A Knowledge Chunk Reuse Support Tool

based on Heterogeneous Ontologies

Takeshi Morita

1 a

, Naoya Takahashi

, Mizuki Kosuda

and Takahira Yamaguchi

Faculty of Science and Technology, Keio University, Yokohama, Japan

Graduate School of Science and Technology, Keio University, Yokohama, Japan

Keywords:

Ontology, Knowledge Chunk, Teaching Assistant Robot.

Abstract:

To develop service robot applications, it is necessary to acquire domain expert knowledge and develop the

applications based on the knowledge. However, since, currently, many of these applications have been devel-

oped by engineers using the middleware for robots, the domain expert knowledge is embedded in the codes

and is difﬁcult to reuse. Therefore, it is considered necessary to have a tool that supports the development of

the applications based on machine-readable knowledge of domain experts. We also believe that the machine-

readable knowledge can be reused not only for service robots but also for novices in the domain. To address

the problems, this paper proposes a knowledge chunk (KC) reuse support tool based on heterogeneous ontolo-

gies. In this study, the parts of the reusable workﬂow, indexes required for a search, and a movie recording

of robots movement based on the parts of the workﬂow are collectively known as a KC. Using the framework

of case-based reasoning, the proposed tool accumulates parts of reusable workﬂows as case examples based

on heterogeneous ontologies and facilitates search and reuse of KCs. It promotes domain expert knowledge

acquisition and supports novices to learn the knowledge. As a case study, we have applied the proposed tool to

teaching assistant (TA) robots. Two public elementary school teachers created workﬂows for TA robots using

the proposed tool, and each teacher conducted a lesson with TA robots once. Through questionnaires given to

the teacher, the proposed tool and TA robot application were evaluated to conﬁrm their usefulness.

1 INTRODUCTION

In recent years, the development of service robot ap-

plications has attracted considerable attention in the

service industry. To develop service robot applica-

tions, it is necessary to acquire domain expert knowl-

edge and develop the applications based on the knowl-

edge. However, since, currently, many of these appli-

cations have been developed by engineers using the

middleware for robots such as Robot Operating Sys-

tem (ROS)

, the domain expert knowledge is embed-

ded in the codes and is difﬁcult to reuse.

Therefore, it is considered necessary to have a

platform that supports the development of service

robot applications based on machine-readable knowl-

edge of domain experts using ontologies, workﬂows,

rules, etc. It is also considered that the machine-

readable knowledge of domain experts can be reused

not only for service robots but also for novices in the

https://orcid.org/0000-0001-8963-2562

http://www.ros.org/

domain. In the model of skill acquisition proposed by

(Dreyfus et al., 1986), learners acquire skills through

ﬁve distinct stages: novice, advanced beginner, com-

petence, proﬁcient, and expert. From this point of

view, we believe that the platform should be able to

support the novice and advanced beginner’s learning

by reusing the knowledge of the proﬁcient and expert.

Based on this, we have been engaged in the study

and development of PRINTEPS (Yamaguchi, 2015),

an AI and service robot application development plat-

form. In the PRINTEPS, users can develop appli-

cations based on ontologies, workﬂows, and rules,

and these machine-readable knowledge can be shared

and reused. So far, we have applied this platform to

teaching assistant (TA) robots at multiple elementary

schools as case studies (Morita et al., 2018).

PRINTEPS provides a scenario editor to describe

the workﬂows for robots’ actions and human-robot in-

teractions, and helps develop service robot applica-

tions that are executable on ROS. The scenario editor

has several separate transverse lanes allocated to the

corresponding agent of implementation, consisting of

Morita, T., Takahashi, N., Kosuda, M. and Yamaguchi, T.

A Knowledge Chunk Reuse Support Tool based on Heterogeneous Ontologies.

DOI: 10.5220/0008067002170224

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 217-224

ISBN: 978-989-758-382-7

217

actors and tools, so that implementation procedures

can be deﬁned for each lane. With the scenario ed-

itor, workﬂows can be created while simultaneously

overviewing the interaction between actors.

The scenario editor enables the developer to create

workﬂows while reusing the entire workﬂows or indi-

vidual operation processes; however, since end users

such as domain experts usually do not know functions

and properties of robots, it is difﬁcult to identify and

modify the reusable parts to suit the purposes of reuse

when reusing the entire workﬂows. In contrast, it is

also difﬁcult to work out different combinations of

operation processes when reusing the individual op-

eration processes.

