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Search and Agent October 8, 2002 Jae Kyu Lee Graduate School of Management Korea Advanced Institute of Science and Technology Consumer Mercantile Activities Product/service search and discovery in the information space Comparison shopping and product

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Search and agent l.jpg

Search and Agent

October 8, 2002

Jae Kyu Lee

Graduate School of Management

Korea Advanced Institute of Science and Technology


Consumer mercantile activities l.jpg
Consumer Mercantile Activities

Product/service search and

discovery in the information space

Comparison shopping and product

selection based on various attributes

Prepurchase

interaction

Nogotiation of terms, e.g., price, delivery times

Placement of order

Purchase

consummation

Authorization of payment

Receipt of product

Postpurchase

interaction

Customer service and support (if not

satisfied in X days, return product)


Fundamental stages of buying process l.jpg
Fundamental Stages of Buying Process

  • Stages (Maes’ View, CACM 1999)

    1. Need identification

    2. Product brokering

    3. Merchant brokering (price, warranty, availability, delivery time,

    and reputation)

    4. Negotiation (fixed price introduced only 100 hundred years ago)

    5. Purchase and delivery

    6. Product service and evaluation

  • Buying Behavior studied by Nicosia, Howard-Sheth,

    Engel-Blackwell, Bettman, and Andreasen

  • Role of Software Agent[mediate consumer behaviors]

    Information filtering and retrieval, personalized evaluations,

    complex coordination, and time-based interactions.


Search and comparison shopping l.jpg
Search and Comparison Shopping

  • Key Word Search and Directory

  • Meta-Key-Word Search and Meta-Directory

  • B-Cart and Meta-Malls Architecture

  • Agents based Comparison


Search engine l.jpg
Search Engine

  • Gathering:

    • Manually or automatically

    • Search Agent vs. Mobile Agent

  • Indexing

  • Storing

  • Retrieval


Intelligent search on documents l.jpg
Intelligent Search on Documents

  • Key Word Search: String matching

    • Advanced Search : Conditional, AND/OR relationships, and filtering

    • Demonstrate the advanced features in google.com

  • Proximity Search: Synonyms

    • Languages and Translations

  • Concept Based Search:

    • Concept Object Hierarchy

    • Directory and Linkages

  • Similarity Based Search: Case Based Retrieval

    • Case Based Reasoning; Associations

  • Image Search: Content-Based Image Retrieval

    • Image Processing vs. Text-based Index about images


Slide7 l.jpg

Concept Object Hierarchy

Home

Requirement

Level of Detail

Balancing

Buyer :e-Procurement Directory of KAIST

Supplier: MarketSite.net

Home

+

Packing machi

General Office Supplies

+

Procurement

-

+

Labeling mach

Computer Supplies

Logic Expression

+

+

Sorting machin

Computer Accessories

Generation of Directory

+

+

Peripheral Data Storage

Typing machin

Consistency of Directory

-

+

Printer Supplies

Binding and La

-

Management of Rule Base

+

Toner

Office machina

-

+

Use for Hewlett-Packard

Fusers and acc

Extract

+

+

LaserJet 4V

Printer, fax an

Q & A

+

Manual

LaserJet 5

+

Printer, fax

Help

-

+

LaserJet 6P

Printer, fax

E-Mail

+

  • HP Model C3903A Image Cartri

Toner

+

  • Nu-Kote Model LT105R Image

Transfer roll


Popular search engines l.jpg
Popular Search Engines

  • Google. Com and Google.co.kr

  • Yahoo

  • Empas

  • Naver

  • Daum.net

  • Simmani

  • Hanmir

  • Dreamwiz

  • Lycos

  • Altavista


Meta key word search and meta directory l.jpg
Meta-Key-Word Search and Meta-Directory

Relevant

Pages

Directoies

KeyWord

Inquiry

Customer

  • How to Acquire Knowledge about Directory?

