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Enabling Technologies for Knowledge-Based E-Business
Presented by: Thomas L. Adams, PhDMathematics & Computing Technology
Phantom WorksThe Boeing Company
May 21, 2001
Briefing Outline Trends in Cyberspace and Electronic Business Technical Advances Impacting E-Business Knowledge Representation in E-Business (Ontologies)
What is an Ontology? Why Do We Need Ontologies? Technical Issues in Using Ontologies Ontologies for the Web (Semantic Web) Ontology Standards Ontology Projects at Boeing
Multi-Agent Systems in E-Business (Autonomy, Interactivity) Overview of the Multi-Agent Systems Paradigm The Role of Agents in Electronic Business Agent Standards Agent Projects at Boeing
Evolution of Cyberspace
HTML Revolution
XML Revolution
SemanticWeb
Shared Information Space
Culture of Open Exchange
Information and Process Integration
Document Retrieval Dynamic Content Management Heterogeneous Information Sources
Human Information Processing
Business Service Registries
Integrated Knowledge Management and Business Problem Solving
Ad Hoc Query Answering
Relationships in the Digital Economy
Customers
Suppliers
StrategicPartners
Assemblers
ServiceProviders
Distributors
Enterprise
Adapted from: Ontologies and Taxonomies: A Managers View, Joseph Williamson
Trends in Electronic Business
Ever Changing Relationships With Business Partners and Suppliers Integration of Disparate Information Into a Meaningful Whole Alliances and Shared Technology Investments Virtual Enterprises
Ever Changing Market Adapt to Changes in Available Resources Adapt to Changes in Consumer Requirements
Incompleteness and Ambiguity of Real Business Processes Intelligent Process Automation Cross-Cultural Interoperability
New Kinds of Services and Interactions Between Suppliers and Vendors
Dynamic Generation of Supply Chain Links Automatic Negotiations Coalition Forming Online Configuration of Products
Technical Advances Impacting Electronic Commerce
High Performance Knowledge Bases Intelligent Nodes Local Inferencing and Knowledge Processing
Knowledge Representation Highly Expressive Ontologies Declarative Semantics
Multi-Agent Systems Distributed Problem-Solving Business Process Integration
Semantic Web XML, XML Schema RDF, RDF Schema Topic Maps
Ontologies and Agents for Web-Based Electronic Commerce
Ontologies Provide Knowledge Schema Enable machine-understandable/processable semantics of data Empowers completely new kinds of automated services
Software Agents Provide Problem-Solving Methods (Specialized Reasoning Services) Search for Products Form Buyer and Seller Coalitions Negotiate about Products Automatically Configure Products and Services According to
Specified User Requirements
Kinds of Ontologies
Terms
General Logic
Thesauri
formalTaxonomies
Frames(OKBC)
Data Models(UML, STEP)
Description Logics(OIL)
Principled, informal
hierarchies
ad hoc Hierarchies (Yahoo!)