To address problems associated with workﬂow

creation and reuse of processes using the PRINT-

EPS scenario editor, this paper proposes a knowledge

chunk (KC) reuse support tool based on heteroge-

neous ontologies. Using the framework of case-based

reasoning (Kolodner, 1997), the proposed tool accu-

mulates parts of reusable workﬂows as case exam-

ples based on heterogeneous ontologies and facilitates

search and reuse of KCs. It promotes domain expert

knowledge acquisition and supports novices to learn

and reuse the knowledge.

As a case study, we have applied the proposed tool

to TA robots. Based on the workﬂows of TA robots

given to several elementary schools, we created on-

tologies of teacher knowledge (teaching materials and

methods), robot, and KCs and then established a KC

base (KCB) based on these heterogeneous ontologies.

Two public elementary school teachers created work-

ﬂows for TA robots using the proposed tool, and each

teacher conducted a lesson about a science learning

unit “movement of pendulum” with TA robots once.

Through questionnaires given to the teachers, the pro-

posed tool and TA robot application were evaluated to

conﬁrm their usefulness.

2 KNOWLEDGE CHUNK REUSE

SUPPORT TOOL

In this section, we explain the overview of the knowl-

edge chunk (KC) reuse support tool based on htero-

geneous ontologies. A chunk is deﬁned on page

12 of reference (Tulving and Craik, 2000) as a fa-

miliar collection of more elementary units that have

been interassociated and stored in memory repeat-

edly and that act as a coherent, integrated group when

retrieved. In this study, the parts of the reusable

workﬂow, indexes required for a search, and a movie

recording of robots movement based on the parts of

the workﬂow are collectively known as a KC. The

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Figure 1: The system overview of the proposed tool.

database storing the KCs is called the KCB.

Fig. 1. shows the system overview of the proposed

tool. Using the framework of case-based reasoning,

the proposed tool accumulates KCs as case exam-

ples based on application domain ontology, robot on-

tology, and KC ontology to facilitate the search and

reuse of KCs from the viewpoints of promoting ap-

plication domain knowledge acquisition and robot uti-

lization. The proposed tool was implemented by ex-

panding the PRINTEPS scenario editor.

The steps to reuse KCs using the proposed tool are

as follows. Developers ﬁrst create application domain

ontologies by interviewing domain experts. Then, the

part of the workﬂow corresponding to the KC is ex-

tracted, and the corresponding part of the movie is

edited out. Based on the KC ontology, the developers

create instances of KC classes and assign IDs to re-

lated operation processes, links to movies, and search

indexes based on the application domain ontology.

The user of the proposed tool can search the KCB us-

ing the search index created based on the application

domain ontology and the robot ontology from the sce-

nario editor. A list of KCs matching the search condi-

tions will be displayed, and the selected KC that one

wants to reuse is inserted in the scenario editor so that

the user can edit the inserted workﬂow and reuse it

afterward.

Currently, the proposed tool only supports end

users to search and reuse of the knowledge chunk.

The ontologies and knowledge chunk are manually

created and stored by the developers.

3 ONTOLOGIES

3.1 Overview

The KCB is built based on the application do-

main, robot, and KC ontologies. These ontologies

were built in the form of web ontology language

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

218

(OWL)

using the Ontology Editor Prot ´eg ´e

. We

have deﬁned 661 axioms, 26 classes, 10 properties,

and 83 individuals in these ontologies.

3.2 Application Domain Ontologies

Application domain ontologies organize concepts and

relationships for target application domains. As this

study conducts lessons with teaching assistant (TA)

robots as a case study, teaching material and method

ontologies consisting of systematized teacher knowl-

edge were created as application domain ontologies.

3.2.1 Teaching Material Ontology

The teaching material ontology organizes teaching

material knowledge related to learning units of each

subject based on the school curriculum guidelines set

by the Ministry of Education, Culture, Sports, Sci-

ence, and Technology (MEXT). Fig. 2. shows the

class hierarchy and part of the instances of teaching

material ontology. We deﬁned the “Learning unit”

class as the root class and the “Learning unit” class

of each subject (such as “Science learning unit” class)

as the subclass of the root class. We also deﬁned the

ﬁeld learning unit class as a subclass of each subject

learning unit, such as the “Learning unit of dynam-

ics ﬁeld” class. Each deﬁned class contains learning

units that are taught such as “Regularity of a lever”

and “Movement of pendulum.”

