Fermat's Library | Ten Challenges for Making Automation a “Team Player” in Joint Human-Agent Activity annotated/explained version.

#### 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...

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.

1,2

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

rhoffman@ai.uwf.edu

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.

Directability

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

unfolds.

Common ground

Finally, effective coordination requires

establishing and maintaining common

ground.

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 wasn’t 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 subordinate’s attention and

because the technology didn’t 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.

5–7

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 “understand” and

accept the enterprise’s 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

they’re 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 today’s agents

can communicate and reason about among

themselves, there’s 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.

Predictability

Challenge 3: Human-agent team mem-

bers must be mutually predictable.

To be a team player, an intelligent agent—

like a human—must be reasonably pre-

dictable and reasonably able to predict oth-

ers’actions. It should act neither capriciously

nor unobservably, and it should be able to

observe and correctly predict its teammates’

future behavior. Currently, however, agents’

“intelligence” and 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 operator’s working

style, the more reluctant operators might

be to adapt their own behavior because of

the confusions these adaptations might

create.

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.

Directability

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-

stood—that is directability.

3,11

Policies are a

means to dynamically regulate a system’s

behavior without changing code or requir-

ing the cooperation of the components being

governed.

6,9

Through policy, people can

precisely express bounds on autonomous

behavior in a way that’s consistent with

their appraisal of an agent’s 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 it’s 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.

This

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

agents.

Interpreting signals

Challenge 6: Agents must be able to

observe and interpret pertinent signals of

status and intentions.

Sending signals isn’t 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

environment.

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 can’t recognize the

operator’s stance, much less appreciate the

operator’s 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 human’s cognitive state through

a range of physiological measures in real

time. Similarly, a few research efforts are

taking seriously the agent’s 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 isn’t compatible with what we

know about optimal coordination in human-

agent interaction.

Collaboration

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.

Thus,

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 Ferguson’s 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

agent—like a human—must

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

wouldn’t help most agents)

Because the team members will be in

different states depending on how much of

their original plan they’ve 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 other’s 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

it’s having trouble and when it’s 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 it’s 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 that’s 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 expectation—to try to

achieve economy of effort. Coordination

requires continuing investment and hence

the power of the Basic Compact—a will-

ingness to invest energy and accommodate

others, rather than just performing alone in

one’s narrow scope and subgoals. Coordi-

nation doesn’t come for free, and coordina-

tion, once achieved, doesn’t 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 people’s

needs and knowledge.

The 10 challenges we’ve presented can

be viewed in different lights:

• As a blueprint for designing and evaluat-

ing intelligent systems—requirements

for successful operation and the avoid-

ance or mitigation of coordination

breakdowns.

• As cautionary tales about the ways that

technology can disrupt rather than support

coordination: Simply relying on explicit

procedures, such as common operating

pictures, isn’t 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 AI’s goal is to

create systems that emulate human capa-

bilities, versus the nontraditional human-

centered computing goal—to 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-

bility.

Agents might eventually be fellow

team members with humans in the way a

young child or a novice can be—subject

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 we’ve

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.

Acknowledgments

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 hasn’t yet failed?

When does an agent communicate

that it’s 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;

gary@decisionmaking.com.

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.

Discussion

**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.

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