    • Manual Registration

    • Automatic Learning

Meta

Directory

Merged

Response


Meta search engines experimental l.jpg
Meta Search Engines: Experimental

  • All-in-One Search Page

    • http://www.albany.net/allinone

  • CUSI

    • http://web.nexor.co.uk/cusi/cusi.html

  • Fun City Web Search

    • http://www.funcity.com/search.html

  • HyperNews

    • http://union.ncsa.uiuc.edu/HyperNews/get/www/searching.html

  • Info Market

    • http://www.infomkt.ibm/com/

  • Meta Crawler

    • http://www.cs.whashington.edu:8080/

  • Savvy Search

    • http://www.cs.colostate.edu/~drelling/smartform.html

  • Searchers

    • http://gagms.www.com/~boba/search.html

  • W3 Search Engine

    • http://cuiwww.unige.cd/meta-index.html

  • Web-Search

    • http://www.bidderford.com:80/~soaring/


Mobile agents l.jpg
Mobile Agents

  • Mobile Agents = Mobile Robots

  • Why Robot Exclusion Standard

    • 1993-1994: Too frequent visits of mobile agents

    • Disturbed the visiting servers

    • Robot Exclusion Standard was necessary

  • Format

    • “<field>: <optionalspace><value> <optionalspace>

    • Values in Field: User-agent, Disallow

  • Examples 1:

    User-agent: *

    Disallow: /cyberworld/map/

    Disallow: /tmp/

    • Any robots are not allowed to visit the URLs in “/cyberworld/map/”, “/tmp/”


Examples robot exclusion standard l.jpg
Examples: Robot Exclusion Standard

  • Examples 2:

    User-agent: *

    Disallow: /cyberworld/map/

    User-agent: cybermapper

    Disallow:

    • All robots except the cybermapper are not allowed to visit

      “/cyberworld/map/”

  • Examples 3:

    User-agent: *

    Disallow: /

    • No robot is allowed to visit.


Five types of comparison support tl2 11 p 58 63 l.jpg
Five Types of Comparison Support(TL2.11, p. 58-63)

1. Search on Hypertext files using Agents

  • Bargain Finder

    2. Search in a Web-based Database: human and S/W agents sharing information

  • Mysimon, Junglee, Jango

    3. Comparable Item Retrieval and Tabular Comparison

  • Compare.net (Side by Side comparison possible)

  • Meta-Malls Comparison Architecture

    4. Comparison of Multiple Items from Multiple eMarketplaces

    - Personalized B-Cart

    5. Comparison as a Multiple Criteria Decision Making

  • Personalogic


Characteristics of key comparison sites l.jpg
Characteristics of Key Comparison Sites

ILA* : Internet Learning Agent engine, 50% Performance Enhancement to BF

VDB* : Virtual DB

ILA*: Internet Learning Agent Engine (50% Performance Enhancement to Bargain Finder)

VDB*: Virtual DB


Compare shopping sites l.jpg
Compare Shopping Sites

  • Google.com: Search “Comparison Shopping”

    • Get List of Sites

  • PricingCentral.com

    • List of search engines for item categories

    • Try with Notebook: Review the site and user’s rating

  • Dealtime.com

    • Specificational conditions

    • Display stores with rating, product specificaitions, price and link

  • Compare.Net

    • Tabular Comparison

  • Dell.com

    • By Application Category, Customized Configuration and offer price

  • CNET.com

    • Post Popular: Comparative Review

    • Show by Models

    • Show Stores with rateing, price, shipping cost, stock status, take orders, related products, Price Drop Alert

  • MySimon

    • Specification based Search


Korean comparison sites l.jpg
Korean Comparison Sites

  • Omi.co.kr

  • Bestbuyer.co.kr

  • Danawa.co.kr

  • Enuri.com

  • Shop.Lycos.co.kr

  • Price.naver.com

  • Clickprice.co.kr

  • Yavis.com

  • Compare.co.kr

  • Shopbinder.com

  • Mymargin.com

  • Okprice.com

  • Shoppal.net






Current status of comparison shopping l.jpg
Current Status of Comparison Shopping

  • Retrieval of Standard Models

  • Graphic Displays

  • Preference Based Scoring

  • Tabular Comparison

  • Configuration Support

    • Intelligence: Rules, Constraints, Preference, Similarity

       Multiple Criteria Decision Support


Opportunities in comparison shopping l.jpg
Opportunities in Comparison Shopping

Research Opportunities

  • Configuration with Options:

    - CBR: Find the Best Standard Model

    - CSP: Search toward the best options

  • Multi-vendor based Configuration and Order Processing

    • Pick and Delivery necessary

  • Buyer Agents

  • Buyer’s Bahavior?


Architecture of b cart l.jpg
Architecture of B-Cart

Buyer

Sellers

e

-

Procurement

e

-

Catlaog

MyB

-

Cart

System

Collect / Order

MyS

-

Cart

.

