soft ware agents

8/12/2019 Soft Ware Agents

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Intelligent Agent TechnologyJeffrey M. Bradshaw

Bob Carpenter

Rob Cranfill

Mark Greaves

Heather Holmback

Renia Jeffers

Luis Poblete

Amy Sun

Applied Research and Technology

Shared Services Group

The Boeing Company

[email protected]



Why Software Agents? Original agent work instigated by researchers studying distributed

intelligence

New wave of agent research motivated by two practical concerns:

– Overcoming the limitations of current user interface approaches

– Simplifying the complexities of distributed computing

Though each of these problems can be solved in other ways, the

aggregate advantage of agent technology is that it can address both ofthem at once:

– by supplementing direct manipulation with indirect management

approaches

– by building in high-level, loosely-coupled collaborative

capabilities “out of the box”



Evolution of System Connectivity

Disjoint

Ad hoc

Encapsulated



Cooperating Systems with Single

Agent as Global Planner



Agent-Enabled System

Architecture



What is a Software Agent?

Agents are software entities that function continuously and

autonomously in a particular environment that is often inhabited by

other agents and processes

Ideally a software agent should be able to: – carry out activities without requiring constant human guidance

– learn from its experience

– communicate and collaborate with people and other agents

–

move from place to place over a network as necessary

Not all software agents need be “intelligent” (agents vs. minions)

There is no hard dividing line between object technology and multi-

agent technology



Basic Agent Characteristics

Agents adapt to their

environment.• Dynamic Interaction

• Alternate Methods

• Machine Learning

Agents cooperate to

achieve common goals.• Communication Protocols

• Knowledge-Sharing

• Coordination Strategies

• Negotiation Protocols

Agents act autonomously to

accomplish objectives. • Goal-Directed

• Knowledgeable

• Persistent

• Proactive & Reactive

Note: Agents can be either static or mobile, depending on

bandwidth requirements, data vs. program size,

communication latency, and network stability

Autonomous

Adaptive Cooperative

(Dyer, DARPA CoABS)



Agents and Objects

Objects Agents

instance agent

unconstrained knowledge, desires,

intentions, capabilities,…

operations messages

defined in classes defined in suites

implicit defined in conversations

none honesty, consistency,…

Basic unit

State-defining parameters

Process of computation

Message types

Message sequences

Social conventions

(Adapted from Shoham)



Applications of Software Agents

Office automation/engineering support – mail filtering

– meeting scheduling

– intelligent assistance

– training and performance support

Information access – retrieval, filtering, and integration from multiple sources

– Internet, intranet, extranet

Resource brokering – “fair” allocation of limited computing resources

– dynamic rerouting and reassignment of tasks

Active document interfaces – intelligent integration and presentation to suit the task

– dynamic configuration according to resource availability and platform constraints

Intelligent collaboration – between systems

– among people

– mixture of people and agent-assisted systems



Boeing IAT Program Objectives

u More powerful agent frameworks

– New KAoS release

– UtterKAoS: Conversations, Security, Persistence, Mobility,

Middle Agents, Planning

– Incorporation of COTS components (e.g., Voyager, Java platformenhancements)

u Easier creation of sophisticated agents

– ADT, comprised initially of CDT, SDT, PDT

u Deploy in spectrum of application areas – Current areas: Information Access, DIG-IT, NASA Aviation

Extranet, DARPA JumpStart

– New opportunities: Spacecraft autonomy, hybrid networking QoS,

security, UCAV, engineering, manufacturing



Some Long-Term Requirements for

Industrial-Strength Agents Architecture appropriate for a wide variety of domains and

operating environments

Hardware-, operating-system-, programming-language-

independent

Separability of message and transport layers

Foundation of distributed-object/middleware

(e.g.,CORBA, DCOM) and Internet technologies

Fits well into component integration architectures (e.g.,

ActiveX, JavaBeans, Web browsers)

Principled extensibility of agent-to-agent protocol

Designed to work with other agent architectures, and to

allow easy “agentification” of existing software

Must be able to incorporate agent interoperabilitystandards as they evolve



T

KAoS Implementation Context

Adaptive Virtual

Document

Web and

other

Internet

services

Link

Servers

Fine-graineddata objects

Component

tools and

services

Object Request

Broker

SGML/XML

Component

Database

Component

Multimedia

Component

Component integration framework

Agents

CORBA

Local and remote databases and services



KAoS Extension and Generic Agent

Generic Agent

Agent Extension

Conversation

Support

Transport-Level

CommunicationSecurity

Optional

Planner

Various

Capabilities

Shared byAll Agents

Specific to

ParticularAgents



Agent-to-Agent Communication Within an

Agent Domain

Generic

Agent

Instance

Generic

Agent

Instance

Agent A

Agent B

Agent-to-

Agent

Protocol

Agent Domain

D i M d M h k



Domain Manager and MatchmakerThe Domain Manager:

