iscram summerschool 2009 lecture - teaming with machines (martijn neef)

The role of autonomous systems in collaborative environments

Teaming with Machines

Martijn NeefTNO Defence, Safety and Security

ISCRAM Summerschool 2009Teaming with Machines1

My name is…

Martijn NeefNetworked Organizations GroupBusiness Unit Information and OperationsTNO Defence, Security and SafetyThe Hague, The Netherlandse-mail: [email protected]

• Artificial Intelligence• Collaborative Decision Making• Networked Organisations• Human – Machine Systems


Lecture overview

• Part I: Teaming with machines• On teams, machines and autonomy• From automation to joint cognitive system design

• Part II: Designing Human – Machine teams• Case study: Augmented Team design, • A practical example from our work at TNO Safety and Security

• Part III: Implications and Discussion• Summary and implications• Points to ponder

On teams, machines and autonomyPart I: Teaming with Machines



We are surrounded by machines

• Our working and living environments are filled with machines, systems and networks

• communication devices, computers, sensors, networks, informationsources, actuators, displays, and so on…

• we are connected with and dependent on humans and machines in many ways..


We are being surrounded by smarter machines

• Technological developments produce smarter systems• systems that exhibit smart behaviours• new response options through smart coupling of capabilities• more prominent and active role for intelligent, autonomous systems • artificial systems are being granted more autonomy.

• Lots of examples in security, safety and space• robots and unmanned vehicles• information management systems and intelligent agents• traffic management systems• safety and self-defense systems


We need a new way of thinking about teams

• Technology will:• become more capable, adaptive and reliable• be given more autonomy to define their own behaviour

• Increase of system autonomy leads to different ways of working with machines:

• technology role goes from supplement to active participant• task groups will gradually develop into hybrid teams• we need for a new understanding of the dynamics of hybrid teams

task allocation between man and machineinteraction patterns between man and machinecoordination and communicationauthorization and responsibility…


team

team


Human – Machine teams

• What is a human – machine team, what is not?• what is a team? • what must an artificial system be capable of? • what are the features and implications of a human – machine team?

• Why is this an important area of research?• we need new capabilities to face modern challenges• we need to learn to work together with advanced systems• tight collaboration between humans and machines yields operational

benefits… complement each other’s strengths and weaknesses.


What is a team?

Defining Characteristics of Teams

• Two or more individuals• Multiple information sources• Meaningful task

interdependencies• Coordination among members• Common, valued goals

(Salas, 1995)

• Specialized member roles and responsibilities

• Task-relevant knowledge• Intensive communication• Adaptive mechanisms• Hierarchically organized

requires a lot of mutual understandingrequires a common groundingrequires training and trustusually only used for human teams


Teaming with machines

• humans working together with artificial actors


Human – Machine Teams


Human – machine teams


‘Man – computer symbiosis’

Man-computer symbiosis is an expected development in cooperative interaction between men and electronic computers. Itwill involve very close coupling between the human and the electronic members of the partnership.

The main aims are 1) to let computers facilitate formulative thinkingas they now facilitate the solution of formulated problems, and 2) to enable men and computers to cooperate in making decisions and controlling complex situations without inflexible dependenceon predetermined programs.

J.C.R. Licklider (1915-1990), Man-Computer SymbiosisIRE Transactions on Human Factors in Electronics,

volume HFE-1, pages 4-11, March 1960


From automation to socio-technical design

Based on Chalmers (2001)

make technology more capable

make the joint human – machine teammore capable


The Substitution Myth

The Substitution Myth It is a common myth that artefacts can be value neutral in the sense that the introduction of an artefact into a system only has the intended and no unintended effects. The basis for this myth is the concept of interchangeability as used in the production industry, and as it was the basis for mass production –even before Henry Ford. Thus if we have a number of identical parts, we can replace one part by another without any adverse effects, i.e. without any side-effects. …

While this in practice holds for simple artefacts, on the level of nuts and bolts, it does not hold for complex artefacts. A complex artefact, which is active rather than passive, ie, one that requires some kind of interaction either with other artefacts or subsystems, is never value neutral. In other words, introducing such an artefact in a system will cause changes that may go beyond what was intended and be unwanted

Joint cognitive systems: foundations of cognitive systems engineering,Erik Hollnagel, David D. Woods.


Task division and coordination

• MABA-MABA: Men Are Better At, Machines Are Better At..

• Task division steers coordination.• Coordination renders tasks.

