Structure Control in Agentbased Simulation

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Structure Control in Agent-based Simulation

• Bernard P. Zeigler, Ph.D.,Co-Director,Arizona
  Center for Integrative Modeling and
  Simulationwww.acims.arizona.eduandJoint
  Interoperability Test CommandFort Huachuca, AZ
  85613-7051

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Outline

• Agent and multi-agent based simulation
• DEVS modeling and simulation
• DEVS support of agents
• Structure change control
• Application to distributed opportunistic testing
  of complex defense collaborative agent systems
• Some issues and implications

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References, Available fromwww.acims.arizona.edu

• Theory of Modeling and Simulation, 2nd Edition,
  Academic Press, Bernard P. Zeigler, Herbert
  Praehofer , Tag Gon Kim ,2000
• Nutaro, J., Hammonds, P., "Combining the
  Model/View/Control Design Pattern with the DEVS
  Formalism to Achieve Rigor and Reusability in
  Distributed Simulation",
• Zeigler, B. P., Fulton, D., Nutaro, J., Hammonds,
  P., "MS Enabled Testing of Distributed Systems
  Beyond Interoperability to Combat Effectiveness
  Assessment", 9th Annual Modeling and Simulation
  Workshop, Dec. 8-11, 2003, ITEA White Sands
  Chapter
• Zeigler, B.P., Fulton, D., Hammonds, P.,
  Nutoro., J., "Framework for MS-Based System
  Development and Testing in Net-centric
  Environment", in ITEA Journal, Nov, 2005
• Using Discrete Event Modeling and Simulation to
  Automate Testing In a Net-Centric Environment,
  Bernard P. Zeigler, Eddie Mak, Phillip Hammonds,
  Dale Fulton, Dasia Benson,Kimberly Nunn,

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Agent-Based Simulation

• some of the simulated entities are agents
• explicitly represents specific behaviors of
  specific individuals
• contrast with traditional macro-level aggregated
  representations
• extends object-oriented simulation
• facilitates simulation of group behavior in
  highly dynamic situations
• allows study of "emergent behavior"
• well-suited to populations of heterogeneous
  individuals
• vehicles (and pedestrians) in traffic situations
• actors in financial markets
• consumer behavior
• humans and machines in battle fields
• people in crowds
• animals and/or plants in eco-systems
• artificial creatures in computer games

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Multi-agent Systems

• A dynamic system might be described as a
  multi-agent system
• E.g. in a bio cell, agents are used as a metaphor
  to describe and understand the dynamics within
  the cell
• enzymes, DNA, and mRNA and repressors interact as
  autonomous reactive entities
• Suited for parallel and/or distributed simulation

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Spectrum of Agent Properties
domains
goal management capability
intention management capability
domain knowledge
belief management capability
language skills
agent model
decision making abilities
communication capabilities
perception abilities
manipulation skills
mobility skills
navigation skills
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Layered Architecture
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How is simulation software different from other
software?

• It represents the behavior of dynamic systems
  whose states are functionally dependent on time
• Properly controlling the flow of time is critical
• Simulation software may combine
• continuous (time-driven) and discrete
  (event-driven) processes
• actual operating hardware and software
  representations
• wall clock and faster/slower than real time
  advance

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DEVS Background

• DEVS Discrete Event System Specification
• Provides formal MS framework specification,simul
  ation
• Derived from Mathematical dynamical system
  theory
• Supports hierarchical, modular composition
• Object oriented implementation
• Supports discrete and continuous paradigms
• Exploits efficient parallel and distributed
  simulation techniques

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DEVS Hierarchical Modular Model Framework

• Atomic lowest level model, contains structural
  dynamics -- model level modularity

Coupled composed of one or more atomic and/or
coupled models
hierarchical construction
coupling
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Some Types of Models Represented in DEVS
Coupled Models
Atomic Models
Partial Differential Equations
can be components in a coupled model
Ordinary Differential Equation Models
Processing/ Queuing/ Coordinating
Networks, Collaborations
Physical Space
Spiking Neuron Networks
Spiking Neuron Models
Processing Networks
Petri Net Models
n-Dim Cell Space
Discrete Time/ StateChart Models
Stochastic Models
Cellular Automata
Quantized Integrator Models
Self Organized Criticality Models
Fuzzy Logic Models
Reactive Agent Models
Multi Agent Systems
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JAMES (Java-Based Agent Modeling Environment for
Simulation)

