Fujitsu AI
Ideally Matches Children to Daycare Centers
Ideally Matches Children to Daycare Centers
Matched
about 8,000 children in Saitama to daycare centers in just seconds
about 8,000 children in Saitama to daycare centers in just seconds
Fujitsu
Laboratories Ltd.,Kyushu University,Fujitsu Limited
Laboratories Ltd.,Kyushu University,Fujitsu Limited
Tokyo, Kawasaki and Fukuoka, Japan, August 30, 2017
Fujitsu
Limited, Fujitsu Laboratories Ltd., and the Fujitsu Social Mathematics Division
of the Institute of Mathematics for Industry at Kyushu University(1) have developed an AI-based
matching technology that uses game theory to automatically calculate an optimal
matching of children to daycare centers. Using this technology, a complicated
daycare admissions screening that had previously required several days by hand
took only seconds.
Limited, Fujitsu Laboratories Ltd., and the Fujitsu Social Mathematics Division
of the Institute of Mathematics for Industry at Kyushu University(1) have developed an AI-based
matching technology that uses game theory to automatically calculate an optimal
matching of children to daycare centers. Using this technology, a complicated
daycare admissions screening that had previously required several days by hand
took only seconds.
The
admissions process of matching children to daycare centers seeks to fulfill as
many of the applicants’ preferences as possible based on complex requirements,
including applicant priority criteria set by each local government and requests
for siblings to be admitted to the same daycare center. This has made it
difficult to automate the matching process in a way that would satisfy all
applicants. As a result, many local governments have, until now, manually
conducted trial and error to as much as possible accommodate preferences for
siblings to attend the same daycare center, but this meant that seat assignment
by the local government could take several weeks, requiring some time before
the applicants could be notified of the results. There were also issues such as
siblings being admitted to different daycare centers due to applicant
preferences not being accepted.
admissions process of matching children to daycare centers seeks to fulfill as
many of the applicants’ preferences as possible based on complex requirements,
including applicant priority criteria set by each local government and requests
for siblings to be admitted to the same daycare center. This has made it
difficult to automate the matching process in a way that would satisfy all
applicants. As a result, many local governments have, until now, manually
conducted trial and error to as much as possible accommodate preferences for
siblings to attend the same daycare center, but this meant that seat assignment
by the local government could take several weeks, requiring some time before
the applicants could be notified of the results. There were also issues such as
siblings being admitted to different daycare centers due to applicant
preferences not being accepted.
Now,
this newly developed technology has made it possible to match children to
daycare centers, meeting as many preferences as possible, following a priority
ranking. This is done by modeling the dependency relationships of complex
requirements, including parents who prioritize siblings going to the same
daycare center, or parents who do not mind if their children go to different
daycare centers as long as both children get a seat, using a mathematical model(2) based on game theory, which
rationally resolves the relationships between people having differing values.
When this technology was evaluated using anonymized data from about 8,000
children in the city of Saitama, it successfully calculated an optimal
assignment result in just a few seconds.
this newly developed technology has made it possible to match children to
daycare centers, meeting as many preferences as possible, following a priority
ranking. This is done by modeling the dependency relationships of complex
requirements, including parents who prioritize siblings going to the same
daycare center, or parents who do not mind if their children go to different
daycare centers as long as both children get a seat, using a mathematical model(2) based on game theory, which
rationally resolves the relationships between people having differing values.
When this technology was evaluated using anonymized data from about 8,000
children in the city of Saitama, it successfully calculated an optimal
assignment result in just a few seconds.
Fujitsu
plans to offer this technology as an optional service for MICJET MISALIO
Child-Rearing Support, a childcare support system for local governments, during
fiscal 2017. The company will also work to apply this technology to a variety
of matching problems as part of Fujitsu Human Centric AI Zinrai, Fujitsu’s
approach to artificial intelligence.
plans to offer this technology as an optional service for MICJET MISALIO
Child-Rearing Support, a childcare support system for local governments, during
fiscal 2017. The company will also work to apply this technology to a variety
of matching problems as part of Fujitsu Human Centric AI Zinrai, Fujitsu’s
approach to artificial intelligence.