structured Glossaries
XML DTDs
Data Dictionaries
(EDI)
ordinaryGlossaries
XML Schema
DB Schema
Multi-dimensional continuum: Increasing formality, amount of meaning specified, support for reasoning Decreasing potential for amgibuity
Evaluation Framework for Comparing Ontologies
KnowledgeRepresentation
Classes Metaclasses Slots/Attributes Facets
Taxonomies
Procedures
Relations/Functions Instances / Individuals / Facts Axioms
Production Rules
InferenceMechanisms
ExceptionsAutomatic classifications
InheritanceM onotonic, Non monotonicSimple, Multiple
Execution of Procedures
Constraint Checking
Reasoning with rules Backward chaining Forward Chaining
Evaluation framework
Source: Evaluating Knowledge Representation and Reasoning Capabilities of Ontology Specification Languages,Oscar Corho and Asuncion Gomez-Perez
Why Do We Need Ontologies? Shared Domain Conceptualization
Objects in the Domain Behaviors and Plans
Basis for Planning Activities Representation of Goals and Plans Characterization of Resources and Services
Basis for Information Sharing Modes of Interaction (Query, Publish, etc) Message Content
Basis for Business Interactions Protocols for Joint Activities Negotiation Strategies
Technical Issues in Using Ontologies
Expressivity versus Computational Complexity Rich Vocabulary of Epistemological Primitives => Higher Levels of
Interoperability Concept Hierarchies Sufficient for Concept-Based Information Search Business Rules and Axioms Required for Process Integration
Translation Among Independently Developed Ontologies Merging of Ontologies Partial Understanding
Translation Between Human Mental Models and Formal Ontologies Lack of Precision of Natural Language Support for Personalized Views of Concepts and Information
Challenges for Users in Using Available Ontological Information
Standards are still forming Most vocabulary information needs to be
augmented Extend UN/SPSC to have more depth in certain areas
Complete tool suites are still in development Ontology Building Maintenance Validation and Verification Merging Evolution
Semantic Web Activity at the W3C
Source: http://www.w3c.org
Languages of the Semantic Web
Resource Description Framework (RDF) Topic Maps (XTM) Ontology Interface Layer (OIL) DARPA Agent Markup Language (DAML)
Ontology Languages
Enriched First-Order Predicate Calculus CyC KIF
Frame-Based Languages Ontolingua Frame Logic
Description Logics Classic OIL
What is Cyc? Highly Expressive Knowledge Representation Language (CycL) High Performance Inference Engine
Multiple Inference Strategies (about 30 currently) Specification at Epistemological Level (EL) Execution at the Heuristic Level (HL) based on syntactic
categories Supports Default Reasoning (Arguments for and Against) Knowledge is partitioned into microtheories
Very Large Repository of Common Sense Knowledge Developers Toolkit
Ask, Assert, Create, Explanation Concept Descripitons/Hierarchies Java API (interaction with non-Cyc system elements KE Text (batch processing of knowledge; testing)
Sources of Ontology Standards Standardizing Knowledge Representation/Agents on the Web
World Wide Web Consortium Resource Description Framework (RDF) Semantic Web Initiative
DARPA Agent Mark-Up Language (DAML) Program DAML-ONT DAML-Logic
Draft ANSI standard Knowledge Interchange Format (KIF) Mapping terms between domain specific models
IEEE Standard Upper Ontology (SUP) Defining Ontology Related Services
FIPA Ontology Special Interest Group Open Knowledge Base Connectivity (OKBC)
European-Funded Semantic Web Projects On-To-Knowledge (Access to Static Information Sources) IBROW (Dynamic Reasoning Services)
Types of Ontology Standards
Large Horizontal Cover Cover all possible product areas Examples
Universal Standard Products and Services code (UN/SPC) Defines a concept hierarchy to classify all products
UCEC Defines attributes to describe products
Large Vertical Cover Focus on a certain domain Provide much more detailed descriptions Examples
RosettaNet describes the products of the hardware and software industries in
detail
Ontology Research at Boeing Boeing Thesaurus Noun-Compound Analysis System (NCAS) Concept
Analyzer Information Integration and Access
Semantic Integration of Heterogeneous Information Sources
Collaborative Information Retrieval with U of Wash, Microsoft, Center for Human-Machine Interaction
(Denmark)
Extended Enterprise Consortium for Integrated Collaborative Manufacturing Systems Supply Chain Project
Business Rules for E-Commerce IBM-Led Consortium Boeing as Customer
The Boeing Thesaurus/Ontology
From Boeing Technical Libraries Used for classifying library documents Rich in aerospace & Boeing-specific concepts Massive knowledge repository!
35,061 concepts 17,832 synonyms 157,407 relationships (3 types)
Many person-years investment of effort
A (tiny) fragment of the ontology...
