Sharedo: To-Do List Interface for Human-Agent Task Sharing
Jun Kato†
Daisuke Sakamoto‡Takeo Igarashi‡
Masataka Goto††
National Institute of Advanced Industrial Science and Technology , The University of Tokyo‡{jun.kato, m.goto}@aist.go.jp†, {d.sakamoto, takeo}@acm.org‡
ABSTRACTIn this paper, we propose a to-do list interface for sharing
tasks between human and multiple agents including robots
and software personal assistants. While much work on
software architectures aims to achieve efficient(semi-)autonomous task coordination among human andagents, little work on user interfaces can be found for useroriented flexible task coordination. Instead, most of theexisting human-agent interfaces are designed to command asingle agent to handle specific kinds of tasks. Meanwhile,
our interface is designed to be a platform to share any kindsof tasks between users and multiple agents. When agents canhandle the task, they ask for details and permission to execute
it. Otherwise, they try supporting users or just keep silent.
New tasks can be registered not only by humans but also byagents when errors occur that can only be fixed by humanusers. We present the interaction design and implementationof the interface, Sharedo, with three example agents,
followed by brief user feedback collected from a preliminary
user study.
Author Keywords

Human-robot interaction; social media platforms; to-do list.
ACM Classification Keywords

recommended by friends, etc. Such ambiguity in tasks does
not need to be resolved instantly, and we sometimes want tokeep them ambiguous till the time comes.
In addition, which agent should handle a task is sometimes
ambiguous since multiple agents can handle it in different
ways. For instance, to watch a movie, a shopping robot can
buy a Blu-ray disc at a movie store and a web-based agent
can show it online. Therefore, it is important to think ofinteraction techniques for task coordination as well as task
specification.
For organizing ambiguous tasks whose details (who, when,
how to handle them) are yet to be decided, a to-do list has
been a good tool for human users [1]. Kreifelts investigatedits use for human-human task sharing [8]. There are also
many web services that allow sharing to-do lists with otherssuch as Remember The Milk [10]. With these existing usesof to-do list interfaces for task sharing in mind, we propose
Sharedo (Figure 1), a web-based to-do list interface that
supports the ambiguous state in human-agent task sharing. It
extends the use of the to-do list interface not only for humans
but also for agents and allows them to share, discuss, andcomplete tasks together.

H.5.2. User Interfaces [Interaction styles]; I.2.9. Robotics[Commercial robots and applications].
INTRODUCTION

There is an increasing number of agents available in our daily
lives, including software personal assistants on smartphones,e.g., Siri for iPhone and Cortana for Windows Phone, androbots at home, e.g. iRobot Roomba. While existing agents
can handle various tasks through usable interfaces, e.g.
speech and button-based interfaces, they are usuallydesigned to instantly respond to the user. Therefore, tointeract with such agents, the user needs to be prepared toclarify details. However, our daily lives are filled with
ambiguity. We do not know what we want to eat tonight, whowill cook it, whether to order books online or to buy them at
a nearby bookstore, when we have time for watching videos
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HAI 2014, October 29–31, 2014, Tsukuba, Japan.
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http://dx.doi.org/10.1145/2658861.2658894

Figure 1. To-do list interface for human-agent task sharing

suggests, purchases, and delivers products to the home withthe help of the Amazon API, and a media content agent thathelps in consuming media content such as reading online
articles and watching online videos. In the following sections,related work, interaction design, implementation details, anduser experience collected from a preliminary study arereported.
RELATED WORK
Software Architectures for Task Coordination

Much work has been done proposing software architectures
and algorithms for autonomous task coordination of multiagent systems [2], aiming at efficient task completion. A
notable example is Electric Elves [3], in which human users
and agents are equally represented as proxies to achieve
human-agent collaboration. The agents in the system support
human organization by tracking schedules and the locationsof human users with palmtop computers, deciding presenters
for meetings, and arranging food for the meetings by faxing
orders to restaurants. While these agents consider user input,
e.g., accepting/declining meeting schedules, and achieve
adjustable autonomy, the task coordination is an autonomous
process without user input. In more recent work [11], aprototype infrastructure was proposed that allows the user tohelp in task coordination, which resulted in more efficientrobot-agent-person collaboration for disaster rescue.