.

.

.

.

.

Update

Intermediaries

Buyer

Visit / Order

e

-

Catlaog

Interface

MyI

-

Cart


Meta malls architecture l.jpg
Meta-Malls Architecture

  • Goal

    • Shopping Over Multiple Independent Cyber Shopping Malls

    • One Stop Shopping and Payment over Multiple Malls

    • Product Level Comparison Support As A Multiple Critera Decision Marking


Characteristics of the meta malls architecture l.jpg
Characteristics of The Meta-Malls Architecture

  • Meta-Malls Coordinator Keeps:

    • Summary Products and URL

    • Relationship Indices between Products and Malls

    • Communicate with the Mall Operators

  • Mall Operator

    • Independent Entities

    • But has optional linkage with Meta-Mall Coordinator

    • Merchant: Compatibility with the Coordinator necessary


Individual buyer s cart b cart l.jpg
Individual Buyer’s Cart: b-Cart

  • Tentative Picking and Decision Support

  • Allow Budget Consideration

  • Supporting Record Keeping Possible

  • Individual Buyer Assistant in Buyer’s Personal Computer

  • Coupling with the ERP can be supported

  • One-Stop Payment

  • IC-Card Based User Access for Certification and Electronic Wallet can be supported



Agent based commerce l.jpg
Agent Based Commerce

  • Agent

  • Software Agent

  • Intelligent Agent

  • Agent Communication Languages

  • Robot

  • Softbot


Slide35 l.jpg

Definitionof Agent Technologies

(U.S. Lee, ICEC 98 Tutorial)

Internal definition

A software module which acts with its own knowledge, belief, interest and intention against informations given from environment

External definition

A functional unit which acts like a human being or transacts tasks on behalf of a human with human features visually and aurally

-

-

A general term to indicate a functional module which solves

a problem autonomously toward a specific goal by recognizing

situation and cooperating other systems based on interactions

with environment


Slide36 l.jpg

Features of Agent

[Wooldrige and Jennings (1994)]

Weak Notions

-

  • Autonomy or semiautomatic

  • Social ability

  • Reactivity

  • Pro-activeness

-

-

-

Strong Notions

-

Mentalistic notions

Knowledge

Belief

Intention

Obligation

Emotion

-

-

-

-

-


Slide37 l.jpg

Features of Agent

Other Attributes

-

  • Continuous running: monitoring

  • Mobility : Mobile Agent

  • Veracity (성실성)

  • Benevolence (선행성, 순응성)

  • Rationality

-

-

-

-


Slide38 l.jpg

Core technologies for Agents in EC

  • Autonomous / Intelligence

    • Recognition of environment

    • Problem solving / Learning mechanism

-

-

  • Communications

    • Task / Result / Knowledge sharing

    • Agreement / Negotiation / Competition / Balancing

-

-

Anthropomorphism


Slide39 l.jpg

A Prototype of Agent Development

AGENT

ENVIRONMENT

Processing Modu.

Problem Solver

Cooperation Controller

Behavior Controller

Communication Modu.

  • Human Clients

  • Other Agents

  • / Systems

Message/event handler

Protocols for

collaboration

negotiation

Knowledge Modu.

Knowledges for

problem solving

control

cooperation

knowledge management

See UNIK-AGENT


Agent based ec contract type and protocol l.jpg

Seller R1

Seller R4

Seller R2

Seller R3

Agent-based EC: Contract Type and Protocol

Requirement of Human Customer

Requirement of Human Customer

Buyer C1

Buyer C0

Final Approver1

Final Approver2

Final Approver3

Final Approver4


Architecture of unik agent l.jpg

Directory

Architecture of UNIK-AGENT

AGENT

Communication Controller

Problem Solver

Outgoing Msg

Problem Solving Manager

Incoming Msg

Message Base

Message Manager

Directory Consulting

Other

Agents

Solution Engines

Individual Messages

Message Queue Mgt.

Data Base

Knowledge Base

Message Gate


Slide42 l.jpg
KQML

  • KQML

  • Performatives

    ask-if, evaluate, tell, etc.

  • Performative Parameters

    sender, receiver, reply-with, in-reply-to, content, ontology, and language.

