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Structure Control in Agentbased Simulation

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Title: Structure Control in Agentbased Simulation


1
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

2
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

3
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,

4
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

5
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

6

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
7
Layered Architecture
8
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

9
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

10
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
11
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
12
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

13
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
14
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
15
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
16
Discrete Event Nature of Link-16 Specification
System Theory Provides Levels of
Structure/Behavior
17
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

18
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
19
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


20
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
21
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
22
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


23
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

24
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")
25
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!!
26
AWACS Opportunistic Testing in JCAS
CAS Model with AWACS observation
Test Control
Initially empty Test Suite
27
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,
28
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
29
AWACS Opportunistic Testing in JCAS (contd)
First stage detector removes self from test suite
second stage waits for expected response from
AWACS to request
30
AWACS Opportunistic Testing in JCAS (contd)
Second stage observes correct AWACS response and
removes itself and starts up second part
31
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
32
AWACS Opportunistic Testing in JCAS (contd)
Second stage observes situation brief output from
AWACS thus passing test, It removes itself and
enclosing Test Detector
33
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

34
Global Structural Change Examples
35
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

36
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37
  • 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
38

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
39
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
40
down selection
SIAP agent
SACHEM agent
41
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42
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
43
Accounting for crashes
  • vehicle/car-following conditions for crashes
  • weather conditions
  • driver perception/mental state

44
On foot evacuations
  • information needed
  • daytime location of poplulation
  • children in school
  • pets
  • stay or leave
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