Figure 2. Discussion board for removing task ambiguity

All users and agents of the Sharedo interface have virtualembodiments associated with their social media accounts.
Once they have logged-in to the platform by using theaccounts, a to-do list can be created to share tasks with otherusers and agents. Both a user and agent can register new tasks
to the list (the user through the web page and the agent
through a web API). A task has its own page that shows its
detailed information and a discussion board for discussing
the details of a task, as shown in Figure 2. All users andagents are notified, whenever they edit the task informationand create posts on the discussion board (the users through amessage on the social media account and the agents througha web API hook). Discussion on the task gradually removes
its ambiguity and leads to its completion. Finally, the task
can be marked as completed either by a user or agent.
To provide concrete examples of human-agent interaction on
the platform, three agents with different characteristics are
implemented: a house cleaning agent that schedules theappropriate time for cleaning a room and executes it bycontrolling an iRobot Roomba robot, a shopping agent that

These papers do provide some information on their graphicaluser interfaces, but their focus is consistently on softwarearchitectures and algorithms for efficient (semi-)autonomous
task coordination. In contrast, our work focuses on a userinterface design that creates an intuitive mapping between
actions on shared to-do lists and events of task coordination.
It aims to allow human users to coordinate casual tasks on
the basis of their occasional preferences.
Human-Robot Interfaces for Commanding Tasks

Various user interfaces have been proposed for operating
robots interactively, such as use of a handheld device for the
remote control of a mobile robot [6] and a multi-touch
display for simultaneous control of multiple robots [7].
These interfaces allow real-time control and are suitable fortele-existence, military, and gaming applications in which
the user needs to continuously monitor what the robot isdoing.
Home robots are designed to complete household tasks bythemselves and free the user from spending time on them.
Therefore, it is desired that user interfaces for such robots
can specify the details of repetitive and/or time-consumingtasks in intuitive ways. For instance, Cooky [12] allows theuser to command multiple robots to cooperatively cook
meals. Magic Cards [13] allows the user to put cards in aroom where the user wants to particular tasks done such as“clean here” and “move objects from here” and “to there.”Then, the system drives a card collector robot and delegateseach task to an appropriate robot, e.g., cleaning task to acleaner robot and delivering task to a carrier robot. Whenthere is an error during the task execution, a printer robot
prints error messages. This system and our work share theconcept of commanding multiple kinds of agents and getting
responses from them. The difference is that their system
assumes the task details are clear when the user puts cards inthe environment, while ours is capable of handlingambiguous tasks.
Instead of creating new interfaces for commanding robots,
making use of social media platforms is proposed [9]. The
system uses existing tools originally designed for humanhuman communication, including text messaging on cell
phones, video chatting on personal computers, and an online
shared calendar. Text messaging and video chatting both
involve text-based dialogues and allow interactive task
specifications, similar to the discussion board in our work.
Though, there are two major differences. First, the prior work
focuses on comparing social media to find the characteristics
of each medium as a commanding interface. Our work,
however, focuses on proposing a novel use of one social
medium, a to-do list. The novelty is in its bi-directionalcommanding; it allows not only commanding to a robot but
also from a robot. Second, in the prior work, each medium
supports one-to-one communication between the user and arobot, but our work supports communication involving
multiple users and multiple agents.
To-do List Interface for Task Management

A to-do list is one of the standard tools for task management.
There are many web-based to-do list services, which often
have an option to share the list with others for computersupported cooperative work (CSCW). Within the researchcontext, the effectiveness of sharing the lists in a distributed
environment was discussed [8]. To-do lists have also often
been discussed as part of personal information management(PIM) systems [1]. Intelligent assistance for to-do lists was
proposed [5] that divides existing tasks into sub tasks,
prioritizes tasks, merges similar tasks, and assigns tasks toautomated programs on the basis of “common knowledge”
collected in advance from volunteers and online corpora. In
these cases, details on each task are shared and discussedwith others via other conversation media, typically e-mail. In
contrast, our interface has a discussion board that enables
seamless integration of information sharing and discussion
between human and agents.
Towel [4] is one of the closest examples in that it combines
a to-do list interface with a text-based chat window for
discussion between the user and a software personal assistant.
While Towel provides a chat window for one-to-one humanagent communication to delegate digital tasks to a specificagent, our interface is designed to be a platform forcollaborative completion of both physical and digital tasks.
For instance, while Towel’s chat log is only visible to theuser and agent, our discussion board is accessible by all
members of the to-do list, including human and agents. This
allows support for more complex use cases, e.g., multiple
agents propose handling the same task in parallel.

