A Proposal of the Person-centered Approach for Personal Task Management
Ikki Ohmukai
The Graduate University for Advanced Studies
Department of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japani2k@grad.nii.ac.jp

Hideaki Takeda
National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan
takeda@nii.ac.jp

Mitsunori Miki
Doshisha University
1-3 Tatara-miyakodani, Kyotanabe-shi, Kyoto, Japanmmiki@mail.doshisha.ac.jp

Abstract
This paper proposes a person-centered approach for personal task management in which people can decide management of their tasks according to their environments including their subjective and multivalent judgement and human relationship. In our approach the task management
is modeled as a decision-making process on their own resources. Human decision-making process consists of three
types of activity, i.e., the intelligence activity, the design
activity, and the choice activity. The proposal system assists each activity by three sub-systems, i.e., Visualizer, Optimizer, and Recommender respectively. At first, Visualizer indicates the attributes associated with each task suchas the deadline, the subjective priority, and the workload,
which are determined by the user. And Optimizer generates executable schedules from these tasks by active scheduler and multi-objective genetic algorithm. Finally, Recommender evaluates these alternatives by analytic hierarchy
process. Also the system is able to analyze the human relationship of the user group with PageRank algorithm, andthis result is utilized to improve the performance of the taskscheduler. We implement client/server system which uses
mobile phones. And we verify the function of the proposedsystem along the lines of two scenarios.

1 Introduction
In this paper, we propose a person-centered approach forpersonal task management in which people can treat with
their tasks appropriately according to their environments including their subjective and multivalent judgement and human relationship.

The features of our approach as task management are
not only to assist scheduling tasks but also to assist people
to clarify their intension for tasks, e.g., awareness of inconsistency and subjective judgements for tasks.
In this approach the task management is modeled as adecision-making process[10] on their own resources. As
we design our personal task management system with theframework of decision-making theory, it consists of threebasic processes that corresponds to three types of activities
in decision-making theory.
Human decision-making process consists of three typesof activity, i.e., the intelligence activity, the design activity,
and the choice activity. The proposed system assists these
activities with Visualizer, Optimizer, and Recommender respectively.
Firstly, Visualizer indicates the attributes associated witheach task such as the deadline, the subjective priority, andthe workload, which are determined by the user. Secondly,
Optimizer generates executable schedules from these tasks
by optimization algorithms. Thirdly, Recommender evaluates these alternatives by an analytic process. The system
is also able to analyze the human relationship of the usergroup, and this result is utilized to improve the performanceof the task scheduler.
We show how the proposed system can work with two
scenarios. In the first scenario, we show how to resolve
overlap of tasks. In the second, we establish the processthat the priority of client is calculated and propagated.
This paper organizes as follows. In Section 2, we describe the personal task scheduling problem and clarify thedifficulty of this problem. In Section 3, we propose a new
approach based on decision-making theory. We explain
three types of activities of human decision process and redefine the personal task schedule problem coordinating withthese processes. In Section 4, we describe the proposed system in detail. We show functions of Visualizer, Optimizer,
Recommender, and Analyzer. In Section 5, we confirm thefunctions of the proposed system along with two scenarios.
We describe the specification of the implemented system in
Section 6, we summarize the paper in the last section.

2 Personal Task Schedule Management
In this section, we clarify our objective problems.

2.1 Personal Resource Management
Managing our resources such as time and money is important today. Complexity about the personal resource management is increasing along with growth of complexity of
our society.
Most people now belong to multiple organizations orcommunities, and assign most of their resources to achieve
their objectives, and they entrust the management to them.
In recent years, the social structure is in the process of drastically changing. And such change affects people’s lifestyle.
For example, suppose a person who belongs not only abusiness company but also a non-government organization(NGO) or non-profit organization (NPO), which is playing
an important role in our society. In this situation, it is difficult that multiple organizations manage his/her resourcecooperatively.
There are many studies on groupware and resource coordination based on software agent[3]. However, these studies
aim to support a single organization.