This study selected an elementary school science

lesson as a case study and therefore created teaching

material ontology referring to “Content Classiﬁcation

of Science” of “Explanatory Manual of Elementary

School Curriculum Guideline for Science Courses”

provided by the MEXT.

3.2.2 Teaching Method Ontology

The teaching method ontology organizes the conﬁg-

uration for promoting teaching methods, such as its

introduction and summary. Fig. 3. shows class hierar-

chy and part of the instances of the teaching method

ontology. We deﬁned the “Teaching method” class

as the root class and the teaching method class for

each subject as the subclass of the root class. In addi-

tion, we also deﬁned the “Subject-independent teach-

ing method” class that was common to all subjects, in

which instances as “Starting and introducing the les-

son” and “Summarizing and ending the lesson” were

deﬁned.

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ing method ontology.

The study deﬁnes instances of the “Teaching

method for science” class based on the guide issued

by the MEXT, which is used by the teachers that took

part in the case study.

3.3 Robot Ontology

The robot ontology organizes the functions of a robot

and interactions between robots and people (in this

case, teachers and pupils). From the results of the

questionnaires given to the pupils and teachers on the

lessons with TA robots in our previous case studies,

it can be concluded that a lesson workﬂow in which

there are interactions between teachers and robots or

between robots and pupils, rather than a lesson work-

ﬂow in which lessons are given only in one direc-

tion from the teacher or robot to the pupils, leads to a

higher evaluation score by the teachers and pupils.

Fig. 4. shows the class hierarchy and part of the

instances of the robot ontology. In the robot ontol-

ogy, the “Use of robot” class was deﬁned as the root

class and the “Use of robot involving interaction” and

“Use of robot having received favorable responses”

A Knowledge Chunk Reuse Support Tool based on Heterogeneous Ontologies

219

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ontology.

Table 1: Properties of the knowledge chunk ontology.

Property name Domain Range

learningUnit Knowledge chunk Learning unit

teachingMethod Knowledge chunk Teaching method

robotUsage Process Use of robot

hasProcess Knowledge chunk Process

processId Process String

chunkFile Knowledge chunk String

chunkMovie Knowledge chunk String

classes were deﬁned as the subclasses. From the pre-

vious workﬂows, the interactions between robots and

humans were extracted, and then only the workﬂows

which pupils showed favorable responses were named

and deﬁned as instances for each class, including “Ap-

proach from robots to pupils.”

3.4 Knowledge Chunk Ontology

The KC ontology organizes the relationship between

the processes constituting the KC and application do-

main ontologies. Fig. 5. shows KC classes and the

instances used in the KC ontology. The “Knowl-

edge chunk” class and “Process” class were deﬁned

as classes for the KC ontology. KCs are deﬁned as

instances of the “Knowledge chunk” class. The pro-

cesses of the PRINTEPS scenario editor are deﬁned

as instances of the “Process” class.

Table 1 lists the properties of the KC ontology.

The “learningUnit” and “teachingMethod” are prop-

erties connecting KC to target learning unit and teach-

ing method. The “robotUsage” is a property connect-

ing process to target use of robot. The “hasProcess”

is a property connecting KC to the process related to

use of robot. The “processId” is a property indicating

the ID of the relevant process in the scenario editor.

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Figure 5: Knowledge chunk classes and instances of the

knowledge chunk ontology.

The “chankFile” is a property connecting KC to cor-

responding chunk ﬁle including process set that con-

ﬁgures KC, its connection relation, and related mod-

ule information used in those processes.

For teachers who do not know robots, since it is

difﬁcult to create a clear picture of robot movements

by just referring to the workﬂow and reading the text,

we prepared the “chunkMovie” property that con-

nected the KCs with the corresponding movie ﬁles.

We also aimed to encourage the reuse of KCs by al-

lowing the teachers to learn from effective ways of the

movies, which are difﬁcult to pick up without experi-

ence. These ways are including dealing with pupils’

responses or pausing the interaction with the robot.