Jetengines
flameout
combustion
Burningrate
afterburning
Ramjetengines
Hydrogenfuels
enginesPropulsion
systems
thrustliftTurbojetengines
Enginestarters
Flamestability
Combustionstability
Flamepropagation
Pneumaticequipment starting
ignition
sprayJet spray
Uses of NCAS
Allow users to construct rather than selectconcepts Handle concepts not in the original Thesaurus
Processing of noun phrases in text documents automatic concept-based indexing of text resources
Space Station Spacelocated inWith NCAS:
Space Station Spacerelated toCurrent:
Process the Thesaurus itself, refining related to links
- creation of a knowledge-base from the Thesaurus
Information Access
InformationService Agent
Information BrokerAgent
UserAgent
InformationService Agent
UserAgent
Information BrokerAgent
InformationService Agent
InformationService Agent
Metadata/Ontology
Agent
Metadata/Ontology
Agent
Web pages Databases Experts Documents ...
tube placement
Business Rules for E-CommerceGoals
High Level of Conceptual Abstraction; easier understanding and specification by non-programmers
Automatic Execution; Matchmaking of Buyers with Sellers
Rule-Based Business Processes for Both B2B and B2C Represent Business Processes (sales help; customer help;
procurement; authorization; brokering; workflow) Represent Buyers Requests, Interests, Bids Represent Sellers Offerings (products and services,
capabilities, bids; map offerings from multiple sellers to common catalog
Source: EECOMS Project Briefing, Benjamin Grosof
NSF Sponsored Collaboration (Boeing, Microsoft, University off Washington, Center for Human-Machine Interaction)
Social Aspects of Information Retrieval in a Variety of Workplace Settings
Collectively Resolve Information Problem Undertaken by a Work Team 2 or more members follow a path together Team members use different paths, in parallel or
sequentially Team members guided by other team members
Collaborative Information Retrieval
Ontologies for eBusiness Conclusions
Knowledge Sharing is a Prerequisite for Business Process Interoperability Common Domain Conceptualization Intelligent Access and Execution of Business Services
Ontologies Describe the Core Concepts and Their Interrelationships
Specialized Knowledge Representations May be Used Internally (e.g., for efficiency)
Ontologies Permit Translation Between Internal and External Knowledge Representations
Ontologies May Be Built Incrementally Start with Taxonomies; Concept-Based Search Add Expressiveness and Knowledge Processing Capability
Multi-Agent Systems Technology in Electronic Business
Overview of the Multi-Agent System Paradigm
The Role of Agents in Electronic Business Standards for Agent-Based Systems Agent Applications and Research at Boeing
Evolving Computing Environment
MAINFRAME
SOFTWAREPROCEDURES DATA
Server
Server
Server
MAINFRAME
SOFTWAREPROCEDURES DATA
C
C
C
C
C
C
SoftwareObject
SoftwareObject
DataObject
DataObject
Server
Server
ServerSoftwareObject
SoftwareObject
DataObject
DataObject
Centralized Architecture- Dumb Terminals - Rigid Mainframe Applications- Hard-Wired Connections
Procedural Software- Single Monolithic Program - Closed System- Unmaintainable Spaghetti Code
PAST PRESENT FUTURE (?)Clients
Client-Server Architecture- User Workstations (Clients)- Application & Data Servers- Programmed Connectivity
Object-Oriented Software- Modular Software Objects- Open Interfaces & Protocols- Adaptable by Programmer
Agent Services- Taskable User Agents- Composable Network Services- Dynamically Brokered Interaction
Agent-Based Software- Autonomous Software Agents- Dynamic Coordination Protocols- Self-Organizing & Adapting
FUTURE (?)Server
Server
DataService
DataService
Server
Server
DataService
DataService
AAA A
A
AAA
A
A
AA
A
A
AA
A
A
A
AA
A
AA
A
A
A
AA
A
AA
(adapted from Gunning, DARPA)
Key Agent Characteristics
Agents adapt to their environment.
Dynamic Interaction Alternate Methods Machine Learning
Agents act autonomously to accomplish objectives.
Goal-Directed Knowledgeable Persistent Proactive & Reactive
AutonomousAutonomous
AdaptiveAdaptive CooperativeCooperative
Agents cooperate to achieve common goals.
