimental results are shown in Section 4. Section 5 shows an example of searching a similar case and how to use it. Finally, Section 6 is theconcluding remarks.

2. Overview of AMAS
In this section, we describe an overview of AMAS. The
process of AMAS consists of three phases. In phase One,the system receives a trouble case from a client, and it
gives an advice to resolve a trouble by showing a suitableflowchart. Fig.2 shows an example of a flowchart. The
participant answers questions then the system shows
an explanation. These flowcharts are constructed byconsultants from old cases.
If the client are not satisfied the result of theguments and demands each other. And the mediator
checks whether the participant’s response is suitable
or not, using negotiation rules. The mediation is performed based on the arguments and demands submitted by both participants. Secondly, the mediator sends
the mediation proposal. The mediation will be finishedwhen both of participants agree the mediation proposal.
Besides above mediation process, the participants canuse the participant agent which assists them. The participant agent sends proposals and facial expressions tothe opponent. By using the participant agent, the participant can reduce a cost of dispute.

3. A Participant Agent

Fig. 2: Flowchart

3.1

Architecture of a Participant Agent

The participant agent supports the participant in mediation. The participant agent has three functions. The
first is to generate the unintentional facial expressions
automatically. The intentional facial expressions arecontrolled by a participant directly. The second is toprovide the participant with similar old cases. The third
is to make proposals by following the participant’s instruction.
• Unintentional and Intentional Facial ExpressionFig. 3: Screenshot of AMAS
flowchart, the process goes into phase Two. The client
consults with a consultant by exchanging emails. In themost cases, ADRs finish in this phase.
If the clients aren’t still satisfied, the ADR goes into
phase Three. The consultant sends an email to the opposite person, and if he agrees, the mediation process
starts. The system provides a negotiation table thatenables an agent to mediate between two parties via anetwork. Fig.1 shows an overview of phase Three ofAMAS. Two participants dispute through a computer
network by exchanging proposals, opinions, or assertions, with facial expressions each other. A consultant(mediator) moderates the dispute between two participants. The mediator also makes some mediation proposals and sends them.
Fig.3 shows a screenshot of an interface for the participant of phase Three. AMAS system shows the proposals, opinions, assertions and facial expressions of theparticipants on the screen. The mediator controls thefacial expression of the animated agent in the center,the opponent controls the right one, and the participant controls the left one. The participant decides thenext proposal and the next facial expressions, and sends
them to the opponent.
In the mediation, at first, the participants send ar-The agent generates the unintentional facial expressions based on the emotional model, automatically. In order to generate them, the agent learns
the user’s pattern of the unintentional facial expressions. And the user selects an intentional facialexpression when the user sends the next proposal.
• Provide Information
The participant agent provides the participant withinformation from similar old cases. This agent
searches the similar old case which has the samefeatures of current argument.
• Make a Mediation Proposal
The participant agent makes some mediation proposals from similar old cases. The participant selects and edits a proposal from the proposals thatthe participant agent makes.

3.2 Modules
Fig.4 shows an architecture of a participant agent.
The participant agent consists of Message Management
Module, User Interface, Similar Case Retrieval Module,
Evaluation Function Module and TAA module. We describe each module in detail.
• Message Management ModuleThis module exchanges messages (a proposal andfacial expressions) between an opponent and thisagent. When a proposal and facial expressions

Fig. 5: Emotional States for B
TAA has two modes, a learning mode and an automatic mode. In the learning mode, TAA learns
the user’s tendency of selecting the unintentional
facial expression, and constructs the user’s emotional model. In the automatic mode, TAA works
as a part of an agent and generates the unintentional facial expression instead of the user basedon user’s Emotional Model.