INTERACTION DESIGN

The main interface of the Sharedo platform looks like astandard web-based to-do list service, as shown in Figure 1
and Figure 2. The user can have more than one to-do list andswitch between them in accordance with their purpose andcontext. For instance, one to-do list is for private use sharedwith the media content agent, while another is for sharing
with family users and the cleaning agent, etc. Within each todo list, multiple tasks can be registered. For each task, its
own page shows the task details and a discussion board
where human and agents can discuss the task. To identify
each user and agent, we use the authorization API of an
existing social medium, Twitter. Twitter is also used to send
notifications to human users through its private messaging
feature called “direct message.”
Despite these standard features, the novelty of this interface
comes from the platform being capable of hosting not only
human-human but also human-agent interaction. In thefollowing subsections, the human-agent interaction enabledon the platform is explained with concrete example scenarioswith three agents: the house cleaning agent (@sharerom), theshopping agent (@sharezon), and the media content agent(@sharecnt). These agents are carefully designed to havedifferent characteristics as shown in Table 1 to reveal the use
cases of the platform.
Share Tasks

The user can add a task to a to-do list by specifying the title,
e.g. “Buy milk,” detailed description (optional), assignment,
and deadline (optional). Options for the assignment are theuser him/herself, a specific member among humans andagents sharing the to-do list, and anyone sharing the list.
When a task is added to a list, it is immediately notified to allusers and agents sharing the list. Each agent checks if it hasanything to do with the task. For instance, the shopping agent
is capable of buying milk, but the house cleaning and media
content agents are not. In this case, the shopping agent
proceeds to the next step of discussion, and the others keep
silent.
An agent can also add a task to the list if it needs the help ofhuman users or has some recommendations (Figure 3). For
instance, the house cleaning agent might fail to complete
cleaning because of errors such as low battery and being
trapped in tangled cables on the floor. When the errors canAgents and theirfeatures

Home
Cleaning

Shopping

Media
Content

Share – add user tasks

✓

-

✓

Discuss – clarify details

✓

✓

-

Discuss – notify progress

✓

-

✓

Complete tasks by itself

✓

✓

-

Physical

Both

DigitalTasks are…

Table 1. Variety of agents with different characteristics

agents post comments to the discussion board. The shopping
agent searches for broomsticks on shopping sites, lists
relevant products, and asks the user for a response. The housecleaning agent does not know if the user is going to use thebroomstick to clean the room by him or herself, so it searches
for time slots when the user is out of the house and offers
those slots in which the Roomba robot will clean the room.
The user can read these posts and assign the task to one of
the two agents to mute further posts from the other agent.
The user can even ignore these posts and go to the next stepof completing the task by him or herself, making both agentsstop posting to the discussion board.
Another example of ambiguous tasks is “Watch the Disney
movie ‘WALL-E’” accompanied with a YouTube URL(Figure 2). The shopping agent can buy and deliver the Bluray disc, shown as example interaction (B), but the media
content agent can also show it online, shown as example
interaction (A). The media content agent posts an inline
YouTube video player to the discussion board.
Figure 3. Examples of tasks created by agentsonly be fixed by a human, the agent adds the to-do “Help me!”to the list and asks for help. The media content agent mightalso add a task for human users to watch a video that is
considered highly relevant to recently watched videos.
Please note that the tasks added to the list can be freely edited
or removed if human users think they are not desirable. Oncethey are removed, the agents stop handling them. Everychange the user makes on the Sharedo platform tells
something to the agents, which in turn achieves a sort of nonverbal communication.
Discuss Tasks