Communication Protocols Knowledge-Sharing Coordination Strategies Negotiation Protocols
Mobile:Agents can either be static or mobile, depending onbandwidth requirements, data vs. program size,communication latency, and network stability
Spectrum of Object and Agent PropertiesSocial Agents
Capable of sophisticated coordination Joint intentions, runtime team formation Reasoning about other agents
Intentional Agents Speech-act-based messaging Formulate plans in pursuit of own agenda Reflective reasoning
Learning Agents Persistent state across invocations Malleable, adaptive behavior based on pattern recognitionand reinforcement learning
Reactive Agents/Actors Each agent has its own thread of control Asynchronous messaging, not direct invocation Responds reactively to events and messages
Distributed Objects Invocation across process boundaries Separation between interface and implementation typical
Objects Components that can be specialized incrementally Encapsulation and inheritance Selective reuse of structure and behavior
Components Pluggable entities with self-contained data and behavior Encapsulation but no inheritance Reuse of structure and behavior
Note: The upper layers are not
necessarilyordered
hierarchically as shown
Cooperativity: Alternative ApproachesKnowledge-Sharing
Agents share knowledge about capabilities and requests.
Agent matchmakers and brokersdynamically match requests to capabilities.
System dynamically adjusts as capabilities are added to and removed from the environment.
Team Coordination Agents share knowledge about
goals, plans, tasks & subtasks, commitments and performance.
Teams cooperative through partially synchronized actions to accomplish individual subtasks and common goals (joint intentions).
Market-Driven Economy Self-interested agents pursue
personal profit. Behavior is driven by the cost of
resources. Agents are controlled by
specifying market rules, rewards and penalties.
Evolutionary Systems Agents populations evolve over
time through reproduction, mutation and natural selection.
Agents are controlled by specifying selection criteria and reproduction process.
(adapted from Gunning, DARPA)
KAoS Extension and Generic Agent
Generic Agent
Agent Extension
ConversationSupport
Transport-LevelCommunicationSecurity
Optional Planner
Various Capabilities
Specific toParticular
Agents
Common toAll Agents
Generic Agent
Instance
Generic Agent
Instance
Agent A
Agent B
Agent-to-Agent Protocol
Agent Domain
Agent-to-Agent Communication Within an Agent Domain
Why Agents Are Useful in eBusiness Autonomy
Business Units Have Different Goals, Preferences, and Resources Goal-Directed Planning Situation Monitoring/Rapid Response to Changing Conditions and Markets
Heterogeneity Size (SME/Fortune 500) Role (Buyer, Partner, Supplier) National Differences (Culture, Legal Restrictions)
Dynamic Relationships Rapid Formation/Dissolution of Business Relationships Real-Time Negotiation of Contracts/Changes Accommodate Cultural Differences in Styles of Interaction
Integration of Knowledge Management and Business Process Execution Nonlinear Information Flows Business Processes Adapt to Perceptions of Environment Design for Manufacturability Quality Management/Improvement
The Role of Intelligent Business Agents in Electronic Commerce
Natural Merging of Object Orientation and Knowledge-Based Technologies
Rich and Expressive Models of an Enterprise Incorporate Reasoning Capabilities Within the Business
Application Logic Inclusion of Learning and Self-Improvement Capabilities Use Interaction Protocols and Organizational Knowledge
to Engage in Task-Oriented Business Dialogues
Structure of a Typical Business Agent
PROCUREMENT AGENT
Information Brokering Agent
Negotiation Agent
Planning Agent
Scheduling Agent
Translation Agent
Integration Agent
Local Knowledge
Base
Resource Descriptions
Ontology
Adapted From: The Role of Agent Technology in Business to Business Electronic Commerce, Mike P. Papazoglou
Example of Negotiation in a Multi-Agent Business Web
A
D
CB
41
23
5,7,10
1,6,91,12,14 8,11,13
1. REQUEST: 50 Widgets at Catalog Price by Next Thursday 2. QUESTION: Are You Bidding on As RFQ3. INFORM: Yes, I Am.4. REFUSE 5. PROPOSE (INFORM & REQUEST): How About 40 Widgets at Catalog Price by Next Friday?6. REQUEST :Please Send Me 40 Widgets at Catalog Price by Next Friday.7. COMMIT: I Plan to Send You 40 Widgets at Catalog Price by Next Friday.8. COMMIT: I Plan to Send You 50 Widgets at Catalog Price by Next Thursday.9. ASSERT: I Found a Better Supplier, and Am Not Relying on Your Commit.10. REFUSE: I Am Abandoning My Commit.11. SHIP: Here Are Your Widgets. Please Pay Me.12. ASSERT & REQUEST: You Are 5 Widgets Short. Please Send the Difference.13. SHIP: Her are 5 More Widget. Please Pay Me.14. Pay.
