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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
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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”
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Evolution of System Connectivity
Disjoint
Ad hoc
Encapsulated
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Cooperating Systems with Single
Agent as Global Planner
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Agent-Enabled System
Architecture
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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
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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)
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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)
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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
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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
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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
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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
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KAoS Extension and Generic Agent
Generic Agent
Agent Extension
Conversation
Support
Transport-Level
CommunicationSecurity
Optional
Planner
Various
Capabilities
Shared byAll Agents
Specific to
ParticularAgents
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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
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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
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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
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KA S C ti P li i
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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
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“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
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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
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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
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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
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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
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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”
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C i li l
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Conversation Policy Example:
Winograd and Flores CFA
Combining Finite State Based
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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
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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)
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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
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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)
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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
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Agent “Scram” Capabilities for
Anytime Mobility
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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
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A t R l i T h i l I f ti
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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
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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
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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
Industry DataSources
Industry DataSources
DomainService
Stations
DomainServiceStations
DomanServiceStations
DomainServiceStations
Authenticate Once
Extranet Security
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Permission-Based Access
Encryptable Communication
Extranet Security
Agent Based Framework for
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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