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Information Technology
and C4ISR 14 March 2011
James H. Lawton, PhDC4ISR/IT
European Office of Aerospace R&D
Air Force Office of Scientific Research
AFOSR
Distribution A: Approved for public release; distribution is unlimited. 88ABW-2011-0782
2
2010 AFOSR SPRING REVIEWC4ISR/IT PORTFOLIO OVERVIEW
NAME: James H. Lawton
BRIEF DESCRIPTION OF PORTFOLIO:
International Research in C4ISR and Information Technologies
LIST SUB-AREAS IN PORTFOLIO:
• Command & Control (C2)
Planning and Scheduling
• Software and Systems
• Information Assurance
• Information Management
• Quantum Information Processing
• Complex networks
• Signal Processing (RF)Other
• Primarily AFRL (RY, RI and AFOSR)
• ONR(G), ARMY - VTT, NBU, CTU
• DARPA (Kohout) - C2 planning
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EOARD C4ISR/IT Topics being Emphasized, Decreased, Pursued or Transitioned
• C2 Planning and Scheduling
• Software and Systems
• Information Assurance
• Information Management
• Complex Networks
• Quantum Information Processing
• Signal Processing
4
FY 09/10 Research Portfolio
United KingdomC2 Planning
Sensor Prognostics
MAS Comm & Control
Software Dependability
Information management
Spain HPC
GermanyStegonography
Steganalysis
GreeceAdHoc Network Modeling
Czech RepublicAdversarial Planning
Network Monitoring
PolandWireless Info Xfer
HungaryData mining
ItalyWaveform Diversity
Information management
Israel Uncertainty Management
Software regression verification
Ukraine Multimodular recurrent NNs
Bulgaria Analogical Planning
The NetherlandsInformation management
QIP (w/Physics PM)
FinlandCog Networking
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Scientific ChallengesC2 Planning & Scheduling
• Motivation: Need to improve the speed, quality and creativity of process
– Want to use information technology for improvements
– Computationally intractable (NP-complete)
• Specific C2 P&S questions being addressed:
Analogical reasoning (Petkov)
• People can plan/schedule in highly complex domains using experience –
How?
• Extend existing fundamental analogical reasoning model to address
complex planning domains
• Transformational: make it tractable, change the way we plan
Plan-representation (Wickler)
• Find fundamental components of a “plan”
• Define representation for mixed-initiative interaction
• Transformational: lead to true mixed-initiative planning & execution
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“Adaptive Problem Solving by Analogy”Georgi Petkov, New Bulgarian University
• Analogy-making is a fundamental cognitive ability that lets us perceive
the world and behave effectively by taking into account our past
experience.
• Problem solving and learning are not separate processes but run in
parallel and influence each other.
• Objective: Analogies for Planning
– Explore how certain cognitive mechanisms can be modelled within
the DUAL cognitive architecture and how they would enhance its
ability to do problem-solving by analogy.
• goal-driven and context-sensitive reasoning,
• knowledge abstraction and abstract concepts formation,
• generalization of known solutions, and
• adaptation of old memory traces.
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Anticipation by Analogy
?
Representation of the target; retrieval of a base; mapping;
transfer of an anticipation; action
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DUAL (Kokinov, 1994) Multi-agent Hybrid Architecture
• Each agents has:
– Symbolic part: represents small pieces of knowledge
(features, relations, propositions)
– Connectionist part: represents its relevance
• Integration of the Representation in Hybrid Micro-Agents
– Agent actions include: marker passing, hypothesis
generation, generalization
– Possibilities for context-dependent restructuring of the
knowledge base – the connectionist activation is considered
as power supply for the symbolic processor.
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Long-Term Memory and WM in DUAL Architecture and AMBR Model
focus
Goal node
Input nodeLTM
WM
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AMBR Reasoning System
• Reasoning component utilizing the DUAL architecture, that unifies deductive, inductive and analogical reasoning
• All processes emerge from the local interactions between the nodes; no global control is exerted;
• All processes run in parallel and influence each other:
– the retrieval process runs continuously and changes the relevance and therefore changes the mapping;
– the mapping process creates new nodes (hypotheses) and links between them and in this way it changes the spreading activation process.