Fig. 4: Overview of Participant Agent
come from the opponent, this module sends to Similar Case Retrieval Module and Evaluation Function Module. And this module sends a message
from the user to the opponent.
• Similar Case Retrieval Module
When this module gets the opponent’s proposal
and facial expression, this module searches similarcases in old cases database, then shows the candidates of proposal from the result of search to theparticipant. This module searches the cases which
are similar to current dispute in old cases database.
All cases have some features about detail of the dispute. We analyzed the old cases and selected somekey features. These key features are used as an index of each case. This module judges that two cases
which have the same features are similar cases.
• User Interface
The participant connects an AMAS server via theInternet with this module. And the participant inputs his/her proposal and facial expression by using
this module. This module displays the opponent’s
agent and shows the proposal and facial expressionsof the opponent. When the participant inputs proposal, this module shows candidates of proposal
selected by Similar Case Retrieval Module. The
participant can select and edit this candidate as
the next proposal.
• Evaluation Function ModuleThis module receives the message from Message
Management Module, and outputs a subjectivevalue as a number from -1 to 1 of the proposal.
The evaluation value is sent to TAA Module• TAA Module

4. Emotional Model in TAA
In this section, we describe an emotional model to selectan unintentional facial expression.

4.1

ABX Model

The emotional model in TAA is based on Newcomb’s
ABX model2) (Fig.5). The ABX model describes emotional states among two participants and one proposal.
The relation among two of these entities is indicated bya plus sign, minus sign, or 0. The plus sign means aperson has a positive feeling. The minus sign means aperson has a negative feeling. The 0 means intermediate
state.
In this model, each state is categorized into unbalanced states and balanced states. If one person has
both positive relation for one entity and negative relation for another entity, then the state is unbalanced.
Otherwise, the state is balanced. The unbalanced states
are unstable and tend to move to the balanced states.
In the case of negotiation, we regards A and B asparticipants, and X as a current proposal. And we viewarrows from participants to X as evaluation values of a
proposal by participants3, 4) .
During the negotiation, when a participant receives a
proposal from the opponent, the state transition occurs.
When a participant A offers a proposal X, A must like
X. Therefore, just after A receives B’s proposal X, A
and B’s emotional state must be one of 9 states of Fig.5.
The state (S1) and state (S9) are unbalanced states andthe other balanced states.

Fig. 8: Facial Expressions
Fig. 6: Generation of Facial Expressions by Bayesian
NetworkFig. 7: Tool for Price Negotiation

4.2

Generation of Facial Expressions
Using Bayesian NetworkWe assume that the participant’s emotional state transits from one state to another state with some probability, when the participant receives a new proposal anda facial expression. And one of unintentional facial expressions is generated with some probability, when thetransition occurs. We use Bayesian Network in order tolearn the probabilistic relations (Fig.6). The networkshows that the transition from a previous emotional
state (P3) to a current emotional state (P4) when theuser receives a new proposal (P1) and a facial expression (P2). And a facial expression is generated when
the transition from P3 to P4 occurs (P5).
These probabilities depend on a personality of a human. An emotional model is defined as probabilitiesof state transitions and generating unintentional facialexpressions. To construct the emotional model, TAA
needs to learn these probabilities from a participant.

4.3

Learning Emotional ModelWe carried out the experiment of learning the emotionalmodel in TAA. At first, we acquired probabilities byobserving users’ selections in an experiment using pricenegotiation. Next, we compared a selection of the users’
and that of TAA.
The interface of learning is shown in Fig.7. Thisshows an operating panel and facial expressions of anopponent on the screen. The user selects a proposal

Fig. 9: The case of Proposal(+) and Facial Expression(HAPPY)and an unintentional facial expression in the panel andthey are sent from the user to the opponent in turn.
Fig.8 shows facial expressions that the user can select(i.e., COOL, ANGRY, HAPPY, SAD, SURPRISED).
We used MFACE5) to display facial expressions. In thisnegotiation, users play a role of buyer and a program
plays a role of seller, but users don’t know whether theseller is human or not. And users must input an evaluation (+, 0, or -) of a feeling against the opponent on
the panel.
Eleven students conducted the negotiation game.
And Fig.9 shows a sample result after the tool learned
the probabilities of transitions among emotional statesand generations. Probabilities in Fig.9 means existenceprobabilities of the each states and facial expressions.
We used a Bayesian Network Tool ”Netica” to calculate existence probabilities6) .
The upper right part of Fig.9 shows the existence
probabilities of ’current emotional states’ when theuser received the proposal(+) and the facial expressions(HAPPY), at the state was ’previous emotionalstates’. And the lower part shows the existence probabilities of facial expressions at that time. In that case,the probability of S3 has a 86.7% and the probabilityof HAPPY has a 79.7%. This means that when theTable 3: Example of Similar Cases
CaseProposal