• DEVS-based framework facilitates experiments with
  agents under temporal and resource constraints
• supports
• endomorphy, i.e., models which contain internal
  models about themselves and their environment
• variable structure models, i.e. models whose
  description entails the possibility to change
  their own structure and behavior
• parallel distributed execution

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DEVS/RAPs

• RAP (Reactive Action Package)
• defines a tree of possible ways a task may be
  carried out with associated contingencies
• elementary constructs are query and action
  (command) events
• events are asynchronous messages generated
  internally or externally
• RAPs compose hierarchically to provide highly
  flexible reactive decision making

KIB (Knowledge Interchange Broker) handles
synchronization, concurrency, and timing of
interchanged messages
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Testing of interface standards is a focus area
for automated simulation-based testing. Link-16
is required in all Joint and multi-national
operations.
The Joint Interoperability Test Command (JITC)
has developed an automated test generation
(ATC-Gen) methodology as its core technology for
testing conformance of systems to Link-16 This
methodology is fundamentally enabled by the DEVS
formalized modeling and simulation
approachSelected as the winner in
the Cross-Function category for the 2004/2005
Department of Defense MS Awards
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ATC-Gen Goals and Approach

• Goals
• To increase the productivity and effectiveness
  of standards conformance testing (SCT) at Joint
  Interoperability Test Command (JITC)
• To apply systems theory, modeling and
  simulation concepts, and current software
  technology to (semi-)automate portions of
  conformance testing

Objective Automate Testing
Capture Specification as If-Then Rules in XML
Analyze Rules to Extract I/O Behavior
Synthesize DEVS Test Models
Test Driver Executes Models to Induce Testable
Behavior in System Under Test (SUT)
Interact With SUT Over Middleware
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Discrete Event Nature of Link-16 Specification
System Theory Provides Levels of
Structure/Behavior
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ATC Gen Process Overview

• Rule Capture in XML
• Analyst interprets the requirements text to
  extract state variables and rules, where rules
  are written in the form
• If P is true now Condition
• Then do action A later Consequence
• Unless Q occurs in the interim Exception
• Dependency Analysis Test Generation
• Dependency Analyzer (DA) determines the
  relationship between rules by identifying shared
  state variables
• Test Model Generator converts Analyst defined
  test sequences to executable simulation models
• Test Driver
• Test Driver interacts with and connects to SUT
  via HLA or Simple J interfaces to perform
  conformance testing
• Validated against legacy test tools

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Test Driver for Controlled Testing
Coupled Test Model
Component Test Model 1
Component Test Model 2
Component Test Model 3
Jx1,data1 Jx2,data2 Jx3,data3
Jx1,data1 Jx2,data2 Jx3,data3
Jx1,data1 Jx2,data2 Jx3,data3
Jx4,data4
Jx4,data4
Jx4,data4
Middleware
SUT
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Test Model Generation for Controlled Testing

• Mirroring (flipping) the transactions of a SUT
  model (system model behavior selected as a test
  case) allows automated creation of a test model

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Multiplatform Distributed Simulation -
Opportunistic testing
Platform (System, Component)
Platform (System, Component)
Platform (System, Component)
Observer
Observer
Observer
Test Coordinator
Distributed Observers look for opportunities to
test
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Test Manager for Opportunistic Testing

• Replace Test Models by Test Detectors
• Deploy Test Detectors in parallel, fed by the
  Observer
• Test Detector activates a test when its
  conditions are met
• Test results are sent to a Collector for further
  processing

Test Manager
Jx1,data1 Jx2,data2 Jx3,data3Jx4,data4
Test Detector 1
Results Collector
SUO
Observer
Test Detector 2
Other Federates
Test Detector 3
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Test Detector Generation for Opportunistic Testing

• The Test Detector watches for the arrival of the
  given subsequence of messages to the SUO and then
  watches for the corresponding system output
• Define a new primitive, processDetect, that
  replaces holdSend
• Test Detector
• Tries to match the initial subsequence of
  messages received by the SUO
• When the initial subsequence is successfully
  matched, it enables waitReceive (or
  waitNotReceive) to complete the test

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Problem with Fixed Set of Test Detectors

• after a test detector has been started up, a
  message may arrive that requires it to be
  re-initialized
• Parallel search and processing required by fixed
  presence of multiple test detectors under the
  test manager may limit the processing and/or
  number of monitor points
• does not allow for changing from one test focus
  to another in real-time, e.g. going from format
  testing to correlation testing once format the
  first has been satisfied