• Challenge: find types of tasks that are suitable for each actor, and designate tasks that can be performed by more than one.

typical humantasks

collaborativetasks

typical agenttasks

systemtasks

knowledge intensiveness


Fitt

s’Li

st (

1951

)


Computationally instantiate their models of the world.

They are aware of the fact that the model of the world is itself in the world.

Effect positive change following situation change.

Adaptability to change is high and is goal-driven.

Help them align and repair their perceptions because they rely on mediated stimuli.

Sensitivity to change is high and is driven by the recognition of anomaly.

Help them stay informed of ongoing events.

Sensitivity to context is high and is knowledge- and attention-driven.

Yet people create machines to:People are not limited in that:

Keep the model aligned with the world.

They are not ‘aware ’ of the fact that the model of the world is itself in the world.

Repair their ontologies.Adaptability to change is low and is ontology-limited.

Keep them stable given the variability and change inherent in the world.

Sensitivity to change is low and recognition of anomaly is ontology-limited.

Keep them aligned to context.Sensitivity to context is low and is ontology-limited.

Machines need people to:Machines are constrained in that: Un-Fitts’ listHoffman (2002)


Parasuraman (2000)

Levels of automation


Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity1. To be a team player, an agent must fulfil the requirements of a Basic Compact

to engage in common grounding activities2. To be an effective team player, agents must be able to adequately model the

other participant’s intents and actions vis-à-vis the state and evolution of the joint activity – e.g. are they having trouble? Are they on a standard path proceeding smoothly? What impasses have arisen? How have others adapted to disruptions in the plan?

3. Human-agent team members must be mutually predictable4. Agents must be directable5. Agents must be able to make pertinent aspects of their status and intentions

obvious to their team-mates6. Agents must be able to observe and interpret signals of status and intentions.7. Agents must be able to engage in goal negotiations8. Support technology for planning and autonomy must enable a collaborative

approach9. Agents must be able to participate in the management of attention10. All team members muist help control the costs of coordinated activity

Klein (2004)


Autonomy

• Autonomy: to have control over own internal state and behaviour• Challenge: control the autonomy of autonomous systems

(Bradshaw, 2003)


Example: Human – Agent – Robot Teams

• Work from Institute for Human and Machine Cognition• Jeffrey Bradshaw

• Demonstrate the feasibility of a human – machine team for a security task – intruder detection and apprehension by a team of humans and robots.

• Smart use of agreements and coordination support to optimize joint performance.

• (movie of HART in action)

Case: Augmented Teams for Security Missions

Part II: Designing Human – Machine teams



Augmented Teams: Assembling Smart Sensors, Intelligent Networks and Humans into Agile Task Groups

Martijn NeefNetworked Organizations GroupBusiness Unit Information and OperationsTNO Defence, Security and SafetyThe Hague, The Netherlands

Martin van Rijn – Distributed Sensor Systems GroupJan Willem Marck – Distributed Sensor Systems GroupDanielle Keus – Modelling, Simulation and Gaming Group


Augmented Teams

We are exploring design principles for augmented teams.

An augmented team consists of a collective of sensors, actuators, information processing systems and humans

• that are interconnected by a intelligent network• that collaborate in a close and adaptive fashion, and • that, by presence of the artificial actors, augment the capabilities of

the human actors alone.


Design challenges

Can we come up with a design concept for augmented teams..

• .. with adaptive role- and tasking capabilities between human and artificial actors

• Essential for agility and resilience. The team must be able to cope with changing circumstances, e.g. by changing the behaviour or structure of the organization.

• .. that is suitable for the current and future state of technology• Prevent technology bias. Limit the influence of the current state or

technology on the approach as much as possible.


Design challenges

Basic challenges• It must be possible to change the organization structure• The team must be configurable at run-time• All elements must be network-connected• Actors must be able to represent themselves• The information flow must self-organize

cont

rol

info

rmat

ion

self organisingadaptive team

hybrid team withclear divison of labour

human teamwith sensor network


Approach

• Three main ingredients:

• Functional model• Provides a functional blueprint for augmented teams

• Organization modeling framework• Provides means to structure interactions and responsibilities

• Social and interaction contracts• Provides a way to specify community rules and collaboration demands


Functional Model

• Networked Adaptive Interactive Hybrid Systems model (NAIHS)

• Blueprint for networked cognitive systems, grounded in the JDL model

• Elements fulfill functional components.