After a task is shared in a to-do list, the discussion phasestarts and continues till it is completed or removed from thelist (Figure 2). The discussion takes place on a discussion
board provided separately for each task in the list. This
allows focused and effective discussion towards taskcompletion.
One major role of the discussion board is to remove any
ambiguity in the task and to create a complete task definition
required to complete the task. The task definition in generalconsists of who should complete the task and when and how
it should be completed. While who refers to one of thehumans and agents sharing the list and when refers to a
specific time and date, what kind of information how consistsof is different from one task to another. On the Sharedo
platform, each agent relevant to the task tries removing
ambiguity in its own way in parallel. In other words, eachagent has its own task definition and asks the user to fill inblank fields by concurrently posting comments. Such
concurrent posts make the discussion board look similar toexisting social media such as Twitter.
For instance, adding the task “Buy a broomstick and clean
the room” makes both the shopping and house cleaning

The other role of the discussion board is to remind the userof the task. Since posting comments on the board triggers thesending of notifications to all users sharing the to-do list,
agents sometimes make use of the board to make sure thathuman users complete the task. This is critical when adeadline is set to a task.
For example, suppose one user adds the task “See video
before next meeting” to a to-do list shared with colleagues.
The task is accompanied with a YouTube URL in its
description and with a specific deadline. The user thinks thatwatching the video is mandatory to prepare for the meetingbut is not sure if all of the colleagues will watch it. When the
to-do list is shared with the media content agent, the agent
addresses this concern. It posts a comment with an inline
YouTube player allowing the colleagues to easily watch thevideo. Furthermore, it tracks the activity of the colleagues on
the comment. When one starts playing the video with theinline player or clicks the link to watch it on the full YouTube
site, the agent internally marks the user as “done.” Thecreator of the task can post a new comment such as “Whohasn’t watched the video yet?” to make the agent respondwith a comment listing the colleagues whose activity has notbeen observed yet. Since the agent provides clear
information on the progress, the creator of the task can berelieved from communicating with each colleague just to
make sure whether they have watched the video.
Complete Tasks

Once any ambiguity in a task is removed, the last step is tocomplete it, mark the task as “done” on the system, andnotify all users and agents of the task completion.
The user is allowed to complete tasks at any time, even if the
discussion is in progress or if the user has already assigned
the agent to handle it. For instance, the user might notice hecan “buy milk” on his way back home. There are cases where
agents cannot understand subtle nuance in the task

definitions. For example, the user could find time to “clean
the room” and actually want to do it by himself rather thanasking the house cleaning agent, which does not consider the
specific space he is temporarily using for an intricate jigsaw
puzzle. Once the user completes a task and marks it ascompleted on the system, the agents do their best to cancel
further actions. For instance, when the cleaning task ismarked as done, the house cleaning robot cancels its
scheduled cleaning.

communicate with the platform. The agents also have a web
hook mechanism, a Web API called by the platform for event
notification. The implementation of the user client andagents is separated so that the user has control over theprivacy policy (which agent to share what information) andto ensure capability for implementing more agents on theplatform. Currently, the house cleaning agent, the shopping
agent, and the media content agent are implemented on topof the platform.

Each agent has its own implementation for completing tasks.
The task completion procedure can be self-contained as withthe case of the house cleaning agent and the shopping agent.
These agents can potentially complete the tasks without help
from human users. Meanwhile, the procedure can depend on
the human users’ actions as with the case of the media
content agent. It essentially observes the action of each user
and marks the task as done when all conditions are met.
During this procedure, new tasks are potentially made byagents. For such examples, see the second paragraph in thesubsection “Share Tasks.”

User interface – The user interface is implemented with
HTML5, CSS3, and JavaScript. All communication between
the user interface and the platform is done via Asynchronous
JavaScript + XML (Ajax), except for the authentication
process that uses the OAuth API provided by Twitter and theinitial load of the top page. Ajax allows smooth transitions
between pages and provides a better browsing experience.

IMPLEMENTATION
Sharedo Platform

The Sharedo platform is implemented as a set of Java
Servlets on the Google App Engine (GAE) server (Figure 4).
Both the user client and agents call the Sharedo Web API to

Event notification – The Sharedo platform notifies users ofan event that has occurred in the to-do lists through a direct
messaging API provided by Twitter. The notification is sent
to their mobile phones or mailboxes, depending on theirpreferences. The platform also notifies the agents of theevent through WebHook, which is a technique for sending
HTTP requests to the Web APIs of agents. The current
implementation notifies users and agents of events including
the start and end of sharing, that is, the insertion, edit, andremoval of a to-do, and the posting of a comment.
House Cleaning Agent

The house cleaning agent consists of two major components– the server-side implementation responsible for virtualembodiment of the agent and the client-side implementation
responsible for physical embodiment of the agent.