Adapted from Specifying Agent Interactions Using UML, James Odell
Agent Standards
Foundation for Intelligent Physical Agents (FIPA) Non-profit organization aimed at producing standards for the
interoperation of heterogeneous software agents Main industry locus of action since 1996 for intelligent agents
knowledge-interchange standards work Object Management Group (OMG)
Formed in 1989 to create a component-based softwaremarketplace by hastening introduction of standardized object software
Standard interfaces for distributed object computing Common Object Request Broker (CORBA)/ Internet Inter-ORB
Protocol (IIOP) Unified Modeling Language (UML) and other specifications
supporting analysis and design Agent-Based UML (AUML)
extend UML to express agent-based concepts
What Needs to be Standardized?
Domain Conceptualizations Product Descriptions Process Descriptions Transaction Content
Communication Primitives Modes of Interaction (Query, Publish)
Interaction Protocols Negotiation
FIPA SpecificationsGuiding Principles
Openness (Dynamic Participation) Agents can join or leave at run time without recompiling or
reconfiguring FIPA naming conventions and registration process used to find the
location of another agent
Interoperability (Abstract Specification) Minimum set of requirements Avoid commitment to particular hardware
Explicitness (Declarative Specification) Information and assumptions about the agent system are explicit
Roles and capabilities of the agents Modes of interaction Meaning of message content
Structure of FIPA Specifications Abstract Architecture
Abstract designs that can be formally related to every valid implementation Agent Message Transport
Delivery and representation of messages across different networkenvironments
Agent Management Logical model for the creation, registration, location, communication,
migration and retirement of agents Agent Communication
Communicative acts based on speech theory that are independent of the content of the message
Interaction protocols describe entire conversations between agents for the purpose of achieving some interaction of effect
Agent Applications Service and ontology descriptions Case scenarios Integrating legacy software not communicating via FIPA ACL
Software Agent Research and Technology at Boeing
NASA/FAA Aviation Extranet Collaboration DARPA Control of Agent-Based Systems
Jumpstart Coalition Technology Integration Experiment
Intelligent Agent Technology Investment Project
Aviation Extranet GoalsBy the turn of the century, airlines will be able to dynamicallyBy the turn of the century, airlines will be able to dynamically
reconfigure their flight operations for improved safety and morereconfigure their flight operations for improved safety and moreefficient transportation for the traveling publicefficient 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 Extranet = virtual private network Software agents provide security and intelligent coordination of resources Foundation of meta-databases, data warehouses, CORBA & Web technologies
Conduct advanced research in decision support tools for the Aviation Community
The CoABS Agent Grid
User-Centric
Agents
Translation Agents
Problem
Solving
Agents
Control Agents
Grid is Dynamic Matrix supporting:
Dynamic teaming
Brokering services
Variable semantics
(adapted from Kettler, 1999)
Grid Forum Activities
Agent Development for the Grid
The Future of Agent Ensembles Dynamically created teams of agents written by independent
groups, using different frameworks and different assumptions about agent context and approach
Greatly increased diversity of reasoning/planning capability Greatly increased scope and difficulty of agent tasks
The Future of Agent Developers More agents written by domain experts with minimal training;
fewer agents written by agent-technology experts Decreased ability to apply formal agent theory Decreased ability to understand/predict agent environment
Jumpstart Agent Toolkit
