#### TL;DR AI is becoming increasingly integrated into our daily...
**Basic compact** refers to the implicit agreement between humans a...
Good rules to live by. It would be interesting to identify the poss...
**Challenge 2 - Adequate models:** Intelligent agents must be able ...
**Challenge 3 - Predictability**: Effective collaboration requires ...
**Challenge 1 - A Basic Compact**: Intelligent agents need to be ab...
**Challenge 4 - Directability**: Agents must be directable, they sh...
**Challenge 8 - Collaboration**: Technologies supporting agent plan...
**Challenge 5 - Revealing status and intentions**: Agents must make...
**Challenge 6: Interpreting signals**: Agents must be able to obser...
**Challenge 7 - Goal negotiation**: Agents must be able to engage i...
**Challenge 10 - Cost control**: All team members must help control...
**Challenge 9 - Attention management**: Agents must be able to part...
NOVEMBER/DECEMBER 2004 1541-1672/04/$20.00 © 2004 IEEE 91
Published by the IEEE Computer Society
Human-Centered Computing
puting that we have developed individually and jointly
over the years, and is adapted from a more comprehensive
examination of common ground and coordination.
Requirements for joint activity
among people
We define joint activity as an extended set of actions
that are carried out by an ensemble of people who are
coordinating with each other.
Joint activity involves at least four basic requirements.
All the participants must
Enter into an agreement, which we call a Basic Compact,
that the participants intend to work together
Be mutually predictable in their actions
Be mutually directable
Maintain common ground
The Basic Compact
To carry out joint activity, each party effectively enters
into a Basic Compact—an agreement (often tacit) to facil-
itate coordination, work toward shared goals, and prevent
breakdowns in team coordination. This Compact involves
a commitment to some degree of goal alignment. Typi-
cally this entails one or more participants relaxing their
own shorter-term goals in order to permit more global and
long-term team goals to be addressed. These longer-term
goals might be shared (for example, a relay team) or indi-
vidual (such as highway drivers wanting to ensure their
own safe journeys).
The Basic Compact is not a once-and-for-all prerequi-
site to be satisfied but rather has to be continuously re-
inforced or renewed. It includes an expectation that the
parties will repair faulty mutual knowledge, beliefs, and
assumptions when these are detected. Part of achieving
coordination is investing in those actions that enhance the
Compact’s integrity as well as being sensitive to and coun-
teracting those factors that could degrade it.
For example, remaining in a Compact during a conver-
sation manifests in the process of accepting turns, relat-
ing understandings, detecting the need for and engaging
in repair, displaying a posture of interest, and the like.
When these sorts of things aren’t happening, we might
infer that one or more of the parties isn’t wholeheartedly
engaged. The Compact requires that if one party intends
to drop out of the joint activity, he or she must signal this
to the other parties. Breakdowns occur when a party aban-
dons the team without clearly signaling his or her inten-
tions to others.
While in traffic, drivers might have defensible motives
for rejecting a Compact about following the rules of the
road, as when they’re responding to an emergency by rush-
ing someone to the nearest hospital. At such times, drivers
might turn on their emergency blinkers to signal to other
drivers that their actions are no longer as predictable. But
in most kinds of joint activity, the agreement itself is tacit,
and the partners depend on more subtle signals to convey
that they are or aren’t continuing in the joint activity. In a
given context, sophisticated protocols might develop to
acknowledge receipt of a signal, transmit some construal
of a signal’s meaning back to the sender, indicate prepara-
tion for consequent acts, and so forth.