2.2 Personal Task-scheduling Problem
Since tasks for individuals have variety in their purpose
and value, it is difficult to provide a single definitive objective function. We have a number of tasks at any time, andshould decide among these tasks which is to perform. Eachtask has some attributes, such as the deadline, the subjective
priority, and the workload. People have their own standards
for judging. It makes the evaluation process complicated.
For example, one may prefer to perform smaller and easier tasks at first, then she/he might fail to perform important tasks because they tend to be bigger and more difficult.
For improvement in personal productivity, it is important toevaluate tasks from a multifaceted and a long-term perspective. Conventional scheduling tools such as the day plannerand the calendar are not adequate to manage the tasks properly.
There are some issues about this problem.
• Objectives and Constraints

Most of the ordinary scheduling problems assume that
all of the given tasks must be assigned to one’s schedule. However, it is not necessary to perform all tasks
in the personal task-scheduling problem. The proposedsystem deals with this problem as a multi-objective optimization problem. Thus alternatives obtained by thesystem may not contain all of the given tasks.
• Type of task
We define two types of task, one is a movable task
which can be assigned whenever possible. The otheris a fixed task which cannot change their time such as
meetings or conferences. There are few studies on thescheduling problem with mixed task environment.
• Dynamically changes of environment
The tasks and their objectives may change when newtasks come in.

3 Task Management as Decision Support
System
To solve problems described in the previous section, weapply a concept of decision-making theory and decision
support systems to them.
Decision support systems provide a better solution for adecision-making entity with interactive operations of data
and models.
Decision-making is not a one-shot action such a determination or selection. Simon argued that human decisionmaking process consists of three types of activity, i.e.: theintelligence activity, the design activity, and the choiceactivity [10]. Consequently, the decision support systemshould assist these three activities.
Conventional decision support systems tried to resolve
problems of which objective is complicated but welldefined. To design the systems for these problems, the designer defines objective functions that mean which state is
the best, then she/he describes relations between the objective functions and each of decision-making process. Thesesystems can evaluate a relevance of alternatives and decide
the best solutions in them. However this method is applicable only if the designer can define suitable objective functions.
Consequently we formulate the personal task-schedulingproblem as an iterative process, i.e.,
1) The system generates several active schedules for a set oftasks.
2) The user selects one of the schedules, and executes it.
3) Return to 1) when a new task comes in or an unforeseen
incident occurs.
And the system applies support algorithms continuously
to each process.

4 The Proposed System
We show the configuration of the proposed system asshown in fig.1. The system is based on server/client architecture. The server has a task database and provides fourfeatures as described later. Mobile phones are used to access to the system.

Figure 2. 2D interface on prototype system

Figure 1. System configuration

4.1 Data Structure
The user of the system enters task information with thefollowing attributes:• Name of task

Figure 3. Task parameters• Time parameters– Start time
– Deadline– Workload
The movable and the fixed task are distinguishable
whether these parameters are defined or not.
• Priority
Decision maker evaluates each task subjectively on
scale of 1 to 5.
• Client

4.2 Visualizer: Support for Intelligence Activity
The process that a user recognizes status of her/his own
task scheduling will create an opportunity for a better management. Therefore it is important that the decision support
systems extract issues related to task scheduling from thegiven data.
Since the mobile phones have only small screens andcannot display high-resolution graphics, Visualizer canshow the chronogram by different scale, i.e., weekly andmonthly. This method stimulates awareness of users’ stateof task. It shows users not only how many tasks they have,but also overlap of tasks in an easy-to-understand way.

Fig. 2 and 3 show how Visualizer works in the prototype
system. In the chronogram in fig. 2, each task is displayed
as a horizontal bar of which width indicates the workload of
the task along the time axis. The attributes of each task can
be changed in the dialog box as shown in fig.3.
The Visualizer warns the user when it detects overlappedtasks or an unexecutable task. Then it displays a button thatinvokes Optimizer if it judges the necessity for rescheduling.

4.3 Optimizer: Support for Design Activity
It is important to allocate her/his resource correctly for
problem solving. Depending on users’ request, the decision
support systems generate active schedules.
Active scheduler resolves the overlaps of the given tasks
to obtain executable schedules. It serializes the tasks bychanging the attributes of each task.
The Optimizer generates alternate schedules if the active
scheduler fails to resolve problems about the overlappedtasks. As described in Section 2, criterion of evaluation areoften multiple. Consequently, multi-objective optimization
algorithms must be used to calculate these schedules. We
use the Genetic Algorithm: GA[2], especially the MultiObjective GA: MOGA[1]. Objective functions for MOGA
are set out as follows:• Maximize the number of task
• Maximize the utility rate of time
• Maximize the reward based on the subjective priority• Maximize the reward based on the priority of client
MOGA eliminates non-dominant candidates which is inferior to other solutions in all objective functions. The survivors are passed to Recommender as alternatives.