Conversation Design Tool Policy-based graphical communications design Help developers select, specialize or generate conversation
policies consistent with communicative intent
Agent Management Tool Incorporate policies for Javas extended resource management
and mobility mechanisms Allow agent developers to easily select, specialize or generate
appropriate resource management and security policies Includes visualization and dynamic control tools to monitor and
manage optimal resource utilization and to stop errant agents
CoAXCoAX: The Coalition Agents Experiment: The Coalition Agents ExperimentJeffrey M. BradshawJeffrey M. Bradshaw
Institute for Human and Machine Cognition (IHMC)Institute for Human and Machine Cognition (IHMC)AFRL BriefingAFRL Briefing
12 December 200012 December 2000
AFRL Rome, AIAI, Boeing, Dartmouth, DERA Malvern, Lockheed AFRL Rome, AIAI, Boeing, Dartmouth, DERA Malvern, Lockheed Martin ATL, Michigan, MIT Sloan, OBJS, USC/ISI, UWF/IHMCMartin ATL, Michigan, MIT Sloan, OBJS, USC/ISI, UWF/IHMC
Support from BBN, GITI, ISX, MITRE, Schafer, StanfordSupport from BBN, GITI, ISX, MITRE, Schafer, Stanfordhttp://www.http://www.aiaiaiai.ed.ac..ed.ac.ukuk/project/coax//project/coax/
DARPADARPA
Key Technical Drivers Cannot assume interoperability, reliability or
availability of different nations systems Need for partial (secure) sharing and visualization
of processes, data and facilities Need to work with agents in multiple
dynamically-determined domains Need for flexible inter-agent task and process
management Need for rapid formation, management and
change of agent relationships
Policy-MediatedAgent Interaction
What is a policy? Formal specification to describe or govern interaction with other agents,
groups of agents, or grid resources Provides a way to anticipate problems, facilitate and enforce desired
behavior, assist problem resolution, and justify sanctions for non-compliance
Examples Security policies: e.g, all messages must be encrypted Resource policies: e.g., ceilings on system resource consumption Conversation policies: e.g., timing and message sequencing Domain membership policies: e.g, agents cannot simultaneously be
members of domain X and domain Y Benefits of explicit policy: reuse, operational efficiency, responsiveness to changed
conditions, possibility of off-line policy verification, tunable social control
Intelligent Agent Technology Project Description
Safe and Secure Execution for KAoS Dynamic policies for security and resource allocation
Scalable Distributed Information Access Prototype for smart information access Multiple concurrent users Across multiple data services
Agent Architecture Investigation Import and demonstrate the best features of other agent
architectures with respect to security and scalability
Conclusions -- Multi-Agent Systems in eBusiness
The Capabilities of the Multi-Agent System Paradigm Match the Requirements of the Global eBusiness Environment Autonomous, heterogeneous business units Flexible, adaptive business structures Supports a wide range of styles of interaction (coalition, competition,
teams) Multi-Agent Systems Paradigm Builds on the Object-Oriented
Paradigm and Extends its Capabilities (Evolution vs. Revolution) Multi-Agent Systems Standards are Under Development at FIPA and
OMG. Practical Experiments in MAS are Underway to Evaluate Standards
and Prototype New Capabilities
Conclusions
Enrichment of Web-based Business Interactions with Knowledge-Based Technology Will Enable New eBusiness Capabilities
Shared Domain Conceptualizations (Ontologies) Provide the Capability to Seamlessly Share Knowledge and to Identify Business Partners and Opportunities
Rapid Advances in Expressivity and Inference Competency Can Be Expected
Multi-Agent Systems Technology Provides the Capability for Intelligent Interaction Among Diverse and Distributed Business Entities
Interoperability of Heterogeneous Agent Frameworks and New Modesof Interaction Are Under Intensive Development