• Basic mechanisms
– Spreading of activation
– Marker passing and formation of hypotheses
– Formation of anticipations (transfer)
– Constraint-satisfaction network
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Cued Recall in AMBR
glass
is-broken
kitchen
hplate
cause
on
binding node
yesterday
glass
kitchen
binding node
yesterday
Cue=Target Base=Old Episode
cause
on
is-broken
hplate
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Example 1 – “Terrorist” Simulation
terrorist Asuicide
Kamikaze Bsuicide
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Kamikaze Bsuicide
motivation
Prosperity of
the country
Prosperity of
the emperor
Honor of the
family
Japan
Movie
“Shogun”
England
Ireland
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terrorist Asuicide
Motivation
(CONCEPT)
motivationnostalgia
Un-satisfaction
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Base “Immigrant”
motivationnostalgia
Un-satisfaction
Ireland
Immigrant CBeating his
wife
Bulgarian
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Beating his
wifeImmigrant C
Open
Bulgarian
restaurantCause
Stop beating
his wife
Popularizing
traditional
culture
Stop bad
actionsCause
Is-aIs-a
Is-a
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“Terrorist” example demonstrates
• Analogies between relatively remote
domains;
• Using multiple analogies to re-represent the
target and to retrieve the more appropriate
remote base;
• Transfer and adapting of solutions.
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Scientific ChallengesSoftware and Systems
• Motivation: “Construct and deliver with confidence software-intensive systems” (Luginbuhl)
– Very difficult to formally ensure correctness and non-functional properties
– Need to extend formalisms to systems engineering
• Specific S&S problems being addressed
Software Regression Verification (Strichman)
• Tractable testing of complex software, approaching solution to Hoare‟s „Grand
Challenge‟
• Needed for legacy and other non-formally defined systems
• Transformational: Software Engineers will need understand formal representation &
specification, this is a middle step
Formal modelling of multi-core programming (Labarta)
• Define task-based programming model that auto-magically exploits parallelism and
maintains correctness
• Transformational: Many cores ARE coming (here? University of Glasgow 1000-core),
need to take advantage of them
• Transition success
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Software Errors
• USS Yorktown towed to port after
Windows NT divide-by-zero error
(1997)
• Mars Climate Orbiter lost due to
Metric-English unit mismatch
(1999)
• STUXNET worm: targeted
Embedded controllers at Bushehr
Nuclear Plant (2010)
Software Regression Verification: Proving the equivalence of similar programs
Benny Godlin Ofer Strichman
Technion, Haifa, Israel
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Functional Verification
• The main pillar of the grand challenge [Hoare‟03]
– “... the construction and application of a verifying compiler that
guarantees correctness of a program before running it.”
• A more modest challenge: Regression Verification
– Develop a method for formally verifying the equivalence of two similar
programs.
– Pros:
• Default specification = earlier version.
• Computationally easier than functional verification.
– Ideally, the complexity should depend on the semantic difference
between the programs, and not on their size.
– Cons:
• Defines a weaker notion of correctness.
“For every problem that you cannot solve, there is an easier problem that
you cannot solve” (George Polya, in How To Solve It)
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Hoare‟s
1969
paper
has it all.
. . .
…and 6 pages later:
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Project Goals
• Develop notions of equivalence
• Develop corresponding proof rules
• Present a prototype for verifying equivalence of C
programs, that
– incorporates the proof rules
– sensitive to the magnitude of change rather than
the magnitude of the programs
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Notions of equivalence
1. Partial equivalence
Executions of P1 and P2 on equal inputs that terminate, and result in equal outputs
2. Mutual termination
Given equal inputs: P1 terminates P2 terminates
3. Reactive equivalence
Let P1 and P2 be reactive programs. Executions of P1 and P2 which read the same input sequence emit the same output sequence
4. k-equivalence
Executions of P1 and P2 on equal inputs where loops and recursions are restricted to kiterations, result in equal output
5. Full equivalence* = Partial equivalence + mutual termination
6. Total equivalence** = partial equivalence + both terminate
Note: all but k-equivalence are undecidable
* Appeared in Luckham, Park, and M. Paterson [LPP-70] / Pratt [P-71]
** Appeared in Bouge and D. Cachera [BC-97]
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Hoare‟s Rule for Recursion
Let A be a recursive function.
“… The solution... is simple and dramatic: to permit the use of the
desired conclusion as a hypothesis in the proof of the body itself.”
[H’71]
If
Then
Syntactically Entails
(can be derived from)
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//in[A]
A( . . . )
{
. . .
//in[call A]
call A(. . .);
//out[call A]
. . .
}
//out[A]
Proving partial equivalence
A B//in[B]
B( . . . )
{
. . .