Background
In
recent years, a number of measures have been undertaken to slow the declining
birth rate in Japan, including enacting the “Act on Child and Childcare
Support.” Many issues still remain, however, in the childcare situation in
many regions, such as the number of children waiting for places at daycare
centers. One of these issues is the increasing complexity of daycare admissions
screening due to the need to maintain fairness. This poses difficulties in the
admissions screening, in which children are matched to a limited number of
places while taking into consideration the various circumstances of each
family, requiring a great deal of staff and time. There have also been many
cases, depending on the local government, where the result of efforts to
accommodate preferences was that siblings ended up in different daycares
despite the results of numerous consultations. From the perspective of
supporting working women, an important government policy, as well, handling the
admissions assignment process quickly, carefully, and appropriately has become
an urgent need in society.
recent years, a number of measures have been undertaken to slow the declining
birth rate in Japan, including enacting the “Act on Child and Childcare
Support.” Many issues still remain, however, in the childcare situation in
many regions, such as the number of children waiting for places at daycare
centers. One of these issues is the increasing complexity of daycare admissions
screening due to the need to maintain fairness. This poses difficulties in the
admissions screening, in which children are matched to a limited number of
places while taking into consideration the various circumstances of each
family, requiring a great deal of staff and time. There have also been many
cases, depending on the local government, where the result of efforts to
accommodate preferences was that siblings ended up in different daycares
despite the results of numerous consultations. From the perspective of
supporting working women, an important government policy, as well, handling the
admissions assignment process quickly, carefully, and appropriately has become
an urgent need in society.
Issues
When
trying to incorporate complex requirements into admissions rules, such as
parents who prioritize siblings going to the same daycare center, and parents
who do not mind if siblings go to separate daycare centers but who will pull
out if only one child gets a place, there are cases where either there are
multiple matching patterns that satisfy all rules, or times when each matching
pattern violates some rules. This makes it difficult to automatically select
the matching pattern that satisfies applicants to the maximum extent possible.
trying to incorporate complex requirements into admissions rules, such as
parents who prioritize siblings going to the same daycare center, and parents
who do not mind if siblings go to separate daycare centers but who will pull
out if only one child gets a place, there are cases where either there are
multiple matching patterns that satisfy all rules, or times when each matching
pattern violates some rules. This makes it difficult to automatically select
the matching pattern that satisfies applicants to the maximum extent possible.
In
Saitama, for example, to carefully handle the matching of applicants, the local
government undertook their own admissions screening that considered sibling
admissions as well as the timing of the siblings’ admissions, the facilities
they would be admitted to, their age, and their priority ranking. In order to
match 7,959 children to 311 daycare centers while considering these complex
requirements, however, the task required numerous days of work by 20 to 30
employees.
Saitama, for example, to carefully handle the matching of applicants, the local
government undertook their own admissions screening that considered sibling
admissions as well as the timing of the siblings’ admissions, the facilities
they would be admitted to, their age, and their priority ranking. In order to
match 7,959 children to 311 daycare centers while considering these complex
requirements, however, the task required numerous days of work by 20 to 30
employees.
About the
Newly Developed Technology
Newly Developed Technology
Fujitsu,
Fujitsu Laboratories, and Kyushu University have now developed matching
technology that can automatically determine the assignment pattern that will
fulfill the preferences of as many applicants as possible, according to
priority ranking, by creating a model of the relationships that meet the
individual preferences of multiple applicants, in consideration of complex
requirements that are determined by human trial and error. Game theory, which
is used to create the model in this technology, is a mathematical approach that
rationally handles conflicts and cooperation between people in society where
interests are not necessarily aligned. Mathematical research based on the game
theory is primarily ongoing in the field of economics. By applying this theory
to the matching problem of daycare admissions, this technology successfully
finds the optimal assignment pattern that prioritizes applicants having the
highest priority, even in cases of multiple patterns that fulfill all rules, or
no patterns fulfill all rules.