Figure 4. Sharedo platform with multiple agentsThe server-side implementation is installed on the GAE
server. The implementation receives an event and responds
to it on the Sharedo platform. It has various functions, e.g.,
to read the details of a task in the to-do list, to respond tocomments, and to send instructions to the client-side
implementation. When a new task is created, it finds a
keyword related to cleaning in the text-based title and a
description of the task by matching regular expressions.
Then, with permission to access the user’s online calendar(Google Calendar) granted beforehand, it looks for a timeslot when the user is out of the house and offers a time inwhich the robot will work. Next, the agent waits for theuser’s response. If the user approves the offer by posting
comments that contain “yes” or other similar words, theclient-side implementation is commanded to handle the task
at the selected time. If the user rejects the offer, the agent
finds another appropriate time candidate. If the user does not
respond, the agent will not work when the selected timecomes. It will offer a new time for cleaning after the planned
time has passed. The time can also be explicitly specified bycommenting on it. The number of times for a thoroughcleaning job can also be specified; for instance, commenting“twice” makes the robot repeat the cleaning twice. If theusers have not added a cleaning task to the to-do list for morethan a predefined period (a week by default), the agent adds
the task “Clean the room” in the user’s to-do list as areminder.
The client-side implementation is installed on a personal
computer in the user’s house. It receives a HTTP requestfrom the server-side implementation to control an iRobot
Roomba doing an actual cleaning task in the real world. The
Roomba robot is controlled wirelessly with the Roomba
Open Interface protocol via a Bluetooth connection. Whenthe task is finished, it notifies the server-side implementation
to mark the cleaning task as completed. Otherwise, when afatal error is reported by the robot that prevents taskcompletion, it notifies the server-side implementation to add
a new task that asks a human user for help.
Shopping Agent

While the shopping agent is implemented without any
physical embodiment, it certainly affects the real world upontask completion (the product is delivered to the user’s house).
It is installed on the GAE server that orders products from an
online shopping service (Amazon.com). In the future, it
might be possible that we substitute the shopping service
with a mechanical robot capable of buying and delivering the
products to the mailbox.
The implementation finds a keyword in the title anddescription of the newly posted/edited task in a similar way
to the house cleaning agent. It matches regular expressions
to extract the keyword from a task, such as “buy something,”
and then searches for products by using the Product
Advertising API by Amazon.com to show the most likely
products (up to three) in the discussion board. The user canrefine the result by posting a comment with the format“refine keywords” or instruct the agent to search again with“search new keywords.” In addition, if the user wants to buy
groceries, the user can narrow the results by including thecategory keyword “#Grocery.” The search is repeated until
the user is satisfied with the results. When the user approves
an offer by clicking the product image on the discussion
board, the shopping agent posts a comment with a link tomake an order. Finally, when the order has been made, thetask is marked as “completed.” After the payment process,the ordered item will be delivered to the user.
Media Content Agent

The media content agent is installed on the GAE server andcollects information about the URL mentioned in the task
description. It posts a message that helps the human users tounderstand the media content pointed by the URL before
they access it. In addition, by substituting the original URL
with a redirection URL, the agent tracks which user hasaccessed the URL.
While the message depends on the content of the URL, acommon procedure for the agent is to access the URL and try
parsing the contents as a HTML document and retrieve thetitle. The message always starts with a hyperlink to theredirection URL such as “Please see <title of the web page>.”