Table 1. Scenario 1: Test data
Name
A
B
C
D
E

Start time——
1/11 11:00
——
——
——

Deadline
1/10 15:00
1/11 15:00
1/11 20:00
1/13 14:00
1/13 18:00

Workload
10:00
4:00
12:00
11:00
13:00

Pri.
3
5
4
2
5

From
W
X
Y
Z
W

4.4 Recommender: Support for Intelligence Activity
Most of decision-making problems require qualitative
information. Therefore conventional methods based onmathematical model cannot deal with these problems well.
We propose a new method based on an Analytic Hierarchy Process: AHP[9], which is able to treat both qualitative
data and quantitative data. In common AHP, the numberof pairwise comparison increases considerably along with
the increase of the alternatives. It is also necessary to redo
the pairwise comparison when a new alternate is added. We
apply the absolute measurement method[8] to avoid theseploblems.

4.5 Analyzer: Estimation of Priority of Client
The Analyzer supports efficient decision-making to theusers with new information extracted.
It is considered that there are some attributes of the taskchanging with clients. Therefore, we propose a method to
calculate priority of client with the PageRank algorithm[7].

5 Task Management Scenarios with Our
MethodIn this section, we show the way to use the proposedsystem.

Figure 4. Scenario 1: Displaying initial condition by Visualizer

Next Optimizer moves up task d to resolve the second
overlap. But a new overlap between task c and task d is occurred. In this manner, these overlap may take place into a
chain reaction. As a consequence, it may be failed to schedule as shown in fig. 5.
Optimizer generates active schedules by multi-objective
genetic algorithm. Table 2 shows six candidates and theirevaluation values for four functions. A digit given in parentheses indicates rank of merit by each function. And fig. 6
shows the rank of six alternatives. All of evaluation itemsof schedule 6 underruns those of schedule 2 or 4, whereby
schedule 6 is eliminated from alternatives.
Recommender evaluates 5 alternatives which are not

5.1 Scenario 1: Presentation of the Schedule andResolving the Problems
This scenario describes a scheduling procedure with Visualizer, Optimizer, and Recommender. At the beginningof the scenario, five tasks are registered as the test problem
shown in table 1.
Visualizer displays these tasks as fig. 4. A black barmeans a movable task, and a white one means a fixed task.
It indicates that two overlaps are occurred. The system invokes Optimizer to resolve them. Optimizer sets start timeof task c to before the value of workload of task b. As aresult, the first overlap is resolved.

Figure 5. Scenario 1: Fail to scheduleTable 2. Scenario 1: Evaluation value of alternatives
Alternative
Schedule 1
Schedule 2
Schedule 3
Schedule 4
Schedule 5
Schedule 6

Ntask
5 (1)
4 (2)
4 (2)
4 (2)
4 (2)
4 (2)

Rutil
0.500 (6)0.851 (2)0.979 (1)0.809 (4)0.830 (3)0.787 (5)Rpri.
19 (1)
16 (3)
14 (5)
15 (4)
17 (2)
14 (5)

Rf rom17 (1)
14 (3)
12 (6)
15 (2)
13 (5)
14 (3)

Table 4. Scenario 1: Weighting vector of criteria
Criterion
Ntask
Rutil
Rpriority
Rf romWeight
0.059
0.391
0.424
0.126

Table 5. Scenario 1: Final result
Alternative Evaluation value
Schedule 1
0.193 (5)Schedule 2
0.204 (2)Schedule 3
0.202 (3)Schedule 40.196 (4)
Schedule 5
0.206 (1)5.2 Scenario 2: Estimation of Priority based on
Human Relationship

Figure 6. Scenario 1: Rank of alternatives

In the previous scenario, the priority of client is calculated simply based on frequency of task request shown infig. 7. However, human relationship of task request is bidirectional and complicated.

subject to other candidates. We determine the pairwise
comparison matrix as shown in table 3. Table 4 shows theweighting vector of evaluation, that is eigenvector of the
pairwise comparison matrix.
Table 5 shows the final result calculated by AHP. Schedule 5 receives the best rate because it has a high evaluation
value for Rutil and Rpriority which are major components
of its weighting vector.