Controls entry/exit of agents within a domain, governs proxy agents (i.e., security)

Maintains a set of properties on behalf of the domain administrator Provides the address of the Matchmaker to agents within its domain (i.e., naming)

The Matchmaker:

Helps clients find information about the location of agents that have advertised their

services

Forwards requests to Matchmakers in other domains as appropriate

Can be built on top of native distributed object system services (e.g., trader)

Agents Providing Services:

Advertise their services to the Matchmaker

Are notified by the Matchmaker if their services have been registered Withdraw their services when they no longer wish to provide them

Agents Requesting Services

Ask the Matchmaker to recommend agents that match certain criteria

Are given unique identifiers for the agents that match the criteria

Communicate directly with these agents for services

A t f KA S D i



Generic Agent

GA

GAGA

GA

GA

Mediation

Extension

Proxy

Extension

Adapter

Matchmaker

Extension

Domain

Mgr. Extension

Telesthetic

Extension

Ext. from

Foreign

Domain

KAoS Agent

Domain

External

Resource

Proxy to

Another

KAoS

Domain

GA

GA =

Anatomy of a KAoS Domain



KA S C ti P li i



KAoS Conversation Policiesu Interaction among agents best modeled at the conversational

level, rather than isolated speech acts

u Conversation policies are agent dialogue building-blocks that

provide a set of constraints that define and restrict what can take

place in individual agent conversations

– Policies can be expressed via many different representation formalisms,

from regular expression grammars to dynamic logicsu Conversation policies ensure reliable communication among

heterogeneous agents while lessening agent’s burden of

inference

– Agents choose between a greatly reduced number of possibleconversational moves

– Conversation manager (component of “generic agent”) assures

compliance with policy; handles exceptions

u References: http://www.coginst.uwf.edu/~jbradsha/

“C i f A i ” P li



“Conversation for Action” Policy

• Communication about commitments (promise, renege) is handled explicitly, and A

can notify B when the request was not fulfilled to its satisfaction (decline report)

• See formal analysis of Conversation for Action Policy in Smith and Cohen 1996

AAAI paper



JumpStart Project Overview

u Selected under the DARPA CoABS Program

– Approximately 20 other participants

u Partners: Boeing , Sun, UWF, IntelliTek

u Collaborator: Oregon Graduate Institute (CHCC)u Deliverables:

– Prototype software (CDT and SDT)

– Periodic technical reports and demos

– Interoperability demos with other CoABS participants

DARPA’ Vi i f th F t f



DARPA’s Vision of the Future of

Agents

u The Future of Agent Ensembles

– Agents authored by different vendors at different times

– Wide variety of agent reasoning and action capabilities

– Complex operational environment:

• Unpredictable universe of action

• Dynamic task-specific agent teams

• Collaborative, negotiated problem-solving behavior

u The Future of Agent Developers

– More agents written by domain experts; fewer agents written byagent-technology experts

– Decreased ability to control agent contexts of use



u Simple agent systems may require only simple models of

communication to achieve their ends

– Limited tasks, collaborations, interactions with one another

– Predictable all simple-agent universe of action – Limited and domain-specific reasoning requirements

– Conversations are atomic transactions

u Example:

– Simple personal information retrieval agents• interact mainly with non-agent information sources

• little negotiation or bargaining

Simple Agents May Not Need a

Complex Theory



u But, consider more complex applications, involving:

– Higher reliability, verifiability, precision of expression

– Arbitrary, dynamic agent collaboration with negotiation

– Unpredictable universe of action

– Complex autonomous reasoning about other agents, plans

– Extensive human-agent interaction

u Examples:

– Electronic Commerce/Electronic Trading, Air Traffic Control,

Health Care, Military, etc.

u This requires a sophisticated multiagent communication

model, e.g., conversations, with an explicit semantic

foundation.

Sophisticated Agents Require

Sophisticated Theory

O ti i H t



Operating in Heterogeneous

Environments

Mixture of different agent frameworks

Mixture of simple and sophisticated agents

Approach: shared conversation and security policies, generated

off-line, that increase interoperability and robustness in

heterogeneous agent environments

“What We’ve Got Here is a Failure To Communicate”



C i li l



Conversation Policy Example:

Winograd and Flores CFA

Combining Finite State Based



Combining Finite-State-Basedand Plan-Based Conversation

Policy Approachesu Intelligent agents can use less constraining plan-based

policies that give them flexibility of determining many

specifics of conversational moves on-the-fly

u Constraints governing plan-based conversation policiesmake them less complicated to implement than unrestricted

agent dialogue models

u Simpler agents will continue to rely on more rigidly

defined FSM-based policies where the universe of possible

moves has been pre-computed “off -line”

u FSM and plan-based versions of same policy must comply

to same semantics and pragmatics

u Appropriate “version” can be negotiated between agents at

runtime

E t di S ti /P ti



Extending Semantics/Pragmaticsu Participate in ongoing ACL development

– KAoS, AgentTalk, FIPA, KQML-Lite, KQML-Rite

– Ultimate goal of consensus on a compositional semantics with principled extensibility

u Analyze the ACL speech acts & conversation policies

– We will study/develop basic conversation properties (e.g., the

ordering, timing, sequences of communication acts) – Match representations of conversation policies to diverse levels of

agent capability:

