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Adaptive Environments: Essential for Scalable, Survivable, and Secure Multi-Agent Systems. March 21, 2007 Dr. John Zinky jzinky@bbn.com Workshop on Large Scale Multi-Agent Architectures. Outline. Target Applications Cougaar Agent Middleware Adaptive Environments Open Research Topics.

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adaptive environments essential for scalable survivable and secure multi agent systems

Adaptive Environments: Essential forScalable, Survivable, and Secure Multi-Agent Systems

March 21, 2007

Dr.John Zinky

jzinky@bbn.com

Workshop on Large Scale Multi-Agent Architectures

slide2

Outline

Target Applications

Cougaar Agent Middleware

Adaptive Environments

Open Research Topics

extreme applications
Extreme Applications

Realtime distributed P2P applications with

severe resource constraints and with

Scalability, Survivability, Security (S3) requirements

  • Examples of Extreme Applications
  • Information Assurance
  • Surveillance on UAV mobile sensor platforms
  • Proactive content distribution
  • Global network management and optimization
  • Mission Management

Management

Plane

Data Processing Plane

properties of extreme applications

Application

Business

System

Properties of Extreme Applications
  • System Resource Constraints
  • (exhibited during runtime)
  • Distributedness of cyber resources:

centralized vs. LANvs. WAN

  • Data plane speed:

batch vs. online vs. realtime (superhuman)

  • Survivability (reliability & performance):

non-crucial vs. exigent

  • Security adversary level:

trust all vs. compartmentalized trust vs. malicious vs. insider threat

  • Scalability (hosts):

10s vs. 100s vs. 1000s vs. >10,000

  • Application Functional Requirements
  • (addressed at programming/development phase)
  • Communication:

client/server vs. P2P

  • Development cycle:

waterfall vs. adaptive

  • Process during operational lifespan:

fixed vs. evolving

  • Human participation level:

none vs. sensor vs. model vs. cognitive

  • Cross-cyber resource load

CPU vs. network vs. storage vs. all

  • Business Environment
  • (Organizational constraints)
  • Market share:

large vs. medium vs. small

  • Integration environment:

standalone vs. stovepipe vs.

new functionality w/ legacy system integration

abstract architecture for extreme applications

management plane

data

plane

Cognitive

Control Loop

Model-Based

Control Loop

Sensor-Based

Control Loop

Sensor

Proxy

Agents

processing status

coordination

processing. trends

coordination

processing pattern

coordination

Processing

Units

Processing

Units

Situation

Predictor

Agents

Cognitive

Learner

Agents

Real-time

Optimizer

Agents

Processing

Units

Model updates

situation

policy

inference rules

network

Disk

CPU

resource pattern

coordination

resource trends

coordination

resource status

coordination

Sensor

Proxy

Agents

days to hours

hours to minutes

secs to msecs

Abstract Architecture for Extreme Applications

Application

Environment

slide6

Outline

Target Applications

Cougaar Agent Middleware

Adaptive Environments

Open Research Topics

cougaar agent reference model
Cougaar Agent Reference Model

Application

domain

specific

Behavior

Behavior

BB

BB

Agent

Agent

Elastic

Boundary

coordinator

coordinator

sensor

sensor

effector

effector

Infrastructure

Cyber Resource

Physical

service

service

service

service

service

System

specific

component

component

component

component

component

library

library

Abstracted Environment

  • Environment
  • Distributed
  • Services
  • Components
  • Imported libraries
  • Service oriented API
  • Agent/Env. API
  • sensor
  • effector
  • coordinator
  • active API

Agents

  • Local
  • Behavior (plugin)
  • State (BB)
  • Pub/Sub Black

Board (BB) API

separation of application from environment

Behavior

Behavior

Agent

Coordination

Agent

BB

BB

MTS

Node Process

Node Process

IP

Host

Host

Separation of Application from Environment

Agents handle Application Behavior

Environment handles Systemic Adaptation

Agents and Environment can be independently developed, tested, and configured, but run together