Solution

• on-demand inclusion of test detector instances
• remove detector when known to be finished
• employ DEVS variable structure capabilities
• requires intelligence to decide inclusion and
  removal

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Dynamic Inclusion/Removal of Test Detectors
Test Manager
Active Test Suite
Test Control
removeAncestorBrotherOf(TestControl")
message arrives
test detector subcomponent removes its enclosing
test detector when test case result is known
(either pass or fail)
add induced test detectors into test set
addModel(test detector) addCoupling2(" Test
Manager ",Jmessage",test detector", Jmessage")
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Example Joint Close Air Support (JCAS) Scenario
Natural Language Specification JTAC works with
ODA! JTAC is supported by a Predator! JTAC
requests ImmediateCAS to AWACS ! AWACS passes
requestImmediateCAS to CAOC! CAOC assigns
USMCAircraft to JTAC! CAOC sends readyOrder to
USMCAircraft ! USMCAircraft sends sitBriefRequest
to AWACS ! AWACS sends sitBrief to USMCAircraft
! USMCAircraft sends requestForTAC to JTAC
! JTAC sends TACCommand to USMCAircraft
! USMCAircraft sends deconflictRequest to
UAV! USMCAircraft gets targetLocation from UAV!!
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AWACS Opportunistic Testing in JCAS
CAS Model with AWACS observation
Test Control
Initially empty Test Suite
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AWACS Opportunistic Testing in JCAS (contd)
Test Control observes CAS request message to
AWACS
Test Control adds appropriate Test Detector and
connects it in to interface,
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AWACS Opportunistic Testing in JCAS (contd)
First stage detector verifies request message
receipt and prepares to start up second stage
Test Control passes on start signal and request
message
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AWACS Opportunistic Testing in JCAS (contd)
First stage detector removes self from test suite
second stage waits for expected response from
AWACS to request
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AWACS Opportunistic Testing in JCAS (contd)
Second stage observes correct AWACS response and
removes itself and starts up second part
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AWACS Opportunistic Testing in JCAS (contd)
At some later time, second part of Test Detector
observes situation brief request message to AWACS
First stage removes itself and starts up second
stage
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AWACS Opportunistic Testing in JCAS (contd)
Second stage observes situation brief output from
AWACS thus passing test, It removes itself and
enclosing Test Detector
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Structure Change Agent Architectures

• Structure change
• Agents can add or remove other agents
• Agents add or remove coupling between pairs of
  agents
• Scope of effect
• anywhere in the hierarchical structure
• within the children of parent or any ancestor
• within their peer group
• Scope of control
• any agent can induce structure change
• only specialized agents can induce structure
  change
• Implementation issues
• within same processor
• in distributed simulation
• in real time

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Global Structural Change Examples
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Summary

• Structure control is the ability of agents to
  induce structural change in themselves or others
  with the effects of enabling different behaviors
  under different circumstances.
• It has been an under-considered aspect of
  intelligent/adaptive properties and the
  collective behaviors of such agents have yet to
  be explored.
• Structure change is expressable in modeling and
  simulation environments based on Discrete Event
  Systems Specification (DEVS).
• It supports opportunistic testing of complex
  defense collaborative agent systems.
• Implications for modeling local and global
  structure transitions in a variety of
  disciplinary guises were suggested

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• which agent capabilities are included or
  emphasized should depend on questions asked
• environment must be sufficiently rich to
  challenge selected agent capabilities

environment
agent-environment interaction
agent embedded in environment
agent to agent interaction
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domains
goal management capability
intention management capability
domain knowledge
belief management capability
language skills
agent model
decision making abilities
communication capabilities
perception abilities
manipulation skills
mobility skills
navigation skills
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interactions
goal management capability
intention management capability
domain knowledge
belief management capability
domains
language skills
decision making abilities
communication capabilities
perception abilities
manipulation skills
mobility skills
navigation skills
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down selection
SIAP agent
SACHEM agent
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integration/organization
belief management capability
intention management capability
domain knowledge
domains
goal management capability
decision making abilities
perception abilities
language skills
mobility skills
navigation skills
manipulation skills
communication capabilities
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Accounting for crashes

• vehicle/car-following conditions for crashes
• weather conditions
• driver perception/mental state

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On foot evacuations

• information needed
• daytime location of poplulation
• children in school
• pets
• stay or leave