Current

Opponent’s proposal

Case1

Opponent’s proposal

Counter proposal

Context
I’d like to [f31]return the [f3]old doll.
I found that the left hand [f21]broke down.
I think that this is a [f24]defective product.
[f25]The doll’s photograph in the auction site is not clear.
[f25]You didn’t explain that.
Please give me a [f33]refund.
I want to [f31]return the note PC for a full [f33]refund.
[f24]It doesn’t work. It’s a [f24]defective product.
I found that there was something wrong with the battery.
You should have show the flaw.
I can’t accept the [f31]return.
I think that you should buy the battery.
The battery is a only part of PC and you can [f5]exchange it with new one.

Table 1: Accordance of Model’s Selection and User’s
Selection for Facial Expressions
A-A
73.0 %

B-B
68.5 %

A-B
48.8 %

B-A
19.4%

Table 2: List of Key Features
Topic - Internet auction trouble
Content
Mass-produced item
Rare item
Used item
Expensive item
Item with exchangeable parts
Case
Breakdown
Need repair
ScratchDefectImproper explanation
Don’t send item
Don’t pay money
Inputting error of auction price
Requirement Return
RefundRepairApology
Return auction charge
Category
Item

Code
f1
f2
f3
f4
f5
f21
f22
f23
f24
f25
f26
f27
f28
f31
f33
f34
f35
f36

user receives a good proposal and good facial expressions, the state tends to transit a better one, and theuser selected the HAPPY in many time. As above, ouremotional model is a good model which can consider
the feelings for the opponent and the transitions of theemotional states.
Next, we show the effects of learning by the following experiment. In this experiment, we selected twostudents, A and B. At first, we constructed two emotional models, which are gained by both students’ negotiation. Then both students continued the negotiation,

and we compared facial expressions which both students
selected with what selected automatically using thesemodels.
Table 1 shows the result. In this table, ’A-B’ meansthat we compared the TAA’s selection with B’s selection, after TAA learned the pattern of A. TAA matched
the A’s selection by 73.0% when TAA learned the pattern of the A. In the same way, it matched the user’s selection by 68.5% in case of B-B. However, in case of B-A,
it matched by 48.8% and in case of A-B, it matched byonly 19.4%. These result shows TAA learns the user’s
pattern for each individuals and generates the unintentional facial expression appropriately.

5. An Example of Searching Similar Cases
In argument, it is important to know which subject theparticipants should talk about. We made a list of keyfeatures, which shows what the participants should argue. Table 2 shows a part of the list of key features,
which are extracted from Internet auction cases. Basedon the list, we can know the contentions of the proposal.
We define that a similar case is a case which includes same contentions of a proposal. The participant agent searches the similar cases, based on thekind of contentions in the proposal. When the agent
finds the similar case, it shows the proposal and thecounter proposal of the case. Table 3 is an example of found cases. This table shows the case when
the opponent claimed that the opponent’s producthad a problem. In this example, the opponent’s utterance includes [f31]return, [f3]used, [f21]broke down,[f24]defective product, [f25]explain, [f33]refund.
The agent searches the similar case by using thesecontentions. There are similar cases, Case1, in the table. And by using the similar cases, the participant cancompose the counter proposal ”exchange the part of dollwith new one.”, based on the utterance of [f5]exchange

in the example counter proposal. The participant edits
the next proposal and sends it.

6. Conclusion
We introduced an online ADR system and the participant agent. The agent helps the participants by showing
the similar old cases. The agent also uses facial expressions of an animated agent to dispute with the opponent
in real time. It generates facial expressions based on theemotional model of the participant.
In the future, we will use this agent for another systems. We will apply the agent to a moot court support
system of law education.

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