• Finite-state-machine models

• Landmark models

• Emergent conversations – FSM and landmark models of same policy must comply to same

semantics and pragmatics; choice of model negotiated at runtime

between agents

– We will also investigate other pragmatic conditions imposed by

context (e.g., meta-conditions on agent conversations)



Java Security and Mobilityu Java is currently the most popular and arguably the most

security-conscious mainstream language for agentdevelopment

u Its cross-platform nature makes it well-suited for

heterogeneous environments

u However Java 1.0-1.1 failed to address many of thechallenges posed by agent software

– All or nothing philosophy in “sandbox”

– Lack of fine-grained resource control

– Security policy implementation requires writing your own securitymanager

– Applet mechanisms are insufficient for autonomous agent mobility



New Developments in Java

Security and Mobilityu Mechanisms for increasing configurability, extensibility,

and fine-grained access control are under development at

Sun Microsystems

u Java 1.2 enhancements

– Applets and applications on equivalent security footings

– Finer-grained configurability and better resource control

– Specification of much of the security policy via an external policy

file, thus separating policy from mechanism

u These new developments provide an initial foundation forsupport of agent-unique requirements

Sec rit Design Tool (SDT)



Security Design Tool (SDT)u Accelerate incorporation of required agent security and mobility

features into the Java platform

– Foundation of new Java security model + changes to Java VM

– Work with vendors, developers, standards organizations

u Issues for Java platform enhancement and SDT development

– Agent authentication and PKI management

– Secure communication – Enhanced configurability and resource management

• Denial of service issues: CPU, disk, memory, display

• Load balancing and grid “resource dial”

– Support for secure agent mobility

u SDT Benefits

– Configurable “starter set” of agent security policies

– Interoperability among different agent frameworks (grid “security dial”?)

– Faster creation of robust agents by non-experts



Agent “Scram” Capabilities for

Anytime Mobility



Anytime Mobilityu Telescript provided completely transparent agent mobility

u Current Java-based agent systems do not

– Agent system code runs inside the VM; no access to execution state

u Advantages of transparent agent mobility

– Agent code need not be structured with many entry points

– Allows the agent system (as well as the agents themselves) to move

agents between hosts

– May be transparent to the agent (may require additional redirection

of agent resources)

– Supports load balancing of long running agents in the gridu Requires modifications to the Java VM



A t R l i T h i l I f ti



Agent Roles in Technical Information u Agent-Assisted Document

Construction

At the user-interface, agents work inconjunction with compound

document and web browser

frameworks and document

management tools to select the right

data, assemble the needed

components, and present theinformation in the most appropriate

way for a specific user and situation.

u Agent-Assisted Software

Integration

Behind the scenes, agents take

advantage of distributed object

management, database, workflow,

messaging, transaction, web, and

networking capabilities to discover,

link, manage, and securely access theappropriate data and services.

A

A

A

AA



Aviation Extranet Goals

“ By the turn of the century, airlines will be able to dynamically reconfigure their flight operations

for improved safety and more efficient transportation for the traveling public”

Develop middleware components to integrate and extend the

capabilities of aviation legacy systems on a secure extranet to support: – Real-time aircraft and airport situational awareness and scheduling and planning functions

– Maintenance and operations procedures enhancements

– Feedback data mechanisms to design/manufacturing models and simulators

Develop Extranet Global Information Services – Intelligent agents

– Metadatabases and Data Warehouses

Conduct advanced research in decision support tools for the Aviation

Community



Aviation Extranet Middleware Architecture

Design/Manufacturing

Meta-Dbases

Regulations/DocumentationMeta-Dbases

Real-Time OpsMeta-Dbases

Maintenance/AncillaryMeta-Dbases

Web Browser

Intelligent Web Servers

CORBA Interfaces Int el ligent Agents

Industry DataSources

Industry

Data Sources



DomainService

Stations

DomainServiceStations

DomanServiceStations

DomainServiceStations

Authenticate Once

Extranet Security



Permission-Based Access

Encryptable Communication

Extranet Security

Agent Based Framework for



Information

Service

Agent

Metadata/

Ontology

Agent

InformationBroker

Agent

User

Agent

Information

Service

Agent

User

Agent

InformationBroker

Agent

Metadata/

Ontology

AgentInformation

Service

Agent

Agent-Based Framework forInformation Access

Matchmaker

Agent MatchmakerAgent

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