Network

integration with legacy systems

enterprise

service bus

OWL

knowledge

base

JESS

C o u g a a r

runtime

C o u g a a r

runtime

runtime

C o u g a a r

SQL DB

services

MPI Library

Corba/RMI web services

processes/threads

TCP/UDP

network stack

files

wire/fiber/

radio

wire/fiber/

radio

wire/fiber/

radio

disk

disk

disk

CPU

CPU

CPU

Integration with Legacy Systems

word processing

embedded control

scheduling

semantic tagging

Applicationm

Applicationj

banking/airlines

ftp, telnet, ssh

web Services

Applicationn

Applicationk

embedded devices

scientific comp.

grid-based systems

message-based systems

DB-based systems

Main Cougaar architectural feature:

imported libraries and component wrappers

architectural mapping

Sensor

Proxy

Agents

Situation

Predictor

Agents

Cognitive

Learner

Agents

Real-time

Optimizer

Agents

Sensor

Proxy

Agents

Architectural Mapping

management plane

data plane

Cognitive

Control Loop

Model-Based

Control Loop

Sensor-Based

Control Loop

processing status

coordination

processing. trends

coordination

processing pattern

coordination

Model

situation

policy

Processing

Units

inference rules

network

Disk

CPU

resource pattern

coordination

resource trends

coordination

resource status

coordination

days to minutes

secs to msecs

minutes to sec

  • Cougaar
  • Agent societies
  • Cougaar environment
  • Agent coordinations
  • Transitioning of control

loops human to automation

  • Application
  • Functional modules (oval shaped)
  • Underlying distributed environment
  • Sensor to control loop coordination
  • Evolving degree of human involvement

architectural mapping

slide11

Outline

Target Applications

Cougaar Agent Middleware

Adaptive Environments

Open Research Topics

adaptation
Adaptation
  • Adaptation picks the best implementation which meets the application QoS requirements within the resource constraints
  • To make this tradeoff: adaptive systems must have:
    • Multiple implementations
    • Characterization of each implementation based requirement and constraint conditions
    • A mechanism for detecting the system’s conditions
    • A policy for choosing which implementation given the conditions.
    • A mechanism for enabling the implementation

Application Loads

Quality of Service

Algorithm

Implementation

Resource Capacities

Utilization/Cost

static design vs adaptation
Static Design vs. Adaptation

Water Fall Design Process

  • Static Design strives for a simple, elegant, efficient solution to a single situation.

Outside of that situation the design is useless

  • Adaptation strives to just survive in a constantly changing situation.

Adaptation is continuously making design decisions

Requirements

Design

Implement

Test

Adaptive Control Loop

Policy

Adaptive

Control

Conditions

Conditions

QoS

Impl

Loads

Impl

Implementation

Capacity

Cost

example adaptive environment services
Example Adaptive Environment Services

Cougaar examples of how to make adaptive environment services.

  • Support for Adaptive Life Cycle

allows multiple hooks adding adaptive code

  • Coordination Service

allows agents to interact via the environment

  • Knowledge Representation (KR)

manages inference and change notification of agent’s internal state

  • Programming Model

enables developers to decompose application and systems issues

supporting adaptation in the system life cycle
Supporting Adaptation in the System Life Cycle

Cougaar

Middleware

IDE

Application

Plugins

Spec

Tool

Deploy

Rules

Society

Monitor

Run

Server

aspects cross cutting functionality

Work-flow between stations

Component

Component

Component

Component

Aspect Delegates

Aspect Object

Aspect Object

QoS State

QoS State

QoS Services

QoS Services

Aspects Cross-Cutting Functionality
example status dissemination overlay network
Example: Status Dissemination Overlay Network

C

  • Probe Agents
    • Collects real time data
  • Client Proxy Agents
    • Access control, security enforcement, flow-control
  • Dissemination Agents (forms a mesh)
    • Floods Status Records toward subscribers
  • Baseline Agents
    • Holds default ontology and system configuration
  • Management Agents
    • Mesh topology creation
    • Society monitoring and control
    • Agent restart and move

D

D

P

P

D

D

D

P

C

D

D

D

P

C

B

B

  • v12.2 Coordinations
    • Task/Allocation
    • Relays
future support for coordination artifacts
Future: Support for Coordination Artifacts

Defines roles

  • Coordination Artifacts: CAs
    • Are first-class entities in MAS
    • Define explicit roles for role-players
    • Offer shared state between the role-player & the CA
    • Coordinate behavior among role-players
    • Have distributed implementation