Table 3. Scenario 1: Matrix for pairwise comparison

Ntask
Rutil
Rpriority
Rf rom

Ntask
1
7
5
3

Rutil
1/7
1
1
1/3

Rpri.
1/5
1
1
1/5

Rf rom1/3
3
5
1

Figure 7. Scenario 2: Dependency relation oftask (individual)

In this scenario, a small-scale group is targeted as shownin fig. 8. This group consists of five people, and frequencyof task request is shown with the arrows. Analyzer collects
these data and compiles into weighted adjacency matrix asshown in fig. 6.
Analyzer then applies PageRank algorithm to this matrix. Table 7 shows the obtained priority, i.e., the eigenvector of the matrix.
The system overwrites the attributes of tasks andreschedules with them. Table 8 shows the recalculated
value by Optimizer and table 9 is the final result by Recommender. As compared to table 5, the comprehensive evalu-

Table 7. Scenario 2: Weighting vector of person by PageRank
Name
A
W
X
Y
Z

Weight0.044 (5)0.093 (4)0.170 (3)0.370 (1)0.322 (2)Figure 8. Scenario 2: Dependency relation oftask (group)
Table 8. Scenario 2: Evaluation value of alternatives
Table 6. Scenario 2: Task Network
Name A W X Y Z
A
0
4
3 0 0
W
3
0
4 1 0
X
5
2
0 0 1
Y
2
5
2 0 3
Z
4
0
2 1 0

ation values of schedule 4 and 5 decrease relatively, and thevalues of schedule 2 and 3 increase because they contain thetasks from Y and Z who are evaluated highly by Analyzer.
As just described, the analyzer makes the user’s state apparently and changes a suggestion of the system.

6 Implimentation
We developed a prototype system based on the personcentered approach as described above.
Mobile phone SO503i by NTT DoCoMo and Sony isused as the client. It is able to connect to the Internet andexecute applications described with Java 2 Micro Edition
Wireless SDK for DoJa[4]. This device has 120 pixel by
120 pixel LCD display. Figure 9 and 10 show the prototype
system running on the actual device.
Debian/GNU Linux 3.0 with a self-made PC and Java 2
Runtime Environment 1.4 are used as the server.
HORB[5] and iHORB[6] are used as HTTP communication library. The former is a distribute object framework
based on Java, the latter is an implementation of HORB for
mobile environment.

7 Conclusion
This paper proposes a new approach for personal taskschedule management based on decision-making theory.

Alternative
Schedule 1
Schedule 2
Schedule 3
Schedule 4
Schedule 5

Ntask
5 (1)
4 (2)
4 (2)
4 (2)
4 (2)

Rutil
0.500 (6)0.851 (2)0.979 (1)0.809 (4)0.830 (3)Rpri.
19 (1)
16 (3)
14 (5)
15 (4)
17 (2)

Rf rom1.049 (1)0.956 (2)0.879 (3)0.678 (5)0.727 (4)

Decision-making process of human consists of three typesof activity, i.e., the intelligence activity, the design activity, and the choice activity. The proposed system can assist
these three activities. Visualizer, a subsystem which supports the intelligence activity, i.e., indicates task conditionof the user by chronogram-like display. It also shows overlapped tasks clearly. Optimizer, which helps the design activity, generates several alternative schedules with the active
scheduler and the multi-objective genetic algorithm. Thesealgorithms eliminate redundant candidates by comparisonswith multiple objective functions. Recommender supports
the choice activity using the analytic hierarchy process. It
can evaluate qualitative and quantitative data at the sametime easily. Additionally the system is able to analyze thehuman relationship of the user group with Analyzer whichruns bsed on the PageRank algorithm.
We verify the function of the proposed system along with
two scenarios. In the first scenario, we show how to resolve

Table 9. Scenario 2: Final result
Alternative Evaluation value
Schedule 1
0.194 (4)
Schedule 2
0.207 (1)0.206 (2)Schedule 3
Schedule 40.189 (5)0.203 (3)Schedule 5overlap of tasks and set out the candidate schedules. In thesecond scenario, we establish the process that the priorityof client is calculated and propagated.
A further direction of this study is to sophisticate oursystem. We are planning to perform an experiment withlarge-scale groups. In addition to the experiment we will
investigate nature of personal tasks.

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Figure 9. Initial condition on the long-term
scale

Figure 10. Proposed schedule by the system