e propose 10 challenges for making automation
components into effective “team players” when
they interact with people in significant ways. Our analysis
is based on some of the principles of human-centered com-
Ten Challenges for Making
Automation a “Team Player”
in Joint Human-Agent Activity
Gary Klein, Klein Associates
David D. Woods, Cognitive Systems Engineering Laboratory
Jeffrey M. Bradshaw, Robert R. Hoffman, and Paul J. Feltovich,
Institute for Human and Machine Cognition
Editors: Robert R. Hoffman, Patrick J. Hayes, and Kenneth M. Ford
Institute for Human and Machine Cognition, University of West Florida
Mutual predictability
For effective coordination to take place
during the course of the joint activity, team
members rely on the existence of a reason-
able level of mutual predictability. In highly
interdependent activities, planning our own
actions (including coordination actions)
becomes possible only when we can accu-
rately predict what others will do. Skilled
teams become mutually predictable through
shared knowledge and idiosyncratic coordi-
nation devices developed through extended
experience in working together. Bureau-
cracies with high turnover compensate for
lack of shared experience by substituting
explicit, predesigned, structured procedures
and expectations.
Team members must also be directable.
This refers to the capacity for deliberately
assessing and modifying other parties
actions in a joint activity as conditions and
priorities change.
Effective coordination
requires participants adequate responsive-
ness to the others influence as the activity
Common ground
Finally, effective coordination requires
establishing and maintaining common
Common ground includes the
pertinent knowledge, beliefs, and assump-
tions that the involved parties share. Com-
mon ground enables each party to compre-
hend the messages and signals that help
coordinate joint actions. Team members
must be alert for signs of possible erosion
of common ground and take preemptive
action to forestall a potentially disastrous
breakdown of team functioning.
As an example, we had occasion to observe
an Army exercise. During the exercise, a
critical event occurred and was entered into
the shared large-format display of the com-
mon operating picture. The brigade com-
mander wasnt sure that one of his staff
members had seen the change, so he called
that person because he felt it was important
to manage his subordinates attention and
because the technology didnt let him see
if the staff member had noticed the event.
The commander had to act like an aide to
ensure that the staff member had seen a key
piece of information. Special language, often
used in noisy, confusing environments (such
as acknowledge and roger that), serves
the same function.
Ten challenges
Many researchers and system develop-
ers have been looking for ways to make
automated systems team players.
A great
deal of the current work in the software
and robotic-agent research communities
involves determining how to build auto-
mated systems with sophisticated team
player qualities.
In contrast to early
research that focused almost exclusively
on how to make agents more autonomous,
much current agent research seeks to
understand and satisfy requirements for
the basic aspects of joint activity, either
within multiagent systems or as part of
human-agent teamwork.
Given the widespread demand for increas-
ing the effectiveness of team play for com-
plex systems that work closely and collabo-
ratively with people, a better understanding
of the major challenges is important.
A Basic Compact
Challenge 1: To be a team player, an
intelligent agent must fulfill the require-
ments of a Basic Compact to engage in
common-grounding activities.
A common occurrence in joint action is
when an agent fails and can no longer per-
form its role. General-purpose agent team-
work models typically entail that the strug-
gling agent notify each team member of the
actual or impending failure.
Looking beyond current research and
machine capabilities, not only do agents
need to be able to enter into a Basic Com-
pact, they must also understandand
accept the enterprises joint goals, under-
stand and accept their roles in the collabo-
ration and the need for maintaining com-
mon ground, and be capable of signaling if
theyre unable or unwilling to fully partici-
pate in the activity.
Adequate models
Challenge 2: To be an effective team
player, intelligent agents must be able to
adequately model the other participants’
intentions and actions vis-à-vis the joint
activity’s state and evolution—for example,
are they having trouble? Are they on a
standard path proceeding smoothly? What
impasses have arisen? How have others
adapted to disruptions to the plan?
In the limited realm of what todays agents
can communicate and reason about among
themselves, theres been some limited suc-
cess in the development of theories and
implementations of multiagent cooperation
not directly involving humans. The key
concept here usually involves some notion
of shared knowledge, goals, and intentions
that function as the glue that binds agents
activities together.
By virtue of a largely
reusable explicit formal model of shared
intentions,multiple agents try to manage
general responsibilities and commitments
to each other in a coherent fashion that
facilitates recovery when unanticipated
problems arise.