AGENT A

(ask-if

:language KIF

:ontology electronic

:reply-with q1

:content (> (size chip1)

(size chip2)))

AGENT B

(tell

:language KIF

:ontology electronic

:in-reply-to q1

:content true)


Kqml message examples l.jpg
KQML Message Examples

evaluate

: content <expression>

: language <word>

: ontology <word>

: reply-with <expression>

: sender <word>

: receiver <word>

reply

: content <expression>

: language <word>

: ontology <word>

: in-reply-to <expression>

: forte <word>

: sender <word>

: receiver <word>


Kqml examples l.jpg
KQML Examples

ask-if

: content <expression>

: language <word>

: ontology <word>

: reply-with <expression>

: sender <word>

: receiver <word>

tell

: content <expression>

: language <word>

: ontology <word>

: in-reply-to <expression>

: force <word>

: sender <word>

: receiver <word>


Three layers of ec messages l.jpg
Three Layers of EC Messages

  • Agent Communication Language Layer

    • Domain independent communication language among agents (e.g. Knowledge Query and Manipulation Language: KQML)

  • Electronic Commerce Layer

    • Message types and items for agent based commerce

  • Product Specification Layer

    • Representation the specification of products


Message standard in three layers l.jpg
Message Standard in Three Layers

(evaluate

:sender

C1

:receiver

R1 R2 R3 R4 R5

:reply-with

msg_960924_1

:ontology

Agent Based Commerce

:language

UNIK-OBJECT

:content

((title RFP)

(contract_ID contract_960924)

(contract_type

(number_of_bid_round 1)

(competitor_price_referable not_referable)

(announced_estimated_price_limit enforced)

(number_of_proposal_for_each_bidder 1)

(bid_price_change not_allowed)

(bid_price_open_time at_predetermined_time)

(buyer_bidder_prenomination prenominated_by_customer)

(human_involvement enforced)

(bidding_price_type total_amount)

(buyer_rule_for_selection_of_successful_bidder min_price)

(buyer_nego_between_price_&_spec not_allowed))

Agent Ccommunication Language Layer

(bid_time

Electronic Commerce Layer

(end_time 96/10/01/11/00))

(requirement

(payment_method credit_card)

(delivery_method postal)

(delivery_date Oct 14, 1996)

(products

(item_name

PC)

(quantity

1)

(amount

(<= 2500))

Product Specifications layer

(specifications

(main_memory (>= 16MB))

(hard_disk (>= 840MB))

(processor (>= Pentium90)))))))


Application of agents l.jpg
Application of Agents

  • Filter information

  • Match people with similar interests

  • Automate repetitive behavior

  • Mediator in e-commerce: Comparison

  • Perishables: travel, theater, and concert tickets and network bandwidth availability

  • Surplus Inventories: gas, electricity, pencils, music, and books

  • Buyer agent: Monitor consumption quantity and usage patterns, and collect, interpret information on merchants and products, making decision about merchants and products, ultimately entering purchase and payment


Types and applications of agents l.jpg
Types and Applications of Agents

  • Comparison Shopping

  • Multi-agent System

    • E-Commerce: Buyer and Seller

    • Scheduling

  • Mobile Agent

  • Assistant Agent: Personal Agent “Sylvie”, Portico

    • Voice Synthesis : (www.genmagic.com/portico[Closed])

    • Natural Language Processing:

      • www.extempo.com[Closed],

      • More focused Intelligence : www.neuromedia.com

  • User Interface Agent

    • AdEater(www.cs.ucd.ie/staff/nick/research/ae[Closed])

  • Intelligent Agents

    • Portals of robots: www.botsport.com[Closed],

    • References:www.csee.umbc.edu/agentsecommerce.media.mit.edu [Closed]

  • E-Mail Filtering: Maxims


Applications of agents l.jpg
Applications of Agents

  • Music Recommendation/ Learning

    1.  Firefly: Customer taste learning

    [Q : How to learn? => Data Mining]

    2.  Similarity Engine (WWW.ari.net/ se)

    Compare with five recommended musics


Applications of agents51 l.jpg
Applications of Agents

  • Document Filtering

    1.  Web Hunter

    - Web Document Filtering [Q: User Defined Conditions]

    - Learn Relevance with Customers [Q: How to identify key word?]

    2.  Eyes

    - Search newly released books

    - Retrieve customer’s interesting areas [Amazon에응용]

    [Q] DB 검색?

