Dynamic Matchmaking between Messages and Services in Multi-Agent Systems Muhammed Al-Muhammed David W. Embley Brigham Young University

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Dynamic Matchmaking between Messages and Services
inMulti-Agent Systems Muhammed
Al-MuhammedDavid W. EmbleyBrigham Young
University
Supported in part by NSF
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Motivation

• Agents cooperate to achieve goals
• Cooperation needs communication
• Communication possible if agents
• 1- share ontologies,
• 2- speak the same language,
• 3- pre-agree on message format.

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The Problem
Agents must 1- share ontologies, 2- speak the
same language, 3- pre-agree on message format.

• Requiring these assumptions precludes
• agents from interoperating on the fly

The holy grail of semantic integration in
architectures is to allow two agents to
generate needed mappings between them on the fly
without a priori agreement and without them
having built-in knowledge of any common
ontology. Uschold 02
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Solution
The problem was, agents must 1- share
ontologies, 2- speak the same language, 3-
pre-agree on message format.

• Eliminate all assumptions

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MatchMaking System (Initialization)
(MMS)
MatchMaking System
Message-Service Matching
Message Handling
Global Ontology
Response Handling
Services (Agent- Independent Representation)
Translation Repository
Mapping
Translation
Service Analysis
Agent LO code Services
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Some Mapping Problems
Local Ontology Global Ontology
Word SellingValue Word Price
Synonyms Price Synonyms SellingValue
Type Integer Type Real
Value 500 Value Price Recognizer
Domain Computer Domain Computer
Relation ISFOR Memory Relation ISFOR Memory
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MatchMaking System (Initialization)
(MMS)
MatchMaking System
Message-Service Matching
Message Handling
Global Ontology
Response Handling
Services (Agent- Independent Representation)
Translation Repository
Mapping
Translation
Service Analysis
Agent LO code Services
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Matchmaking System (Operation)
MMS
MMS
Message-Service Matching
Message-Service Matching
Message Handling
Message Handling
Global Ontology
Global Ontology
Response Handling
Response Handling
Services (Agent- Independent Representation)
Services (Agent- Independent Representation)
Translation Repository
Translation Repository
Mapping
Mapping
Translation
Translation
Service Analysis
Service Analysis
Agent1 LO code Services
Agent2 LO code Services
I need info about PCs InputLowPrice500, HighPri
ce1000 Output String Make, String Model,int
Price ConstraintNone
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Some Matching Problems
The Service The Service The Message The Message
ServiceName ServiceName PcInfo ServiceName ServiceName None
ActionType ActionType None ActionType ActionType None
ServiceType ServiceType Query ServiceType ServiceType Query
Input Input String PROCESSOR String ADDRESS String MEMORYTYPE String MEMORYCAPACITY Input Input String PROCESSORIntel P4 2.6GHz String CITY Provo, STATE UT, ZIPCODE 84602 String MEMORY SDRAM 512MB
Output Output Date DATE (mm/dd/yy) Real PRICE (USD) Int WARRANTY Output Output String DATE (yyyy-mm-dd) String PRICE (EUR)
InConst. InConst. None InConst. InConst. None
OutConst. OutConst. None OutConst. OutConst. Output sorted(Price)
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Matchmaking System (Operation)
MMS
MMS
Message-Service Matching
Message-Service Matching
Message Handling
Message Handling
Global Ontology
Global Ontology
Response Handling
Response Handling
Services (Agent- Independent Representation)
Services (Agent- Independent Representation)
Translation Repository
Translation Repository
Mapping
Mapping
Translation
Translation
Service Analysis
Service Analysis
Agent1 LO code Services
Agent2 LO code Services
I need info about PCs InputLowPrice500, HighPri
ce1000 Output String Make, String Model,int
Price ConstraintNone
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Matchmaking System (Operation)
MMS
MMS
Message-Service Matching
Message-Service Matching
Message Handling
Message Handling
Global Ontology
Global Ontology
Response Handling
Response Handling
Services (Agent- Independent Representation)
Services (Agent- Independent Representation)
Translation Repository
Translation Repository
Mapping
Mapping
Translation
Translation
Service Analysis
Service Analysis
Agent1 LO code Services
Agent2 LO code Services
I need info about PCs InputLowPrice500, HighPri
ce1000 Output String Make, String Model,int
Price ConstraintNone
Price1USD .
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Preliminary Results

• MMS Implemented
• Real-World Test Cases
• Computer shopping
• Book shopping
• Meeting scheduling
• Global Ontology Drawn from
• Web sites (for shopping applications)
• Individual user-chosen words and phrases (for
  scheduling)
• Agents Coded wrt
• Each Web site (for shopping applications)
• Each individuals worksheet (for scheduling)
• Successful Agent Communication (using MMS)

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Contributions
MMS

• Dynamically generates mappings among agents
• Simplifies agent communication
• Simplifies a developers task
• Increases message answering capabilities

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