• Three steering dimensions:• level of information abstraction• timescale of effects• physical structure

Environment

Signalassessment

Object assessment

Situationassessment

Impact assessment

Signalmanagement

Object management

Situationmanagement

Impactmanagement

Data collection

ActionsPhysical Level

Level 0

Level 1

Level 2

Level 3

Situation Awareness

Command & Control


Organizational Model

• Based on OperA (Virginia Dignum, Utrecht University, NL). • framework for the specification of multi-agent organizations• uses a formal specification language


Social contracts, Interaction contracts

• Social contracts• General agreements that need to be adopted to become part of the

organization (job contracts)• organizational aspects (norms and policies, coordination scheme,

organization structure)• social rules, administrative rules, communication language …

• Interaction contracts • collaborative agreements between actors per task

• interaction behaviour (relation between parties, task division)• format and conditions


Putting it all together…

a) a functional model to structure the general system

b) three levels of abstraction to represent dependencies and interactions

c) social and interaction contracts to put the organization into practice


Adaptivity and agility

Severe• The organization is redesigned to some degree. This might involve added or deleting roles, changing objectives or behavior rules. Changes on this level might necessitate changes in the Social and Interaction Model too. • Example: Because several elements have stopped working, the objectives can no longer be reached. In response, new objectives are set with the remaining set of elements.

Real-time adjustments in the Organizational Model

3

Medium• A role is transferred from one element to another element that is better qualified. • Example: the ‘coordinator’ role is transferred from the actor in the control room to an actor in the field, because he is in a better position to coordinate other actors.

Real-time adjustments in the Social Model.

2

Low• Elements change their interaction agreements to adapt to a certain situation. • Example: Two elements decide to use a different form of communication in response to new circumstances.

Real-time adjustments in the Interaction Model

1

Impact DescriptionChangesLevel

� Organization levels give means to express adaptive measures


Adaptivity and agility

� Approachs allows for gradual introduction of new elements� For instance: introduce artificial actors at higher functional levels

artificial actors at higher functional levels

artificial actors at lowerfunctional levels


autonomy and task allocation

choicesbetween actors

information needs and

accessibility

coordination and control methods


FieldLab Indoor Safety and Security

• Fieldlab Indoor Safety and Security

• Components• Wireless positioning

system• Network of smart

cameras• Communication devices• Tracking and position

prediction service• Information fusion

services• Command center with

common operational picture

• Basic scenarios: intruder apprehension andincident management


Fieldlab Indoor Safety and Security


• Network built around a service oriented network (RESTlet)• Human contracts are still just ‘on paper’, but used strictly (before

and during an experiment)

• Some initial studies• transfer of the coordinator role from the central position to a mobile

guard (role transfer among humans)• transfer intruder tracking role from the camera network to coordinator

(task transfer from system to human)• transfer tracking from positioning system to coordinator (task

transfer from system to human)

• (Movie)

Experiments


Roles changes

B


Some observations

• Observations• Using organizational models and contracts seems worthwhile to

express adaptivity and interaction dynamics in man – machine organizations

• Functional model helps to make basic allocation choices

• it is easy to lose control over the situation after role change,especially for the coordinator role – even in the case of human –human role transfer.

• Need for periodical synchronization of organization awareness and situation awareness is evident


Concerns

• Design concerns

• Define who is responsible for role and task allocat ion• Set boundaries for dynamic allocation• Ensure observability of attributes and responsibili ties• Make the type of adaptivity a design choice• Prevent issues caused by multi-level or multi-role allocation• Prevent communication and interaction issues after role change• Prevent loss of situation and system awareness amon g humans• Counter complacency and skill degredation• Prevent unneccessary increase of mental workload• Gradually build up user acceptance


Scenario 2009

• Incident observation and reconnaissance

• Incident assessment and plan creation

• Incident mitigation and evacuation

• Large set of virtual and actual sensors

• Players get adaptive communication devices and new task coordination tools.

B

command center

VIS

DTA

SA

detectie

Alarm

Alarm

Taak

B B B

B B B

B

B

B


Further developments

Current developments:• Further formalize interaction contracts and contract manage ment,

especially between human and artificial actors• Explore dynamic task allocation schemes• Explore ways to balance self-organising capabilities and procedures• New series of experiments with additional technology (new services,

new mobile devices, more sensors) and an extensive scenario(incident management and evacuation, larger set of human actors)

Other applications under development:• Damage control teams aboard naval frigats• Various distributed sensor network applications• Training environments for civil firefighter teams

Points to ponder

Part III: Implications and Discussion



Implications

• Machine teaming is not the stuff of science-fiction movies. It’s already here.. even though in simple forms..