    Newly의정의, Customer의Interest

    3.  Con Text

    - Information retrieval => Compress & Summarize


Applications of agents52 l.jpg
Applications of Agents

  • Watcher

    1. Open Sesame!

    - Learn frequently used pattern of programs & files

    [Q: How to identify?] 

    2. News Weeder II

    - Learn frequently used Web pages and news articles

    [another. learning.cs.cmu.edu/ifhome.html] {closed}

    3. Empirical

    - News Learning (Machine learning neural network)

    4. Fishwrap:

    - Newspaper; multiple sources; personalized 

    5. Inter Ap

        - Filter out noises and download the files that user

    may need while the system is idle 

    6. Web doggie


Agent systems in ec table 1 in maes et al cacm 1999 l.jpg
Agent Systems in EC [Table 1 in Maes et al., CACM 1999]

  • Effective for repetitive or predictable (habit) purchase

  • Monitors: Amazon.com (notification agent called Eyes) notifies interesting books.

  • Personalogic: product brokering

  • Tete-a-tete: merchant brokering and negotiation stages, utility theory

  • Firefly: collaborative filtering(www.firefly.com)

    - Use the opinion of like-minded people to offer recommendation

    - Rate people: Nearest Neighbor, CBR, Discriminant analysis

  • BroadVision: rule-based technique to personalize product offering for individual customers

  • Bargain Finder: Price only

  • Jango: Online pickup

  • Junglee: Virtual database technique

  • UNIK-AGENT: KAIST


Agents as mediators in e commerce l.jpg
Agents as Mediators in E-commerce

Table 1. Online Shopping framework with representative examples of agent mediation

Persona Firefly Bargain Jango Kasbah Auction T&T UNIK

Logic Finder Bot

Need indentification

Product brokering

Merchant brokering

Negotiation

Payment and delivery

Service and evaluation

(Maes, March 1999/Vol. 42. No. 3 Communication of the ACM)


Kasbah kasbah media mit edu casbah l.jpg
Kasbah (Kasbah.media.mit.edu) = Casbah

  • Developed by MIT Media Lab.

  • Multi-agent C2C transaction system

  • Seller or Buyer Agent

  • Proactively seek out potential buyers or sellers and negotiate with them on behalf of its owner to complete an acceptable deal; subject to constraint such as initial asking (bidding) price, lowest (or highest) price, a date to complete the transaction, and restrictions on which parties to negotiate with and how to change the price over time.

  • Negotiation Protocol:

    [Buying agent] offers a bid to selling agents with no restrictions on time and price.

    [Selling agent] responds “yes” or “no”

    [Kasbah] provides buyers with one of three negotiation strategies: anxious, cool-headed, and frugal (linear, quadratic, and exponential for increasing its bid over time) [See diagram]

    - Effect of Simplicity in negotiation protocol and user acceptance:

    Experiment


Slide56 l.jpg

Kasbah

Name

Duration

Lowest Price

Highest Price

Strategy


Slide57 l.jpg

Collaboration Protocol between Agent

Contract Net

  • task

    announcement

  • bidding

  • awarding)

Contractors

Managers


Auctionbot auction eecs umich edu l.jpg
AuctionBot (auction.eecs.umich.edu)

  • Developed by the University of Michigan

  • You can start a new auction, or bid in an existing auction.

  • The AuctionBot also provides facilities for examining ongoing auctions.

  • Inspect your own account activity.

  • User creates auction types and specifies parameters (such as clearing times, method for resolving tie bids, the number of sellers permitted).

  • Seller bids a reservation price after creating the auction and let the AuctionBot manage and enforce buyer bidding.


Tete a tete ecommerce media mit edu tete a tete l.jpg
Tete-a-Tete (ecommerce.media.mit.edu/tete-a-tete)

  • Unique negotiation approach to retail sales: cooperative negotiation between the seller and buyer agents (terms: warranties, delivery times, service contracts, return policies, loan options, gift services, and other merchant value-added service.)

  • XML based proposals, critiques, and counter proposals from multiple sales agents.

  • [Seller agent] defines a complete product offerings including a product’s configuration, price, and value-added service. Consider the product and merchant features simultaneously.

  • [Buyer agent] evaluates and orders these proposals based on how well they satisfy its owner’s preference.

  • [[Issue: Time of Preference revelation]]

  • [Buyer] may send critique along one or more dimensions, and broadcast these preference changes to sales agents.