Fujitsu Laboratories, and Kyushu University have now developed matching
technology that can automatically determine the assignment pattern that will
fulfill the preferences of as many applicants as possible, according to
priority ranking, by creating a model of the relationships that meet the
individual preferences of multiple applicants, in consideration of complex
requirements that are determined by human trial and error. Game theory, which
is used to create the model in this technology, is a mathematical approach that
rationally handles conflicts and cooperation between people in society where
interests are not necessarily aligned. Mathematical research based on the game
theory is primarily ongoing in the field of economics. By applying this theory
to the matching problem of daycare admissions, this technology successfully
finds the optimal assignment pattern that prioritizes applicants having the
highest priority, even in cases of multiple patterns that fulfill all rules, or
no patterns fulfill all rules.
As
an example, one can consider assigning two sets of siblings (a total of four
children) to two daycares (A and B) that can take two children each.
Considering the number of seats in each daycare, there are six possible
patterns (figure 1). In this example, the parents of each child have requested
daycare A over daycare B, but have also expressed that they would prefer that
both siblings go to daycare B rather than be split up. In this situation, the
rule is to fulfil these preferences as much as possible in determining
admission assignment while also respecting the priority ranking of the
children.
an example, one can consider assigning two sets of siblings (a total of four
children) to two daycares (A and B) that can take two children each.
Considering the number of seats in each daycare, there are six possible
patterns (figure 1). In this example, the parents of each child have requested
daycare A over daycare B, but have also expressed that they would prefer that
both siblings go to daycare B rather than be split up. In this situation, the
rule is to fulfil these preferences as much as possible in determining
admission assignment while also respecting the priority ranking of the
children.
Figure 1: Admissions decision using the rule (assignment 3 is
optimal)
optimal)
If
the preferences for child 2, for example, cannot be met due to the preferences
for child 1, who has higher priority, then that must be accepted, but if they
cannot be met due to the preferences for child 3, who has lower priority, this
would be a violation of the rule. In this way, it is necessary to check if the
rule is being violated while considering both the priority of the children and
their preferences. In addition, in cases where siblings have different priority
rankings, there are cases where there may be multiple assignments that fulfill
the rule. In this case, where seat assignments 3 and 4 both fulfill the rules,
assignment 3 is considered optimal, because it can meet the preferences of
child 1, who has the highest priority ranking.
the preferences for child 2, for example, cannot be met due to the preferences
for child 1, who has higher priority, then that must be accepted, but if they
cannot be met due to the preferences for child 3, who has lower priority, this
would be a violation of the rule. In this way, it is necessary to check if the
rule is being violated while considering both the priority of the children and
their preferences. In addition, in cases where siblings have different priority
rankings, there are cases where there may be multiple assignments that fulfill
the rule. In this case, where seat assignments 3 and 4 both fulfill the rules,
assignment 3 is considered optimal, because it can meet the preferences of
child 1, who has the highest priority ranking.
Figure
1 is a simple example, but as the number of daycares and children increase, the
table can become massive. If five daycare preferences are listed for each of
8,000 children, there will be 5 to the power of 8000 possible combinations. Even
using computers, it would be difficult to calculate each of those one by one in
any realistic amount of time. In addition, even if an assignment that fulfilled
the rules was found by devising the method in which possible assignments are
checked, it would be difficult to guarantee an even better assignment was not
possible.
1 is a simple example, but as the number of daycares and children increase, the
table can become massive. If five daycare preferences are listed for each of
8,000 children, there will be 5 to the power of 8000 possible combinations. Even
using computers, it would be difficult to calculate each of those one by one in
any realistic amount of time. In addition, even if an assignment that fulfilled
the rules was found by devising the method in which possible assignments are
checked, it would be difficult to guarantee an even better assignment was not
possible.
Now
Fujitsu, Fujitsu Laboratories, and Kyushu University have developed technology
in which, using a model based on game theory, the payoff (desirability) from an
admissions assignment is converted to a score, and that score is used to find
the optimal assignment pattern. This makes it possible to rapidly calculate the
one assignment pattern that maximizes the scores for the applicants having
highest priority.
Fujitsu, Fujitsu Laboratories, and Kyushu University have developed technology
in which, using a model based on game theory, the payoff (desirability) from an
admissions assignment is converted to a score, and that score is used to find
the optimal assignment pattern. This makes it possible to rapidly calculate the
one assignment pattern that maximizes the scores for the applicants having
highest priority.
Effects
This
technology was evaluated using anonymized data for about 8,000 children in
Saitama. The result was that this technology was able to calculate in just
seconds seat assignments that fulfilled the complex and detailed requirements
unique to Saitama, which had previously taken 20 to 30 people quite a few days.
