Research Directions for Agents and Ontologies

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Research Directions forAgents and Ontologies

• Michael N. Huhns
• Center for Information Technology
• University of South Carolina

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The Fundamental Problem

• We would like to arrange for effective and
  efficient interactions among large numbers of
  heterogeneous information components databases,
  applications, and interfaces
• Difficulties are
• Components are incomprehensible, inconsistent,
  and often unknown in advance
• We need to enable updates as well as retrievals
• The information environment is open
• We need to consider process and policy, as well
  as structure

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MCC Carnot ProjectOntologies and Articulation
Axioms
Common Ontology
Application 1
Interface 1
TransportationDevice
Articulation Axiom 3
Articulation Axiom 1
Train
Vehicle
Boat
Truck
Automobile
Jeep
Articulation Axiom 2
Articulation Axiom 4
DB1
DB2
Car
Auto
id
make
no
model
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Three Problems

• Where does the ontology come from?
• The ontology must be greater than or equal to any
  local schema
• How are the mappings created?
• In general, they are arbitrarily complex and not
  one-to-one
• Both schemas and data must be mapped
• How and when are the mappings applied?
• Agents

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Ontology Development

• Bottom-Up from Schemas and Key Words
• identify databases
• identify names for all tables, fields, and
  enumerated values (e.g., if value is limited to a
  primary color red, green, or blue)
• form groups of common concepts and assign name to
  covering concept for each group
• iterate or
• Extensional View form classes from instances

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Ontology Development

• Top-Down from First Principles (intensional
  view) a class is defined by a set of membership
  conditions or properties
• Restrictions on Class Formation
• a class must have instances
• a class must contain all properties common to the
  instances in its extension
• classification should obey cognitive
  economy--instances of a class must share some,
  but not all properties
• classification should enable inference of
  properties based on class membership

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Ontology Development (cont.)

• Restrictions on Class Structures
• Completeness--every property must be used in the
  definition of at least one class
• Nonredundancy--a subclass must be defined by at
  least one property not in any of its superclasses
  (the result is that a subclass is always a
  specialization of any of its superclasses, i.e.,
  it has more properties or restrictions, and has
  fewer instances)

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AlternativeRelate Ontology Pieces
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Tools for Developing Ontologies

• Ontolingua and Chimaera (Stanford)
• SHOE Simple HTML Ontology Extension language (U.
  of Maryland)
• JOE Java Ontology Editor (U. of South Carolina)
• IMTS (MCC)
• Cyc Unit Editor
• UML, ER, and Conceptual Modeling Tools

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Classification Is Difficult!

• From the ancient Chinese encyclopedia Celestial
  Emporium of Benevolent Knowledge, It is written
  that animals are divided into
• belonging to the emperor
• embalmed
• tame
• sucking pigs
• sirens
• fabulous
• stray dogs
• included in the present classification
• frenzied
• innumerable
• drawn with a very fine camel-hair brush
• et cetera
• having just broken the water pitcher, and
• that from a long way off look like flies.

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Creating Semantic Mappings
Interface Application Database
CASE Tool
use
generate
Conceptual Schema
Cognition
Universe of Discourse 1
MIST
Universe of Discourse 2
Interface Application Database
CASE Tool
use
generate
Conceptual Schema
Cognition
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Semantic Translation
User
Application 1
Application n
Agent for Application
Agent for Application
Common
Enterprise-Wide
View
Agent for Resource
Agent for Resource
Agent for Resource
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An Agent-Oriented Information System
Middleware Mediators, Brokers, Facilitators,
Ontologies, and Registries
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(de facto) Standard Agent Types and Architectures
Application Program
User Interface Agent
MCC InfoSleuth CMU RETSINA SRI OAA USC-ISI SIMS
TeamCore Global InfoTek Grid
Reply
Reg/Unreg (KQML)
Reply
Query or Update (SQL)
Ontology Agent
Broker Agent
Reg/Unreg
(KQML)
Mediator Agent
Ontology (OKBC)
Reg/Unreg (KQML)
Registry Agent
Mediated Query (SQL)
Reg/Unreg (KQML)
Schemas (CLIPS)
11179 Registry
Mediated Query (SQL)
Reply
Reply
Database Resource Agent
Database Resource Agent
SQL (JDBC)
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Agent Abstractions

• Agent abstractions are mentalistic
• beliefs agents representation of the world
• knowledge (usually) true beliefs
• desires preferred states of the world
• goals consistent desires
• intentions goals adopted for action

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Multiagent Abstractions

• Multiagent abstractions involve interactions
• social about collections of agents
• organizational about teams and groups
• ethical about right and wrong actions
• legal about contracts and compliance

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Why Do These Abstractions Matter?