• Technological advances will require us to rethink collaborative work.

• We need to pay attention to autonomy, ethics and accountability.

• Watch out for automation surprises and uncontrollable adaptivity.


Discussion

• Do you have any personal experiences (good or bad) with technology that resemble man – machine teaming? Or clearly show the need to rethink man – machine collaborations?

• Can you imagine man – machine teaming scenarios for crisis response scenarios?

• What would be the impact of such developments on crisis management organisations? Would the organisation actually change, or work differently?


thank you for your attention!


References (relevant own)

• Neef, Martijn (2006), A Taxonomy of Human - Agent Team Collaborations. In Proceedings of the 18th BeNeLux Conference on Artificial Intelligence (BNAIC 2006), 5-6 October 2006, Namur, Belgium, pp. 245-250.

• Neef, M., van Rijn, M., Keus, D., Marck, J-W. (2009), Organizing smart networks and humans into augmented teams. Proceedings of the 13th International Conference on Human-Computer Interaction, 19-24 July 2009, San Diego, CA, USA, Springer-Verlag: Lecture Notes on Computer Science, Berlin Heidelberg.

• Neef, M., van der Vecht, B. (2009), Agility Through Adaptive Autonomy, Proceedings of the 14th International Command and Control Research and Technology Symposium (ICCRTS 2009), 15-17 June 2009, Washington D.C., USA.

• Neef, M., Maanen, P.-P. van, Petiet, P., Spoelstra, M. (2009), Adaptive Work-Centered and Human-Aware Support Agents for Augmented Cognition in Tactical Environments. In: Proceedings of International Conference on Augmented Cognition, Jointly held with International Conference on Human-Computer Interaction, 2009


References (others)

• Salas, E., Dickinson, T. L., Converse, S. A., and Tannenbaum, S. I. (1992), Toward an understanding of team performance and training. In R. W. Swezey & E. Salas (Eds.), Teams: Their training and performance. Ablex, Norwood, USA, pp. 3-29.

• Licklider, L.C.R. (1960), Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics, v.HFE-1, pp. 4-11.

• Chalmers, B.A. (2001), Design frameworks for computer-based decision support. DREA Technical Memorandum, TM 2001-210, Defence Research Establishment Atlantic, Ottawa, Canada.

• Fitts, P. M. (1951), Human Engineering for an Effective Air Navigation and Traffic Control System. National Research Council, Washington, D.C., USA.

• Hoffman, R.R., Feltovich, P.J., Ford, K.M., Woods, D.D., Klein, G., Feltovich, A. (2002), A Rose by Any Other Name...Would Probably Be Given an Acronym. IEEE Intelligent Systems 17(4): 72-80 (2002)

• Parasuraman, R., T. B. Sheridan, et al. (2000), A Model for Types and Levels of Human Interaction with Automation. IEEE Transactions on Systems, Man, and Cybernetics 30(3), pp. 286-297.


References

• Hollnagel, E. & Woods, D. D. (2005). Joint cognitive systems: Foundations of cognitive systems engineering. Boca Raton, FL: CRC Press / Taylor & Francis.

• Klein, G., Woods, D.D., Bradshaw, J.M., Hoffman, R.R. and Feltovich, P.J. (2004), Ten Challenges for Making Automation a "Team Player" in Joint Human-Agent Activity. IEEE Intelligent Systems 19(6), pp. 91-95.

• Bradshaw, J. M., Sierhuis, M., Acquisti, A., Feltovich, P., Hoffman, R., Jeffers, R., Prescott, D., Suri, N., Uszok, A., and Van Hoof, R. (2003), Adjustable autonomy and human - agent teamwork in practice: An interim report on space applications. In H. Hexmoor, R. Falcone, & C. Castelfranchi (eds.), Agent Autonomy, Dordrecht, The Netherlands: Kluwer, pp. 243-280.

• Kester, L.J.H.M. (2008), Designing Networked Adaptive Interactive Hybrid Systems. Proceed-ings of the IEEE International Conference on Multisensor Fusion and Integration for Intelli-gent Systems 2008 (MFI2008), 20-22 August 2008, Seoul, Republic of Korea, pp. 516-521.

• Dignum, V., Dignum, F., Meyer, J-J.Ch. (2004), An Agent-Mediated Approach to the Support of Knowledge Sharing in Organizations. Knowledge Engineering Review, Cambridge Uni-versity Press, 19(2), pp. 147-174.

iscram summerschool 2009 lecture - teaming with machines (martijn neef)

Technology

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