The document discusses challenges for autonomous response to unexpected situations in complex environments. It proposes using "pick-up games" as a way to test and develop technologies for autonomous systems to respond to such situations. The document outlines several potential challenges and projects for the US Air Force Research Laboratory (AFRL) to lead in developing theories, tools, and techniques to enable effective autonomous response in situations involving strike, electronic warfare, cyber operations and intelligence, surveillance and reconnaissance. These include developing formalisms for situational representation, verification of intelligent machines, uncertainty management, and rapid training transfer for autonomous systems.

1. Collaborative Foresight for Long-
Range Problem Discovery in
Complex R&D
Kirk Weigand, United States Air Force Research Laboratory
&
Peter Jones, OCAD University
Public Release by the United States Air Force, Approved date: 2012-08-03, PA Approval Number: 88ABW-2012-4261

5. 5
A Figurative, tangible representation in a sand tray

6. Visual Narrative
Summary

8. “What challenges ought AFRL lead for autonomous response to the unexpected in
“pick-up” games enabling effective Strike, EW/Cyber & ISR?"
7: Develop theory for
how the (pick-up)
game will 'drive'
autonomous
response
15: Create modeling
& simulation tools for
complex joint human
machine systems
70: Providea data
representation scheme
that is unambiguous &
to the extent possible,
compact
11: Verifying
intelligent machines
are safe& effective
2: Generalize through
adaptive algorithms
based on prior
knowledge, rules, &
observations
76: Uncertainty
management - notion of
being able to generate
multiple plausible
explanations & futures
43: Develop
anticipatory/ predictive
prescriptiveanalytics for
unexpected query
47: Enable machine to
machine teaming to
include man & un-
manned with control&
commarchitectures
68: Transfer of learning
for rapid training
34:Develop formalismfor
situational representation -
reasoning & communication
4: Define a robust
scheme for shared,
accurate, unambiguous
goal representation
ZONE 1 ZONE 2
Evaluate agent &
cognitive theory of
Sit Representation
Define theory of
Situation
Develop
information
models for Sit Rep
Develop / research
effective human sit
rep understanding
Example actions / projects
blue (solutions)

9. 68: Transfer of
learning for rapid
training
76: Uncertainty
management –
generating
multiple plausible
explanations &
futures
2: Generalize
through adaptive
algorithms -
knowledge, rules,
& observations
47: Enable
machine to
machine teaming -
man & un-manned
with control &
comm arch
ZONE 3 ZONE 4 ZONE 5
Test & simulate
technologies as
required for ISR,
Strike, EW, Cyber
capabilities
Third Offset
Deterrence
Strategy
ZONE 2
Types of Pick-Up Games – and testing the required
Technologies as required for ISR, Strike, EW, Cyber capabilities
Break the linearity
of today’s PC-
PADX to achieve
insight fusion
“Combat Cloud”
ISR/EW/Cyber/
CID Merging
Ubiquitous
symbiotic
meaning making
to inform intent
Pick-Up Games
Substrate of
Autonomy
Change
Acquisition
Policy and
organizational
culture
Lower
system
manning
costs
Extend
manned
capabilities
Enhanced
high-risk
operation of
RPAs
Real-time
response to
cyber
threats
Representative outcomes
from Endsley
Strategic
Goal
Strategic
Outcome Strategic
Impact
Operational
Agility
Adaptive
Domain
Control
Global
Integrated
ISR
Rapid Global
Mobility
Global
Precision
Strike
Multi-
domain
Command &
Control

10. “What challenges ought AFRL lead for autonomous response to the unexpected in
“pick-up” games enabling effective Strike, EW/Cyber & ISR?"
7: Develop theory for
how the (pick-up)
game will 'drive'
autonomous
response
15: Create modeling
& simulation tools for
complex joint human
machine systems
70: Providea data
representation scheme
that is unambiguous &
to the extent possible,
compact
11: Verifying
intelligent machines
are safe& effective
2: Generalize through
adaptive algorithms
based on prior
knowledge, rules, &
observations
76: Uncertainty
management - notion of
being able to generate
multiple plausible
explanations & futures
43: Develop
anticipatory/ predictive
prescriptiveanalytics for
unexpected query
47: Enable machine to
machine teaming to
include man & un-
manned with control&
commarchitectures
68: Transfer of learning
for rapid training
34:Develop formalismfor
situational representation -
reasoning & communication
4: Define a robust
scheme for shared,
accurate, unambiguous
goal representation
68: Transfer of
learning for rapid
training
76: Uncertainty
management –
generating
multiple plausible
explanations &
futures
2: Generalize
through adaptive
algorithms -
knowledge, rules,
& observations
47: Enable
machine to
machine teaming -
man & un-manned
with control &
comm arch
ZONE 1 ZONE 3 ZONE 4 ZONE 5
Test & simulate
technologies as
required for ISR,
Strike, EW, Cyber
capabilities
Third Offset
Deterrence
Strategy
ZONE 2
Types of Pick-Up Games – and testing the required
Technologies as required for ISR, Strike, EW, Cyber capabilities
Break the linearity
of today’s PC-
PADX to achieve
insight fusion
“Combat Cloud”
ISR/EW/Cyber/
CID Merging
Ubiquitous
symbiotic
meaning making
to inform intent
Pick-Up Games
Substrate of
Autonomy
Change
Acquisition
Policy and
organizational
culture
Lower
system
manning
costs
Extend
manned
capabilities
Enhanced
high-risk
operation of
RPAs
Real-time
response to
cyber
threats
Evaluate agent &
cognitive theory of
Sit Representation
Define theory of
Situation
Develop
information
models for Sit Rep
Develop / research
effective human sit
rep understanding
Example actions / projects
blue (solutions)
Representative outcomes
from Endsley
Enabling Impacts
AF FOC 2015
Strategic
Goal
Strategic
Outcome Strategic
Impact
Operational
Agility
Adaptive
Domain
Control
Global
Integrated
ISR
Rapid Global
Mobility
Global
Precision
Strike
Multi-
domain
Command &
Control

11. Visual Narrative
Summary

12. 0
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2
3
4
5
6
7
8
9
10
Technology
Management
Influence
Precedence
Age
Culture
Stakeholder Variety
Steve Woods Arkin