Addressing human-agent teamwork pre-
sents a new set of challenges and opportu-
nities for agent researchers. No form of
automation today or on the horizon can enter
fully into the rich forms of Basic Compact
that are used among people.
Challenge 3: Human-agent team mem-
bers must be mutually predictable.
To be a team player, an intelligent agent
like a humanmust be reasonably pre-
dictable and reasonably able to predict oth-
ersactions. It should act neither capriciously
nor unobservably, and it should be able to
observe and correctly predict its teammates
future behavior. Currently, however, agents
intelligenceand autonomy work directly
against the confidence that people have in
their predictability. Although people will
rapidly confide tasks to simple deterministic
mechanisms whose design is artfully made
transparent, they are usually reluctant to
trust complex agents to the same degree.
Ironically, by making agents more adapt-
able, we might also make them less pre-
dictable. The more a system takes the ini-
tiative to adapt to its operators working
style, the more reluctant operators might
be to adapt their own behavior because of
the confusions these adaptations might
92 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS
Agents must also “understand”
and accept the enterprise’s
joint goals, their roles in the
collaboration, and the need for
maintaining common ground.
Challenge 4: Agents must be directable.
The nontransparent complexity and inad-
equate directability of agents can be a for-
mula for disaster. In response to this concern,
agent researchers have focused increasingly
on developing means for controlling aspects
of agent autonomy in a fashion that can be
both dynamically specified and easily under-
stoodthat is directability.
Policies are a
means to dynamically regulate a systems
behavior without changing code or requir-
ing the cooperation of the components being
Through policy, people can
precisely express bounds on autonomous
behavior in a way thats consistent with
their appraisal of an agents competence in
a given context. Their behavior becomes
more predictable with respect to the actions
controlled by policy. Moreover, the ability
to change policies dynamically means that
poorly performing agents can be immedi-
ately brought into compliance with correc-
tive measures.
Revealing status and intentions
Challenge 5: Agents must be able to
make pertinent aspects of their status and
intentions obvious to their teammates.
Classic results have shown that the high-
est levels of automation on the flight deck
of commercial jet aircraft (Flight Manage-
ment Systems or FMSs) often leave com-
mercial pilots baffled in some situations,
wondering what the automation is cur-
rently doing, why its doing that, and what
it will do next.
To make their actions
sufficiently predictable, agents must make
their own targets, states, capacities, inten-
tions, changes, and upcoming actions
obvious to the people and other agents that
supervise and coordinate with them.
challenge runs counter to the advice some-
times given to automation developers to
create systems that are barely noticed. We
are asserting that people need a model of
the machine as an agent participating in
the joint activity.
People can often effec-
tively use their own thought processes as a
basis for inferring the way their teammates
are thinking, but this self-referential heuris-
tic is not usually effective in working with
Interpreting signals
Challenge 6: Agents must be able to
observe and interpret pertinent signals of
status and intentions.
Sending signals isnt enough. The agents
that receive signals must be able to interpret
the signals and form models of their team-
mates. This is consistent with the Mirror-
Mirror principle of HCC: Every participant
in a complex sociotechnical system will form
a model of the other participant agents as well
as a model of the controlled process and its
The ideal agent would grasp
the significance of such things as pauses,
rapid pacing, and public representations that
help humans mark the coordination activity.
Few existing agents are intended to read their
operator teammates signals with any degree
of substantial understanding, let alone nuance.
As a result, the devices cant recognize the
operators stance, much less appreciate the
operators knowledge, mental models, or
goals, given the evolving state of the plan in
progress and the world being controlled.
Charles Billings
and David Woods
argue that an inherent asymmetry in coor-
dinative competencies between people and
machines will always create difficulties for
designing human-agent teams. Neverthe-
less, some researchers are exploring ways
to stretch agents performance to reduce
this asymmetry as far as possible, such as
exploiting and integrating available chan-
nels of communication from the agent to
the human and, conversely, sensing and
inferring the humans cognitive state through
a range of physiological measures in real
time. Similarly, a few research efforts are
taking seriously the agents need to interpret
the physical environment. If they accomplish
nothing more, efforts such as these can help
us appreciate the difficulty of this problem.