• Because modern applications go beyond traditional
  metaphors and models in terms of their dynamism,
  openness, and trustworthiness
• virtual enterprises manufacturing supply chains,
  autonomous logistics
• electronic commerce utility management
• communityware social user interfaces
• problem-solving by teams

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Fundamentals ofSocial Abstractions

• Commitments social, joint, collective,
• Organizations and roles
• Teams and teamwork
• Joint intentions vs. individual rationality

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Kinds of Commitment

• Psychological or mental an agents state of
  being committed to a belief or an intention
• Joint agents commitments to the same intention
  or belief
• Mutual agents commitments to one another with
  respect to the same condition

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Social Commitments

• An agents commitment to another agent
• unidirectional
• arising within a well-defined scope or context,
  which is itself a MAS
• revocable within limits

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Organizations

• Organizations help overcome the limitations of
  agents in various respects
• reasoning
• capabilities
• perception
• lifetime and persistence
• shared context, essential for communicating

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Modeling Organizations

• Abstractly, organizations
• consist of roles
• requiring certain capabilities
• offering certain authorities
• involve commitments among the roles
• Concretely, organizations
• consist of agents
• acting coherently

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Teams

• Tightly knit organizations
• shared goals, i.e., goals that all team members
  have
• commitments to help team members
• commitments to adopt additional roles and offer
  capabilities on behalf of a disabled member

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Teamwork

• When a team carries out some complex activity
• negotiating what to do
• monitoring actions jointly
• supporting each other
• repairing plans

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Joint Intentions

• Traditional accounts of teams are based on joint
  intentions and mutual beliefs
• Team-members jointly intend the main goal of the
  team, which means that they
• all intend it and mutually believe that they
  intend it
• each will notify the others if it drops out and
  mutually believe this notification requirement

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Elements of Trust

• Ultimately, what we would like is to trust our
  agents. Trust involves
• having the right capabilities
• following legal contracts where specified
• supporting ones organization or society
• being ethical
• failing all else, being rational

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Agent-Oriented Software and Software Engineering
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Programming Paradigms

• 1950s -- Machine and assembly language
• 1960s -- Procedural programming
• 1970s -- Structured programming
• 1980s -- Object-based and declarative
  programming
• 1990s -- Frameworks, design patterns, scenarios,
  protocols, and components (ActiveX/COM and Java
  Beans)
• The trend has been from elements that represent
  abstract computations to elements that represent
  the real world

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Interaction-Oriented Software Development

• Modules are active
• Modules are specified declaratively, in terms of
  what, not how
• Modules hold beliefs about the world, especially
  about themselves and others
• Modules more closely represent real-world objects
  
• Modules volunteer

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Programming Paradigm

• Effort is on assembling and coaching a team to
  achieve desired functionality
• Requirements specified in terms of social
  commitments and team intentions
• Components negotiate with each other, enter into
  social commitments to collaborate, and can change
  their mind about their results

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Features ofLanguages and Paradigms
Concept
Procedural Language
Object Language
Multiagent Language
Abstraction Building Block Computation
model Design Paradigm Architecture Modes of
Behavior Terminology
Type Instance, Data Procedure/Call Tree of
procedures Functional decomposition Coding Implem
ent
Class Object Method/Message Interaction
patterns Inheritance and Polymorphism Designi
ng and using Engineer
Society, Team Agent Perceive/Reason/Act Cooperativ
e interaction Managers, Assistants, and
Peers Enabling and enacting Activate
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Example Children Forming a Circle
(Most business software modules, which are
passive, are meant to represent real objects,
which are active)
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What is IOP?

• A collection of abstractions and techniques for
  programming MAS
• Classified into three layers of mechanisms
• coordination living in a shared environment
• commitment organizational or social coherence
  (adds stability over time)
• collaboration high-level interactions combining
  mental and social abstractions

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Benefits of IOP

• Like conceptual modeling, IOP offers a
  higher-level starting point than traditionally
  available
• key concepts of coordination, commitment,
  collaboration as first-class concepts that can be
  applied directly
• aspects of the underlying infrastructure are
  separated, leading to improved portability
• ultimately, can lead to a many-fold increase in
  software productivity and performance by changing
  the way in which software is developed and
  deployed

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Open Problems in Agent Technology

• Design rules for cooperative systems
• Workflows
• Ontologies
• Security concerns in open environments
• Scaling to millions of agents
• User interfaces to heterogeneous resources
• to acquire resource constraints
• to access information
• to control workflows
• Shared ontology maintenance

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Ubiquitous Agents

• Agent technology will affect
• homes
• transportation
• commerce
• business (supply chains)
• education
• Agents are popular because they provide a new way
  to think about systems --they are not simply a
  parallelization of a centralized system!

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A Commuter Approach to Logistics

• Instead of following a centralized plan for
  deploying supplies, imagine that each item of
  materiel is an intelligent agent whose sole
  objective is to reach its assigned destination.
  Just like a person commuting to work, this agent
  would dynamically
• decide its means of conveyance
• contend for storage and transportation resources
• avoid or resolve conflicts with other agents
• make local decisions as it wends its way through
  a distribution network