When this technology is commercialized, it is expected that it will not only
dramatically reduce the burden of seat assignment tasks on local government
personnel, it will also enable decision notifications to be sent to applicants
earlier, improving services to residents. Moreover, it is expected that this
will enable the process to incorporate more detailed requirements without
increasing the amount of work or the chance of overlooking something, improving
applicant satisfaction.
technology was evaluated using anonymized data for about 8,000 children in
Saitama. The result was that this technology was able to calculate in just
seconds seat assignments that fulfilled the complex and detailed requirements
unique to Saitama, which had previously taken 20 to 30 people quite a few days.
When this technology is commercialized, it is expected that it will not only
dramatically reduce the burden of seat assignment tasks on local government
personnel, it will also enable decision notifications to be sent to applicants
earlier, improving services to residents. Moreover, it is expected that this
will enable the process to incorporate more detailed requirements without
increasing the amount of work or the chance of overlooking something, improving
applicant satisfaction.
Comment from
Saitama City Government
Saitama City Government
About the evaluation results:
The
trial results show that, with the imposition of multiple complicated elements,
including priorities of children and preference patterns for siblings applying
at the same time, this technology’s accuracy was equivalent to manual admission
screening in coordinating daycare facility usage (admissions allocation) for
Saitama under the conditions given, and we can say these results can be
accepted as being trustworthy and as close to perfect as possible.
trial results show that, with the imposition of multiple complicated elements,
including priorities of children and preference patterns for siblings applying
at the same time, this technology’s accuracy was equivalent to manual admission
screening in coordinating daycare facility usage (admissions allocation) for
Saitama under the conditions given, and we can say these results can be
accepted as being trustworthy and as close to perfect as possible.
Expected effects going forward:
We
feel that the greatest effect of utilizing AI will be in the time reduction.
Currently, the first round admissions screening for the April daycare usage
applications for Saitama takes numerous days, but if assignment results can be
obtained in seconds using AI, this could dramatically reduce the burden on our
employees. In addition, by settling the results as early as possible,
applicants can be notified of the results more quickly than they are now,
leading to a smoother implementation of our plan to return women to the workplace.
feel that the greatest effect of utilizing AI will be in the time reduction.
Currently, the first round admissions screening for the April daycare usage
applications for Saitama takes numerous days, but if assignment results can be
obtained in seconds using AI, this could dramatically reduce the burden on our
employees. In addition, by settling the results as early as possible,
applicants can be notified of the results more quickly than they are now,
leading to a smoother implementation of our plan to return women to the workplace.
All
told, by incorporating various requirements into the model, including household
circumstances and the preferences of the parents or guardians, and migrating
this process to AI, it will be possible to reflect the preferences of guardians
in an even more detailed way than present, which is expected to contribute to
improved resident satisfaction by resolving the problem of children waiting for
places. We look forward to the full-fledged deployment of this technology with
anticipation.
told, by incorporating various requirements into the model, including household
circumstances and the preferences of the parents or guardians, and migrating
this process to AI, it will be possible to reflect the preferences of guardians
in an even more detailed way than present, which is expected to contribute to
improved resident satisfaction by resolving the problem of children waiting for
places. We look forward to the full-fledged deployment of this technology with
anticipation.
Future Plans
By
the end of this fiscal year, Fujitsu plans to offer this technology as an
optional service in its MICJET MISALIO Child-Rearing Support module, a
childcare support system for local governments.
the end of this fiscal year, Fujitsu plans to offer this technology as an
optional service in its MICJET MISALIO Child-Rearing Support module, a
childcare support system for local governments.
In
addition, as part of its Fujitsu Human Centric AI Zinrai artificial
intelligence technology, Fujitsu will aim to adopt this technology beyond
daycare admissions, to encompass a variety of matching applications, such as
the fair matching of personnel deployments within an organization and employee
schedule matching.
addition, as part of its Fujitsu Human Centric AI Zinrai artificial
intelligence technology, Fujitsu will aim to adopt this technology beyond
daycare admissions, to encompass a variety of matching applications, such as
the fair matching of personnel deployments within an organization and employee
schedule matching.