Goal negotiation
Challenge 7: Agents must be able to
engage in goal negotiation.
In many common situations, participants
must be able to enter into goal negotiation,
particularly when the situation changes and
the team has to adapt. As required, intelligent
agents must convey their current and poten-
tial goals so that appropriate team members
can participate in the negotiations.
If agents are unable to readily represent,
reason about, or modify their goals, they will
interfere with coordination and the mainte-
nance of common ground. Traditional plan-
ning technologies for agents typically take an
autonomy-centered approach, with represen-
tations, mechanisms, and algorithms that
have been designed to ingest a set of goals
and produce output as if they can provide a
complete plan that handles all situations.
This approach isnt compatible with what we
know about optimal coordination in human-
agent interaction.
Challenge 8: Support technologies for
planning and autonomy must enable a col-
laborative approach.
A collaborative autonomy approach
assumes that the processes of understand-
ing, problem solving, and task execution
are necessarily incremental, subject to
negotiation, and forever tentative.
every element of an autonomous system
will have to be designed to facilitate the
kind of give-and-take that quintessentially
characterizes natural and effective team-
work among groups of people.
James Allen and George Fergusons re-
search on collaboration management agents
is a good example.
CMAs are designed to
support human-agent, human-human, and
agent-agent interaction and collaboration
within mixed human-robotic teams. They
interact with individual agents to
Maintain an overall picture of the current
situation and status of the overall plan as
completely as possible based on avail-
able reports
Detect possible failures that become more
likely as the plan execution evolves, and
invoke replanning
Evaluate the viability of proposed changes
to plans by agents
Manage replanning when situations exceed
individual agents capabilities, including
recruiting more capable agents to perform
the replanning
Manage the retasking of agents when
changes occur
NOVEMBER/DECEMBER 2004 www.computer.org/intelligent 93
To be a team player, an intelligent
agentlike a humanmust
be reasonably predictable and
reasonably able to predict
others actions.
Adjust their communications to the
agents capabilities (for example, graphi-
cal interfaces work well for a human but
wouldnt help most agents)
Because the team members will be in
different states depending on how much of
their original plan theyve executed, CMAs
must support further negotiation and re-
planning at runtime.
Attention management
Challenge 9: Agents must be able to
participate in managing attention.
As part of maintaining common ground
during coordinated activity, team members
direct each others attention to the most
important signals, activities, and changes.
They must do this in an intelligent and con-
text-sensitive manner, so as not to over-
whelm others with low-level messages con-
taining minimal signals mixed with a great
deal of distracting noise.
Relying on their mental models of each
other, responsible team members expend
effort to appreciate what each other needs to
notice, within the context of the task and the
current situation.
Automation can com-
pensate for trouble (for example, asymmet-
ric lift due to wing icing), but currently does
so invisibly. Crews can remain unaware of
the developing trouble until the automation
nears the limits of its authority or capability
to compensate. As a result, the crew might
take over too late or be unprepared to han-
dle the disturbance once they take over, re-
sulting in a bumpy transfer of control and
significant control excursions. This general
problem has been a part of several aviation
incident and accident scenarios.
It will push the limits of technology to
get the machines to communicate as flu-
ently as a well-coordinated human team
working in an open, visible environment.
The automation will have to signal when
its having trouble and when its taking
extreme action or moving toward the ex-
treme end of its range of authority. Such
capabilities will require interesting rela-
tional judgments about agent activities:
How does an agent tell when another team
member is having trouble performing a
function but has not yet failed? How and
when does an agent effectively reveal or
communicate that its moving toward its
limit of capability?