  • [Seller agent] may use them to counter-propose better product offerings.


Slide60 l.jpg

Agents in a Filtering Framework

Meeting Scheduling Agent

NewT

Firefly

Open Sesame!

Homework Helper

: MIT(Distributed Problem Solving)

: MIT

: MIT(Personalized music recom. /

Social filtering)

: Charles River Analytics

: Infonautics Corporation


Challenges of agent based commerce l.jpg
Challenges of Agent-based Commerce

  • Ambiguous content, personalized preferences, complex goals, changing environments, and disconnected parties.

    Toward Standardization

  • Definition of goods and services

  • Consumer and merchant profiles

  • Value added services

  • Secure payment mechanisms

  • Inter-business electronic forms

  • Coalition among agents


Slide62 l.jpg

Features of Next Generation Agents for EC

  • Finds market transaction counterparts, negotiates, and

    transacts on behalf of user

  • Infrastructure for agent-based EC

    • Message and negotiation protocol

    • Brokering

    • Product ontology

  • Personalization

  • Sophisticate negotiation protocol

  • Interoperability with legacy and third-party EC systems

  • Support the supplier who want to construct online

  • shopping market on the Web.


  • Slide63 l.jpg

    Prototype of Balancing Directory

    Home

    Requirement

    Level of Detail

    Balancing

    Buyer :e-Procurement Directory of KAIST

    Supplier: MarketSite.net

    Home

    +

    Packing machi

    General Office Supplies

    +

    Procurement

    -

    +

    Labeling mach

    Computer Supplies

    Logic Expression

    +

    +

    Sorting machin

    Computer Accessories

    Generation of Directory

    +

    +

    Peripheral Data Storage

    Typing machin

    Consistency of Directory

    -

    +

    Printer Supplies

    Binding and La

    -

    Management of Rule Base

    +

    Toner

    Office machina

    -

    +

    Use for Hewlett-Packard

    Fusers and acc

    Extract

    +

    +

    LaserJet 4V

    Printer, fax an

    Q & A

    +

    Manual

    LaserJet 5

    +

    Printer, fax

    Help

    -

    +

    LaserJet 6P

    Printer, fax

    E-Mail

    +

    • HP Model C3903A Image Cartri

    Toner

    +

    • Nu-Kote Model LT105R Image

    Transfer roll


    Slide64 l.jpg

    Logical Representation of Directory

    • Extract Subsets of Seller’s Directory and Define a Buyer’s Directory

    SD

    Buyer’s Level of Detail

    Multiple Supplier

    ...

    MyCo

    Standard Directory

    C

    B

    A

    A

    B

    C

    Occurrence of Unbalance State

    Expressed By Logic


    Slide65 l.jpg

    Ingredients of Buyer Directory Balancing

    • Directory Definition Language

      - Representation in Logic

    • Detection of Unbalanced Topology

    • Directory Manipulation Language

      - Implementation by Logic Programming

    • Automatic Balancing Engine

    • Consistency Maintenance with Multiple Seller’s Directories


    Slide66 l.jpg

    Directory Manipulation Language

    • SPLIT UP “A” TO “MyCo”

     Split_up(“A”, “MyCo”) :-

    link_child_of(“A”, “MyCo”),

    delete(“A”).

    Logic Expression

    MyCo

    MyCo

    Split-Up

    C

    a3

    a5

    a4

    b2

    C

    A

    b2

    c1

    c2

    c1

    b4

    b3

    c2

    a1

    a2

    a2

    a1

    b4

    a3

    b3

    a5

    a4

    a7

    a6

    a1

    a2

    a2

    a1

    a8

    a9

    a7

    a6

    a8

    a9


    Slide67 l.jpg

    Directory Manipulation Language

    • MERGE “b2”AND“C” TO“New” UNDER “MyCo”

     Merge([“b2”, “C”], “New”, “MyCo”) :-

    link_to(“New”, “MyCo”),

    link_to(MergedlList, “New”).

    Logic

    MyCo

    MyCo

    Merge

    a3

    C

    a3

    a4

    New

    a4

    b2

    c1

    c2

    b4

    a1

    a2

    b3

    a1

    a2

    a1

    a1

    c1

    c2

    b4

    b3

    a7

    a6

    a7

    a6

    a8

    a8

    a9

    a9


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    Search toward more Balanced Directory

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