Coordination

Artifact

(CA)

Agent

Agent

Role-players

Shared state

Agent

Agent

qos adaptive translation changes the translation mechanics to match the situation
QoS-Adaptive Translation Changes the Translation Mechanics to Match the Situation

QoS-Adaptive Translation

  • Translation should take into account
    • Structure of starting and ending data structures
    • Probability and frequency that structures will change
    • The constraints of the transfer path

Deltas

Change

Reconstruction

Change

Detection

Transfer Constraints

Host

Object

capacity

Process

Latency

Object

Class

Method

Method

Method

Method

Load

Latency= Load / Capacity

frameset knowledge representation
FrameSet Knowledge Representation

Host

Equip

  • Java Objects are code generated
    • Frames and relationships defined using XML
    • Support multiple Java interfaces
      • Cougaar Blackboard,
      • JESS Shadow Facts,
      • Java Beans
      • Web Server
  • Slot inference (Real-time)
    • Type (is-a)
    • Containment (has-a)
    • Visitor Pattern (composed-of)
    • Aggregation (summary-of)
  • Relationships are also Frames
    • Benefits from Frame inheritance
  • Meta-data tags
    • Defined at compile-time
      • Slots, frames, framesets
    • Example Slot meta-data
      • Type, default-value, units, path, doc, member, warn, immutable, notify-blackboard, notify-listeners, transient

Process

Appl

Thing

Object

Class

Type inheritance

Containment inheritance

Frame

name value

name value

name value

Relationship

parent-name value

child-name value

future owl rdf graph support on bb
Future: OWL-RDF Graph Support on BB
  • Nodes are defined by URIs
  • Links are defined by OWL Statements.
    • (Subject, Predicate Object)
  • OWL statements are merged from multiples sources
    • Redundant probes
    • Different time horizons
    • Status Calculus/policies define the merge procedure.
  • Modifying an OWL Statement can:
    • Add an object instance
    • Change an attribute’s value
    • Assert general relationships between entities
  • Queries return a “subgraph”, i.e. linked set of OWL statements

OWL Statement

(“http://bbn.com/CommGear/STU-III-Phone#703-555-1212”,

“serialNumber”,

” 43123154562”)

OWL Statement

(“WI”, “IsA”,”State”)

characteristics of programming models
Characteristics of Programming Models

The programming model for interaction between components, should allow a range of flexibility vs efficiency tradeoffs

future multiple knowledge processing frameworks
Future: Multiple Knowledge Processing Frameworks

Agent Domain Processing

Rule code

Procedural code

LHS

Patterns

RHS

Trigger

Assert

Retract

Domain Processing

Domain Routines

Domain Objects

Code Libraries

Agents

Concentrate

on domain

processing

Facts from

multiple

Partitions

Blackboard Partitions

managed by

Coordination Artifacts

Partitioned Blackboard

Coordination with

External Systems

Coordination with

Physical Environment

Coordination with

Peer Agents

Real-Time Knowledge Feeds

slide24

Outline

Target Applications

Cougaar Agent Middleware

Adaptive Environments

Open Research Topics

open systems research problems
Open Systems Research Problems
  • Adaptive Knowledge Sharing
    • How to automatically and efficiently translate knowledge betweens heterogeneous agents?
    • How can we merge domain ontologies and system constraints ontologies?
  • Coordination
    • How do we make coordination first class?
    • How to formally specify coordination, in order to reason about at runtime?
  • High-level agent Programming Abstraction
    • How to give agents richer and domain-customized programming support?
  • Societies Composition
    • How to merge multiple societies to perform a higher level task?
    • How to partition societies into federations to reduce complexity?
  • Reuse
    • How to define and create libraries of reusable coordinations?
    • What common set of services to standardize in order to simplify agent implementation?
    • Which reusable generic set of agents to offer for specific services?
open source cougaar
Open Source Cougaar
  • Release 12.2 in March 12, 2007
  • ~2000 downloads 12.0 release
  • 30 downloads rel 12.2 (1 week)
  • 46 active hosted projects

(~10 BBN)

  • ~1400 active users

www.cougaar.org