Adding threshold-crossing alarms is the
usual answer to these questions in automa-
tion design. However, in practice, rigid and
context-insensitive thresholds will typically
be crossed too early (resulting in an agent
that speaks up too often, too soon) or too
late (resulting in an agent thats too silent,
speaking up too little). However, focusing
on the basic functions of joint activity rather
than machine autonomy has already pro-
duced some promising successes.
Cost control
Challenge 10: All team members must
help control the costs of coordinated activity.
The Basic Compact commits people to
coordinating with each other and to incur-
ring the costs of providing signals, improv-
ing predictability, monitoring the others
status, and so forth. All these take time and
energy. These coordination costs can easily
get out of hand, so the partners in a coordi-
nation transaction must do what they rea-
sonably can to keep coordination costs
down. This is a tacit expectationto try to
achieve economy of effort. Coordination
requires continuing investment and hence
the power of the Basic Compacta will-
ingness to invest energy and accommodate
others, rather than just performing alone in
ones narrow scope and subgoals. Coordi-
nation doesnt come for free, and coordina-
tion, once achieved, doesnt allow us to
stop investing. Otherwise, the coordination
breaks down.
Keeping coordination costs down is
partly, but only partly, a matter of good
human-computer interface design. More
than that, the agents must conform to the
operators needs rather than require opera-
tors to adapt to them. Information hand-off,
which is a basic exchange during coordina-
tion involving humans and agents, depends
on common ground and mutual predictabil-
ity. As the notions of HCC suggest, agents
must become more understandable, more
predictable, and more sensitive to peoples
needs and knowledge.
The 10 challenges weve presented can
be viewed in different lights:
As a blueprint for designing and evaluat-
ing intelligent systemsrequirements
for successful operation and the avoid-
ance or mitigation of coordination
As cautionary tales about the ways that
technology can disrupt rather than support
coordination: Simply relying on explicit
procedures, such as common operating
pictures, isnt likely to be sufficient.
As the basis for practicable human-agent
systems. All the challenges have us walk-
ing a fine line between the two views of
AI: the traditional one that AIs goal is to
create systems that emulate human capa-
bilities, versus the nontraditional human-
centered computing goalto create sys-
tems that extend human capabilities,
enabling people to reach into contexts
that matter for human purposes.
We can imagine in the future that some
agents will be able to enter into some form
of a Basic Compact, with diminished capa-
Agents might eventually be fellow
team members with humans in the way a
young child or a novice can besubject
to the consequences of brittle and literal-
minded interpretation of language and
events, limited ability to appreciate or even
attend effectively to key aspects of the
interaction, poor anticipation, and insensi-
tivity to nuance. In the meantime, we hope
you might use the 10 challenges weve
outlined to guide research in the design of
team and organizational simulations that
seek to capture coordination breakdowns
and other features of joint activity. Through
further research, restricted types of Basic
Compacts might be created that could be
suitable for use in human-agent systems.
Klein Associates, Ohio State University, and
the Institute for Human and Machine Cognition
94 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS
How does an agent tell when a team
member is having trouble performing
a function but hasnt yet failed?
When does an agent communicate
that its moving toward
its limit of capability?
NOVEMBER/DECEMBER 2004 www.computer.org/intelligent 95
prepared this work through the support of the
Advanced Decision Architectures Collaborative
Technology Alliance, sponsored by the US Army
Research Laboratory under cooperative agree-
ment DAAD19-01-2-0009. David Woods par-
ticipation was also made possible by an IBM
Faculty Award.
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Gary Klein is chief scientist at Klein Associates. Contact him at Klein
Associates, 1750 Commerce Center Blvd. North, Fairborn, OH 45324;
David D. Woods is a professor at the Institute for Ergonomics at Ohio
State University. Contact him at the Cognitive Systems Eng. Labora-
tory, 210 Baker Systems, 1971 Neil Ave., Ohio State Univ., Columbus,
OH 43210; woods.2@osu.edu.