Because “learningUnit” and “teachingMethod”

are properties that are dependent on each application

domain, it is necessary to deﬁne object properties cor-

responding to their respective application domain on-

tologies when using the proposed tool for different ap-

plication domains.

4 KNOWLEDGE CHUNK

SEARCH AND REUSE

4.1 Knowledge Chunk Base

The KCB was created based on the application do-

main, robot, and KC ontologies. Fig. 6. shows an

example of the KCB. In Fig. 6., the name, descrip-

tion, learning unit, teaching method, chunk ﬁle name,

movie ﬁle name, and use and process of a robot are

deﬁned for the instance “Rhythm pendulum” of the

“Knowledge chunk” class.

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

220

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4.2 Knowledge Chunk Search

4.2.1 Search Query of Knowledge Chunks

Listing 1: Source codes of knowledge chunk search query.

1 SELECT DISTINCT ?s ?name ?description ?ﬁle ?

movie

2 WHERE {

3 ?s rdfs:label ?name ;

4 rdfs:comment ?description ;

5 ec:chunkFile ?ﬁle ;

6 ec:chunkMovie ?movie ;

7 ec:hasProcess ?process .

8 {

9 ?s ec:learningUnit ?unit .

10 ?unit rdf:type ?selectedUnit .

11 } UNION {

12 ?s ec:learningUnit ?selectedUnit .

13 }

14 {

15 ?s ec:teachingMethod ?method .

16 ?method rdf:type ?selectedMethod .

17 } UNION {

18 ?s ec:teachingMethod ?selectedMethod .

19 }

20 {

21 ?process ec:robotUsage ?usage .

22 ?usage rdf:type ?selectedUsage .

23 } UNION {

24 ?process ec:robotUsage ?selectedUsage .

25 }

26 }

The source codes of the KC search query written

in SPARQL

are shown in Listing 1. The KC search

query obtains a list of learning units selected by the

user, the teaching method, the KC name (?name) as-

sociated with the instance in the use of the robot class,

the description (?description), KC ﬁles (?ﬁle), and

movie ﬁle passes. These are displayed in a spec-

iﬁed format as the search result of the KC search

screen shown in Section 4.2.2. In the positions de-

scribed as ?selectedUnit, ?selectedMethod, and ?se-

lectedUsage of Listing 1, the instances of the learning

unit, teaching method, and use of robot classes that

https://www.w3.org/TR/sparql11-overview/

have been selected by the user on the search screen

are assigned.

4.2.2 Knowledge Chunk Search Screen

Fig. 7. shows the KC search screen, which can be

called from the scenario editor. First, a category of the

KCB is selected from the category pulldown menu.

Then, the setting ﬁle for the selected KCB category is

read, and the items set as the search condition are dis-

played on the search screen (“Leaning unit,” “Teach-

ing method,” and “Use of robot,” as shown in Fig. 7.).

From the ﬁrst pulldown menu of each search condi-

tion, any subclass just below the root class of the on-

tology corresponding to the search condition can be

selected. For example, learning unit classes such as

the “Science learning unit” class can be selected using

the learning unit search condition. After the ﬁrst pull-

down menu is selected, the second pulldown menu

can then be used to select a list of subclasses of the

selected class. Upon reaching the leaf class, a list of

instances of the selected class will be displayed in the

last pulldown menu.

Once the search button is pressed, the search re-

sults will be displayed. The types of instances are

inferred by the class hierarchies, and all instances of

the knowlege chunk related to the selected class are

obtained and displayed. After conﬁrming the content

of the KC by replaying the movie, the users can insert

the workﬂow corresponding to the KC into the sce-

nario editor being created. Fig. 8. shows a screenshot

of the workﬂow of “Rhythmic pendulum”. If a user

selects the “Rhythmic pendulum” KC, the workﬂow

shown in Fig. 8. is inserted into the scenario editor.

Then, the user can reuse and edit the part of the work-

ﬂow.

If intermediate classes other than the leaf class or

its instances are selected, not only the instances of the

selected class but also all instances belonging to its

subclasses are included in the search target.

5 CASE STUDY

5.1 Overview

To evaluate the proposed tool, lessons with TA robots

were conducted at an elementary school, in coopera-

tion with two teachers in charge of Class 1 and 4 of

the 5th grade. The two teachers had no experience in

programming and development of robot applications,

and this was their ﬁrst time conducting lessons with

TA robots.