// in[call B]
call B(. . .);
//out[call B]
. . .
}
//out[B]
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Proving partial equivalence
The premise is
decidable
• Let AUF , BUF be A,B, after replacing the recursive call with a call to (the
same) uninterpreted function (terminates and produce the same value on
same input).
• We can now rewrite the rule:
2929
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Regression Verification Summary
• Regression verification is an important problem
– A solution to this problem has a better chance to succeed in the
industry than functional verification
– A grand challenge by its own right…
• This project will extend the exiting regression verification
formalism:
– increase the number of cases of proving partial-equivalence can
handle, by making the inference rules more complete
– develop automatic methods for devising invariants that will enable
us to prove partial equivalence in more cases
– develop methods for proving mutual termination between programs
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Recent Transitions
• AgentFly
– ARMY funding flight tests (seeking partners)
• Adversarial Planning
– Cyberspace project fully funded by AFRL
• Barcelona Supercomputing Center
– Follow-on project funding from AFRL
• Waveform Diversity (Greco)
– Transition project in low-cost sensor array (Mike
Wickes)
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Contact Info
James LawtonC4ISR/ITEOARDUnit 4515 Box 14APO AE 09421
Phone: +44 (0) 1895 616187 (Do not use initial 0 if calling from outside the UK) DSN 314 235-6187email: [email protected]
Backup Slides
AgentFly Transitions
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Agent-Based Computing in Distributed Adversarial Planning
Michal Pechoucek, Czech Tech Univ
• Adversarial planning: a decision-making process
through which an agent constructs a sequence of
actions (possibly consisting of a single action only)
leading to the desirable goal state of the world in an
adversarial situation, i.e., when other adversarial
agents are concurrently operating in the world.
• Interaction Stances for actions:
• Self-Interested: action of group G is self-interested if it
increases the utility of G
• Cooperative: an action of G is cooperative with H if it
changes the world in a way that increases the utility of
both groups G & H
• Competitive: an action of G is competitive with H if it
does not decrease the utility of H more than it increases
the utility of G
• Adversarial: an action of G is adversarial with H if it
lowers the utility of H more then it increases the utility
of G, or even decreases the utilities of both the groups.
• Altruistic: as an action of G that helps (increases the
utility of) H while lowering the utility of G
• Usage: Adversarial Potential occurs if most of self-
interested actions are competitive or adversarial.
Backup Slides
Plan Representations for Distributed Planning and Execution
38
“Plan Representations for Distributed Planning and Execution”
Gerhard Wickler, Edinburgh University
• Objective: Develop a formal model describing
the key plan representation aspects needed to
support automated planning and scheduling by
distributed software agents
• Approach: Extend Core Plan Representation
(CPR) by examining and adding concepts from
the Beliefs-Desires-Intensions (BDI) model of
software agency that explicitly account for the
agents‟ contributions to the distributed planning
problem
• Core Benefits:
– CPR/BDI structured plans:
• better indexing of experience
databases
• richer plan descriptions support
execution as intended
– structured execution protocols
• principled way of monitoring progress
and state
• principled way of dealing deviations
Plan
Plan Plan Plan
Plan Plan Plan Plan
STRATEGIC
OPERATIONAL
TACTICAL
INCA/CPR+BDI: Shared PlansPlan
Objective
Actor
TimePoint
Resource
Evaluation
Criterion
Intention
Belief
Desire
has-a
is-a
Activity
Issue
Annotation
Constraint Signature
Parameter
Effect
Precondition
Name
Capability
Activity
Method
State-Belief
Backup Slides
Multcore Programming
41
“Programming models for heterogeneous multicore systems”
Jesus Labarta, Barcelona Supercomputing Center
• Background: To efficiently use the multicore machines we need:
– Understanding of application behavior
– Programming models that can express apps in a general/abstract/portable way.
– Intelligent compilers and runtime environments capable of executing very efficiently those
applications on any target machine.
• Project Objectives:
– Refine the StarSs programming model developed at BSC for multicore processors.
– Apply performance analysis tools developed at BSC to better understand parallelism in AFRL-
provided example applications
• Project Approach:
• Programming model:
– Extend dependency mechanism
– Add support for new core/device type (GPUs)
– Handling of partially aliased and strided references
– Hybrid MPI/SPMSs
• Performance analysis:
– Analyze and improve performance of AFRL-provided
apps
– Determined that tools can be extended with pattern
matching techniques
