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