A Knowledge Chunk Reuse Support Tool based on Heterogeneous Ontologies

221

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dulum”.

The content of the lessons given to both classes

was the introduction of “Pendulum movement of the

science unit.” For both classes, “what determines the

time necessary for a pendulum to make a single recip-

rocal motion” is set as the speciﬁc learning task to be

attained at the end of the lesson.

TA robots applications were developed to conduct

a lesson using the proposed tool; each teacher gave

the lesson with TA robots once to each class. The

proposed tool and the TA robots were evaluated by

the questionnaires given to the teachers .

5.2 Creation of Knowledge Chunk Base

In this case study, the KCB was created based on

the experience obtained from previously conducted

lessons with TA robots. In this process, 11 KCs

were extracted considering that there are different

uses of the robot that are unique to each KC (the in-

stances of the “Knowledge chunk” class as shown in

Fig. 5). The movies corresponding to the KCs were

also edited in such a way that the movie scenes de-

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Figure 9: Lesson environment of case study.

Figure 10: A Photograph from an actual lesson.

picting only knowledge transmission, practical opera-

tions, etc. were removed to make the running time in

the range of 1 minute to 1 minute 30 seconds; com-

ments were added as required. These measures aimed

to shorten the time required by teachers to understand

the content of the KC and allow them to follow the

entire ﬂow of the KC in a short time.

5.3 Lesson Environment

Fig. 9. and Fig. 10. show the lesson environment and

a photograph from a lesson. Because it was neces-

sary to set up the robots and desks on the day before

the class and the teachers needed to rehearse the les-

son, an audiovisual room was used considering class-

room availability and other factors. NAO, which is

a humanoid robot developed by Aldebaran Robotics,

and JACO2, which is an arm type robot developed by

Kinova, were arranged on the teacher’s desk as TA

robots. NAO works with teachers to enhance pupils’

interest and understanding in the lesson. JACO2 puts

KEOD 2019 - 11th International Conference on Knowledge Engineering and Ontology Development

222

a weight on a pendulum and demonstrates swinging

the weight. Eight tables were so arranged such that

four pupils were at each table and each group could

easily perform the swinging pendulum experiment.

5.4 Creation of Workﬂows for TA

Robots

After the teachers learnt how to use the PRINTEPS

scenario editor, they began to create workﬂows for TA

robots on January 2019. They spent approximately

two to three hours per session, requiring three ses-

sions in total to complete the workﬂow.

At the time of the ﬁrst workﬂow creation, they de-

veloped the entire lesson ﬂow using the conventional

scenario editor without using the KC search function.

In the second session, a detailed workﬂow was cre-

ated while referring to the use of the robot with the

KC search function. In addition, during on the same

day as the second session, an introductory lesson us-

ing NAO was given to the pupils to remove the barrier

between pupils and the robots by introducing them to

the robot functions, such as speech recognition and

age estimation based on facial images. In the third

session, which took place a day before the lesson with

TA robots, ﬁnal adjustments were made through a re-

hearsal.

5.5 Reuse of Knowledge Chunks

In the case study, three KCs (“Greetings combined

with precautions on robot lessons”, “Rhythmic pen-

dulum”, and “Pepper gives a posted notice quiz”)

were reused by the teachers with the help of the pro-

posed tool from the viewpoint of teaching material

creation and use of a robot. Here, we would like to

introduce the knowledge chunk of “Rhythmic pendu-

lum”.

The rhythmic pendulum is a standard lesson

taught at the elementary school that is intended to

generate in the pupils mind a question concerning the

change of cycle by allowing them to match the move-

ment of the pendulum with the rhythm of the mu-

sic being played within a certain time limit. In this

KC, NAO gives a signal to start and stop the rhyth-

mic movement of the pendulum and replay the music.

Interactions were added to the workﬂow of Class 1,

including those in which pupils ask NAO to turn on

the music they want to listen to through NAOs speech

recognition system; however, NAO rejects their re-

quest saying that it is in no mood to do that and then

proceeds to tell them what it wants to listen to and

succeeds in getting their approval. An interaction was

also added to the workﬂow of Class 4, in which NAO

directly talks to a pupil who is always slow to get

prepared to listen to what the teacher says before the

teacher starts explaining the rhythmic pendulum.

