Toward mission-specific service utility estimation using analytic stochastic process models

1
Toward mission-specific service utility
estimation using analytic stochastic process
models

• Dave Thornley
• International Technology Alliance
• http//usukita.org
• Imperial College London

2
Quality, Utility, Value

• Quality of Information (QoI) used as a focus for
  comprehension, generality and communication
• What does it mean?
• What else could have told me this?
• What guarantees can we provide?
• Supports choices during action
• Utility of information (UoI) or another service
  supports design choices
• Will this system support our achievement of
  goals, and how well?
• Will it still work when weve finished with it
• Can we sell it or its information products?
• Given utility estimates for some purpose, we can
  assess the value that should/will be ascribed
  (VoI)
• Will be ascribed This piece of information makes
  my life easier.
• Should be ascribed No it doesnt

3
Mission Abstraction, Requirements and Structure

• PLANs provide structure and projections
• PHYsics includes sensor models, traffic
  generators and environmental modulators
• INTelligence includes receipt of signals, fusion,
  storage, hypothesis and dissemination
• Situational Awareness maps knowledge to awareness
  and understanding (more next slide)
• Decision Maker is a representation of the human
  in the loop
• ACTion maps decisions to physical outcomes via
  effectiveness measures

4
Abstract stochastic perspective

• In a given deployment, predictable outcomes are
  influenced by sensing service design choices
• A sensing package that results in better outcomes
  for the same plan is providing higher quality of
  information amortized over the mission, and is of
  higher utility specifically to that mission
• Consider the outcomes as a locus of
  possibilities, which may be a combination of
  discrete and continuous variables (target
  location and assessment, sensor energy remaining
  and functional integrity).
• A stochastic model associates probabilities with
  states as a function of time. If we ensure that
  there is a state defined for each outcome we care
  about, we can quantifies the contributions of
  alternative services in characteristics that can
  be meaningfully compared

5
Information driven model

• Detection cues tracking
• Tracking enables focus of various types through
  intelligence gathering
• Alternative competing hypotheses are evaluated
  using intelligence product arrivals and
  retrieval/ refactoring to achieve focus and
  situational awareness
• Decisions drive action or instruct sources
• Action creates feedback

6
Keithleys Knowledge Matrix
The matrix was originally developed to assess the
value of information fusion algorithms to C4ISTAR
missions to justify the cost of their
development. ISR requirements are specified in
terms of a canonical set of questions. The
questions need to be supported by details of the
required QoI for the mission to succeed. The ISR
question is answered at the level of the
commanders information requirements not the data
level. BUT insufficiently flexible to allow a
detailed consideration for matching resources to
dynamic mission requirements.
7
Timed stochastic outcome modeling for utility
comparison
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Methods

• Performance Analysis Process Algebra
• Compositional timed stochastic modeling
• Abstract to information product delivery and
  operational modes
• Can be massaged into a range of solution tools
• Native model is a continuous time discrete state
  Markov chain
• Equilibrium solutions
• Measurement of consumption and exposure
• Transient solutions
• Response time predictions
• Evolution of accuracy achievable

9
INCIDER

• DSTL human factors team
• Our example scenario is lifted directly and
  simplified somewhat from one of their
  presentations
• www.dodccrp.org/events/2006_CCRTS/html/presentatio
  ns/025.pdf
• Also see
• Dean, D., Vincent, A., Mistry, B., Hossain, A.,
  Spaans, M. and Petiet, P., Representing a Combat
  ID Analysis Tool within an Agent Based
  Constructive Simulation, The International C2
  Journal, Vol 2, No 2

10
Isolated decision making scenario
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Scenario components
FAT ltsignalsgt ( (Sensors ltevidencegt SA) ltpolicygt
DM )

• Policy includes orders and tests
• Signals include EO interpretation, TID comms,
  Scout vision and HQ picture
• Evidence raises, lowers or sets confidence in Red
  and Blue hypotheses
• Model that can generate Red and Blue traffic, and
  the SA maintenance and decision making sequences
  for each has 1597 states, with a 5 phase Erlang
  FAT transition process

12
MARS Federated Analytic Traffic

• Entities are modeled as states that combine, in
  our example
• Location space subdivide according to
  invariants in the response of the mission
• Class, affiliation c. (mood?) just Red/Blue
  here
• Multiple sensing modalities must be modeled and
  correlated, so traffic centralized, and formed of
  components, each representing an entity or group
  of entities

13
FAT traffic progress
14
MARS Situational Awareness

• Confidence in each hypothesis Red, Blue
• Example has zero, low, medium, high
• In general, these demarcations will be selected
  according to regions on the real line that do not
  change the outcome of fusion
• Predicates calculated on these states
• Comparison of values (less/greater/equal)

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Evolution of SA and decisions
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Decision QoI
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Decision making utility
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Mission abstraction

• Priors on encounters and conditions enable
  definition of a traffic and environment generator
• Intelligence services formulated and composed
• Situational awareness maintained by an
  abstraction of the fusion functions to map
  intelligence products to SA upgrades
• Decisions taken by recognizing SA patterns
• Actions pursued leading to feedback to the
  mission physics

20
Abstracting space

• QoI emission characteristics constrain asset
  selections and operational modes
• Regions of validity of service output can be
  defined
• Optimization requires amortization over mission
  projections

21
Amortizing costs

• States can be defined for a composite sensing
  service in which measures of interest conform to
  appropriate invariants
• A specific combination of assets is active
• Battery energy consumption is approximately
  constant
• Personnel are at definable risk

22
Conclusions(question,T).Conclusions

• We have shapes for UoI comparison
• We can make those shapes from timed stochastic
  process models
• Those models can also estimate QoI
• The models are a link between QoI and UoI
• VoI that is subjective because of a situational
  awareness horizon may be found by, for example,
  marginalizing the dependency structures found in
  the equilibrium and transient solutions to the
  models
• When we manage to work in human factors, well
  have a handle on heuristically subjective VoI
• I hope this was an absorbing talk