Jeffrey M. Bradshaw is a senior research scientist at the Institute for
Human and Machine Cognition. Contact him at IHMC, 40 S. Alcaniz
St., Pensacola, FL 32502; jbradshaw@ihmc.us.
Robert R. Hoffman is a research scientist at the Institute for Human and Machine Cognition.
Contact him at IHMC, 40 Alcaniz St., Pensacola, FL 32501; rhoffman@ihmc.us.
Paul J. Feltovich is a research scientist at the Institute for Human and
Machine Cognition. Contact him at IHMC, 40 S. Alcaniz St., Pensacola,
FL 32502; pfeltovich@ihmc.us.


**Challenge 7 - Goal negotiation**: Agents must be able to engage in goal negotiation, especially when situations change, and the team needs to adapt. Agents should convey their current and potential goals so that appropriate team members can participate in the negotiations. **Challenge 5 - Revealing status and intentions**: Agents must make important aspects of their status and intentions clear to their teammates. This ensures that actions are predictable and that team members are not left wondering about the agent's current actions and future intentions. **Challenge 10 - Cost control**: All team members must help control the costs of coordinated activity. Coordination requires continuous investment, and all participants should aim to achieve economy of effort. Coordination doesn't come for free, and once achieved, continuous investment is required to maintain it. **Challenge 6: Interpreting signals**: Agents must be able to observe and interpret pertinent signals of status and intentions. This involves understanding signals such as pauses, rapid pacing, and public representations that help humans mark coordination activity. **Basic compact** refers to the implicit agreement between humans and systems, that entail expectations about behavior and trust placed in those systems. For example in the context of AI and automation, it's about how humans expect AI systems to act and their predictability. **Challenge 8 - Collaboration**: Technologies supporting agent planning and autonomy should prioritize a collaborative approach. This means that understanding, problem-solving, and task execution processes should be incremental, open to negotiation, and always adaptable. The design should promote the natural give-and-take seen in effective human teamwork. **Challenge 1 - A Basic Compact**: Intelligent agents need to be able to establish a "Basic Compact" for effective teamwork. This compact ensures agents engage in common-grounding activities and communicate any struggles or failures to team members. **Challenge 4 - Directability**: Agents must be directable, they should be controlled in a manner that is dynamically specified and easily understood. Policies can be used to dynamically regulate a system's behavior, making it more predictable. **Challenge 3 - Predictability**: Effective collaboration requires both human and agent team members to be mutually predictable. An intelligent agent, like a human, should act in a manner that is both reasonably predictable and capable of predicting others' actions, ensuring neither capricious nor unobservable behavior. **Challenge 9 - Attention management**: Agents must be able to participate in managing attention. Team members should direct each other's attention to the most important signals, activities, and changes in an intelligent and context-sensitive manner. **Challenge 2 - Adequate models:** Intelligent agents must be able to effectively model other participants' intentions and actions concerning the joint activity, discerning issues like whether they're struggling or on a different trajectory. Good rules to live by. It would be interesting to identify the possible applications to the study of groups and diffusion/innovation. #### TL;DR AI is becoming increasingly integrated into our daily lives and professional environments. Understanding how to effectively collaborate with these systems is paramount. This paper discusses the requirements for creating intelligent agents that can collaborate with humans. In an era where AI's role is expanding this paper leads us to think about AI systems not just tools but as collaborative partners. **The main takeaways of this paper:** - Basic Compact: Intelligent agents must communicate struggles or failures to human counterparts. - Predictability: Both human and agent team members should be mutually predictable. Behavior should be transparent and expected. - Goal Negotiation: Agents should be capable of adapting to changing situations. Continuous communication and goal alignment are important. - Collaboration: Technologies for agent planning and autonomy should be designed for collaboration, promoting incremental and adaptable processes. As smart systems and AI become more pervasive in our daily lives it's important to create a symbiotic relationship between humans and machines. Predictability, collaboration, and goal negotiation are important aspects to make sure that AI understands, adapts, and evolves with human intentions.