6 EVALUATION

We evaluated our system by asking the two teachers

to complete a questionnaire on the reuse of teacher

knowledge, use of robot, and user interface.

Regarding the reuse of teacher knowledge, com-

ments such as “While it has the beneﬁt of preventing

to make the same mistake, there are also fears that too

much dependence would hinder creativity and lead to

a rigid teaching system” were made.

On the utilization of robots, teachers had the view

that, “We were able to know in advance about typi-

cal cases that people who interact with a robot for the

ﬁrst time would experience and could use that knowl-

edge to plan the classes” and “There is a clear need

for further research on the timing to use KC search

function.”

Their comments on the user interface included, “It

is convenient to be able to ﬁnd desired class scenes by

narrowing down the categories and using a combina-

tion of categories.” and “It was easy to use because it

was organized for each related item, but there seemed

to be room to subdivide the items.”

It can be concluded from the above that it is neces-

sary to clarify the process of workﬂow where each KC

should be used and allow developers to dynamically

change the degree of detail, priority, and organization

of KC search items to suit the teacher’s knowledge

level.

7 RELATED WORK

Many visual programming tools have been proposed

to support the creation of robot applications (Pot et al.,

2009; Alexandrova et al., 2015; Huang and Cak-

mak, 2017). Although many of these tools support

a ﬂow diagram representation and functional compo-

nent search, it does not have the function to deﬁne the

semantics of the combination of components based

on application domain knowledge and use of robot

knowledge, and make them reusable.

As a related work on the scenario design sys-

tem for learning and teaching based on ontologies,

SMARTIES has been proposed (Hayashi et al., 2009).

In SMARTIES, the design intent of scenarios can be

saved by creating a learning and teaching scenario

based on the learning support theory, OMNIBUS

A Knowledge Chunk Reuse Support Tool based on Heterogeneous Ontologies

223

(hereafter called OMNIBUS ontology). From the per-

spective of knowledge reuse, (Hayashi et al., 2009)

mainly makes knowledge on methods reusable. Addi-

tionally, when the teacher uses SMARTIES to create a

learning and teaching scenario, SMARTIES proposes

the knowledge on methods applicable to an I L event

(concept for deﬁning the relation between teaching

and learning) based on OMNIBUS ontology. This is

intended to support the design and reuse the learning

and teaching scenarios based on learning and teaching

theories.

The subject of this study is lessons with TA robots,

and not ordinary lessons administered solely by teach-

ers. Therefore, it aims to reduce the teachers’ bur-

den of creating workﬂows by creating knowledge and

workﬂows that are mainly related to the use of robots,

about which ordinary teachers, being non-experts on

robotics, do not know much, reusable in the form of

KC using the framework of case-based reasoning.

8 CONCLUSION

This paper proposed a knowledge chunk (KC) reuse

support tool based on heterogeneous ontologies to

solve issues associated with the creation and reuse of

workﬂows using the scenario editor in PRINTEPS.

Based on the workﬂows of lessons with TA robots

given to several elementary schools, we created on-

tologies of teacher knowledge (teaching materials and

methods), robots, and KCs. Then, we established the

KCB based on these ontologies. Two public elemen-

tary school teachers created workﬂows for TA robots

using the proposed tool, and each teacher conducted

a lesson about a science learning unit “movement of

pendulum” with TA robots once. The proposed tool

and TA robot application were evaluated through the

questionnaires given to the teachers to conﬁrm their

usefulness.

In a future study, it is necessary to clarify in which

process of the workﬂow creation should each KC be

used and help the users know the suitable time to per-

form a search. It is also necessary to make it possi-

ble to dynamically change the degree of detail, prior-

ity, and organization of KC search items to suit the

teacher’s knowledge level. In addition, the functions

of PRINTEPS must be extended in a future work so

that more ﬂexible questions and answers, as well as

conversations, can be exchanged by incorporating a

more accurate speech recognition system and such

mechanisms as general ontologies, rule-based system,

and spoken dialogue system.

ACKNOWLEDGEMENTS

This study was supported by the project of “A Frame-

work PRINTEPS to Develop Practical Artiﬁcial In-

telligence,” (JPMJCR14E3) the Core Research for

Evolutional Science and Technology (CREST) of the

Japan Science and Technology Agency (JST).

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