## 3 Question-Answer-Response Dialogue Model
### Utterance Selection in Each State  
Every state (except for the answer state) has a set of objects for the robot utterances and an algorithm for selecting an object from the set. The question state has a set of objects shown in the Listing 1. When the system transitions to the question state for the first time, it selects an object among the set randomly. After the second time, it selects an object with the same topic as the previous question among the set, except for objects that have already been selected. The reason for maintaining the same topic is that it would be unnatural to change topics with every question. However, to prevent the user from getting bored, the topic is changed every four times. ```
1[
2{
3 Question: \"Haveyoueverbeenabroad?\",
4 Topic: \"travel\"
5},
6{
7 Question: \"Wheredoyouwanttotravelto?\",
8 Topic: \"travel\"
9},
10{
11 Question: \"Whatwasyourfavoritefoodinchildhood?\",
12 Topic: \"childhood\"
13},
14...
15
16
17
18
19
20
21 Question: \"Whatwasyourfavoritefoodinchildhood?\",
22 Topic: \"childhood\"
23},
24...
25
26
27
28
29
30 Question: \"Whatwasyourfavoritefoodinchildhood?\",
31 Topic: \"childhood\"
32},
33
34
35
36
37
38
39
40
41
42
431
442
453
46
47
48
491
502
503
504
5055
506
507
508
5091
5101
5111
5112
5113
5114
5115
5116
5117
5118
519
520
The comment state has the same set of objects as in the backchannel state (Listing 3). The selection algorithm is also the same as that of the backchannel state. ```
1{
2{
3 Question: \"Haveyoueverbeenabroad?\",
4 Keyword: [\"yes\", \"yeah\"]
5 Backchannel: \"Cool!\"
6},
7{
8 Question: \"Haveyoueverbeenabroad?\",
9 Backchannel: \"Isee.\"
10},
11{
12 Question: \"Wheredoyouwanttovisit?\",
13 Keyword: [\"kyoto\", \"tokyo\", \"osaka\"]
14 Backchannel: \"Yeah.\"
15},
16...
17}
```

Listing 3: Objects in the comment state. ## 4 Participation of Multiple Robots

Although the question-answer-response dialogue model is expected to be robust against speech recognition failures, the dialogue generated by the model might be tedious for users. This is because the dialogue system is a one-way model; the robot asks a question, the user answers it, and the robot responds to the answer. If this continues for a while, the user might feel bored and stop the dialogue early. We let two robots participate in the dialogue to decrease the tediousness of the model. There have been reports of advantages of using multiple robots in a dialogue. For example, when multiple robots participate in a conversation, a user seems to become insensitive to unnaturalness about consistency in a dialogue [46; 47]. In addition, the user tends to feel that he or she can talk easily with multiple robots [48; 49] and the user is likely to experience eye contact with the robots [50]. [PERSON] et al. [51] developed a multi-agent system that interacts with a driver in a car and showed that using multiple agents reduces cognitive loads of the driver compared to using a single agent. [PERSON] et al. [52] conducted a field trial in which two robots provide information to passersby at the station and found that passersby were more likely to stop when two robots were talking than when a single robot was talking. [PERSON] et al. [47] demonstrated that visitors in an exhibition hall tended to have a longer dialogue with multiple robots than a single one. In this study, we defined the participation framework of a dialogue in which two robots and one user participated, according to [PERSON] [53]. The participation framework contains a speaker, an addressee, and a side-participant. The turn-taking system was implemented based on [PERSON] et al. [54]. The rules of turn-taking of each state in our system are described below. In the question state, the rule of selecting a speaker differs between the first time and the second time or after. In the first time, either of the robots is selected as a speaker randomly. Another robot becomes a side-participant. After the first time, the speaker of the previous question is selected as a speaker.