If the original URL is a YouTube video, the agent posts an
inline player. The title text is shown as a hypertext link thatnavigates to a redirection URL, allowing the agent to trackuser activity with link clicking. Thanks to the YouTube
Iframe API, the agent can also track user activity with theinline video player. When the user clicks the link or plays thevideo, the agent silently marks the user as “done.” When all
human users who are supposed to watch the video aremarked as “done,” the agent posts a congratulation to reward
the users and to notify the task creator of it. In addition, theagent searches for highly relevant videos and posts them as
a new task to the to-do list. Tasks posted by the agent can beeasily distinguished on the list since all tasks made by otherusers and agents are shown with their social media icons.
Otherwise, when the original URL is not a YouTube video,
the agent posts a thumbnail image showing the overview ofthe website instead of the inline player. While the videospecific interaction is omitted, all the rest remains the same;for instance, when all the users access the redirection URL,
the agent congratulates them by posting a message.
PRELIMINARY USER STUDY
MethodWe conducted a preliminary user study for two months byrecruiting four participants in two families (one family witha 25-year-old male and 27-year-old female and the otherwith a 36-year-old male and 26-year-old female.) The study
aimed to compare use of the Sharedo interface against thedirect commanding of agents and to observe how theinterface is used in a real setting. Besides the Sharedo
interface, the participants were allowed to command aniRobot Roomba robot, access the Amazon website, or see
other websites without the help of agents at any time during
the user study.
Results

After two months, the participants made 179 to-dos with 111
comments including 61 from agents in 15 to-do lists. The
Sharedo interface was used well to perform collaborative
work between the participants and the agents.
For several times, the participants pushed the physical buttonof the Roomba robot to get the room cleaned immediately.
Otherwise, they used our interface effectively to postpone
their decision and later handled the cleaning on their own,
asked their partner to do it, or made the robot do it at thecorrect timing. While the cleaning tasks done by the robot
usually succeeded, one participant reported, “I worried
about the robot even when I didn’t get messages while I wasout.” and “I came to feel more like cleaning the room bymyself when possible. Otherwise, I often had to arrange theroom to get it ready for robot cleaning.” Another participantsaid, “When I made the robot clean the room, I wanted to
specify the area to be cleaned. I did not want my Roomba to
run over my belongings on the floor, though there was noother option than moving them by myself. Furthermore, Ithink such an option should not be told to other users andagents through text-based communication.”

workflow of task management, showing a promising way tocoordinate tasks for human-agent cooperation.

Discussions about the shopping agent were interesting. Twoof the participants were a married couple who reported, “We
felt like the shopping agent was a friend. The fact that it has
its Twitter account as we do and it communicates with us
through the account makes it more familiar. The
Amazon.com website also has recommendations for us, but
the feeling was very different,” and, “The recommendations
made by the agents were sometimes beyond our expectations,
expanding our choice.”

ACKNOWLEDGMENTSDISCUSSION AND FUTURE WORK

According to the preliminary user study, the platform itself
was proved to work stably and is thought to be extendible
enough to see a greater variety of agents being implemented
in the future. The process of defining task details through thediscussion board interface got favorable feedback from theusers. While the board could not be used to get an immediate
response from the Roomba robot, it has the potential to help
create long-term good relationship between the user and therobot.
The server-side implementation of agents could be moreconvenient for the user if they can utilize the history ofcompleted tasks. Agents could learn the timing patterns forcleaning a room and remember favorite products for
shopping with the help of programming-by-example
techniques. Some users complained that the text-based
communication on the discussion board for specifying thedetails of cleaning tasks is not thorough enough. To address
this issue, our future work will improve the agent
implementation to integrate more interactive user interfaces
within the discussion board.
Regarding the users’ preference on the relationship with theiragents through Twitter accounts, our future work might
include more integration of our interface with Twitter. Whilethe current implementation only uses a direct messaging API
so that communication between the user and the agent cannot
be seen from other followers of the user on Twitter, it might
be interesting to see what happens when we allow public
communication between them, which is shown in the Twitter
timeline and visible to anyone on the Internet.
Collected comments from the study suggest that the platform
can enhance communication between people. Deploying theinterface as a communication medium for the user andprofessional housekeepers instead of the cleaning agent
would also produce interesting results.
CONCLUSION

We presented a user interface design with which a user canshare, discuss, and complete to-dos together with people andagents. Its implementation, named “Sharedo,” can beaccessed at http://www.sharedo.info/ and comes with three
agents: house cleaning, shopping, and media content agents.
A preliminary study showed that the platform could help
remove ambiguity in task definition through the standard

This work was supported in part by JST, CREST.
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