Related
Websites
Websites
· [1] Fujitsu
Social Mathematics Division of the Institute of Mathematics for Industry at
Kyushu University
Social Mathematics Division of the Institute of Mathematics for Industry at
Kyushu University
Institute
of Mathematics for Industry: Asia’s first mathematics research center focused
on industrial technology. In addition to carrying out mathematical theory
research for industry, it also contains the Laboratory of Advanced Software in
Mathematics, which implements and publishes theories as software. Fujitsu
Social Mathematics Division: A mathematical technology research and development
unit aimed at resolving social issues, established in September 2014, as part
of the Institute of Mathematics for Industry by Kyushu University, Fujitsu
Limited, and Fujitsu Laboratories Ltd.
of Mathematics for Industry: Asia’s first mathematics research center focused
on industrial technology. In addition to carrying out mathematical theory
research for industry, it also contains the Laboratory of Advanced Software in
Mathematics, which implements and publishes theories as software. Fujitsu
Social Mathematics Division: A mathematical technology research and development
unit aimed at resolving social issues, established in September 2014, as part
of the Institute of Mathematics for Industry by Kyushu University, Fujitsu
Limited, and Fujitsu Laboratories Ltd.
· [2] Modeling
… using a mathematical model
… using a mathematical model
This
research was partially supported by JST PRESTO Grant Number JPMJPR14E1, Japan.
research was partially supported by JST PRESTO Grant Number JPMJPR14E1, Japan.
About Fujitsu Laboratories
Founded
in 1968 as a wholly owned subsidiary of Fujitsu Limited, Fujitsu Laboratories
Ltd. is one of the premier research centers in the world. With a global network
of laboratories in Japan, China, the United States and Europe, the organization
conducts a wide range of basic and applied research in the areas of
Next-generation Services, Computer Servers, Networks, Electronic Devices and
Advanced Materials. For more information, please see: http://www.fujitsu.com/jp/group/labs/en/.
in 1968 as a wholly owned subsidiary of Fujitsu Limited, Fujitsu Laboratories
Ltd. is one of the premier research centers in the world. With a global network
of laboratories in Japan, China, the United States and Europe, the organization
conducts a wide range of basic and applied research in the areas of
Next-generation Services, Computer Servers, Networks, Electronic Devices and
Advanced Materials. For more information, please see: http://www.fujitsu.com/jp/group/labs/en/.
About Kyushu University
Since
its foundation in 1911, Kyushu University has become one of the leading
universities in Japan. The University is comprised of 11 undergraduate schools,
18 graduate schools, 17 faculties, 5 research institutes, a University
hospital, and a University library, as well as over 50 affiliated research
centers. Empowered by our 100-years of tradition and experience, we are keen
and ready to tackle whatever challenges we must face. For more information, please
see: http://www.kyushu-u.ac.jp/english/index.php.
its foundation in 1911, Kyushu University has become one of the leading
universities in Japan. The University is comprised of 11 undergraduate schools,
18 graduate schools, 17 faculties, 5 research institutes, a University
hospital, and a University library, as well as over 50 affiliated research
centers. Empowered by our 100-years of tradition and experience, we are keen
and ready to tackle whatever challenges we must face. For more information, please
see: http://www.kyushu-u.ac.jp/english/index.php.
About Fujitsu
Fujitsu
is the leading Japanese information and communication technology (ICT) company
offering a full range of technology products, solutions and services.
Approximately 155,000 Fujitsu people support customers in more than 100
countries. We use our experience and the power of ICT to shape the future of
society with our customers. Fujitsu Limited (TSE: 6702) reported consolidated
revenues of 4.5 trillion yen (US$40 billion) for the fiscal year ended March
31, 2017. For more information, please see http://www.fujitsu.com.
is the leading Japanese information and communication technology (ICT) company
offering a full range of technology products, solutions and services.
Approximately 155,000 Fujitsu people support customers in more than 100
countries. We use our experience and the power of ICT to shape the future of
society with our customers. Fujitsu Limited (TSE: 6702) reported consolidated
revenues of 4.5 trillion yen (US$40 billion) for the fiscal year ended March
31, 2017. For more information, please see http://www.fujitsu.com.
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