Multi-Agent Technologies for Complex Problem Solving Dr. Petr Skobelev SEC «Knowledge Genesis» ( Samara, Russia ) Founder and Chairman of Board of Directors

Multi-Agent Technologies for Complex Problem

Solving

Dr. Petr Skobelev

SEC «Knowledge Genesis» (Samara, Russia)Founder and Chairman of Board of Directors

http://[email protected]

SEC «Magenta Technology» (London, UK)Со-Founder and member of Board of Directorshttp://www.magenta-technology.com

[email protected]

«Knowledge Genesis»Software Engineering Company

Agenda

• Short acquaintance with the Samara region and the company

•Why multi-agent technologies?

•Examples of successful projects based on multi-agent technologies

Samara Region е-government

Adaptive real-time schedulers

Text Understanding

Data mining

•Conclusion

Samara, Russia

Region of 3.3 million people Located on the bank of Volga river City of 1.2 million people Second capital of USSR during WWII National airspace industry centre High Tech Defence industry centre Centre of National logistic Network

Railways, air and automobile hub Educational centre for Volga region

13 Higher Education Institutions 72 Academic Institutions

Traditions and high prestige of engineering professions

Mature and highly developed IT- market

Innovations actively supported by the Samara Regional Administration

SEC «Knowledge Genesis» (Russia)

• Established in 1997 in Samara

• Originally from airspace industry and Russian Academy of Sciences

• Unique competences in Multi-agent systems and Semantic web

• Advanced business & technology vision for solving complex problems

• Innovative technologies for distributed decision making support

• More than 100 J2EE and .net programmers and engineers

• Expertise in large-scale systems, web-applications, data bases, etс.

• Affiliated company – Magenta Technology (UK) – 2000

• Own development platform

• International Network of Partners

• Strong connections with Universities and Research Institutes

• Flexibility and individual approach to each Customer

About the Company

Founded in 2000 together with EU Investment Funds

Solve complex real-world problems using Multi-Agent Systems

Main directions of work: Systems for scheduling of oil

tankers, trucks, taxis, factories, etc

Internet marketing and advertising

Operational platforms design Reducing costs & increasing

customer business performance

Clients in USA & UK Headquartered in London Development Centre in Samara

Strong connections with Universities in Russia, UK and USA

Development center in Samara, Russia

Why Multi-Agent Technology?

One of the new critical software technologies

Capable of applying Fundamental Principles of Self-Organization and Evolution

Provide smart, flexible and pro-active software solutions Based on negotiations, conflicts solving and finding trade-offs “Crack” previously unsolvable problems Address limitations in existing technology solutions Allow representing real-world objects and processes Solve problems the way people do

Developments, Products & Technologies

• e-Government Systems for Welfare and Healthcare

• Real time GPS-based AdaptiveSchedulers

• Enterprise Decision Making Support Systems

• Internet Portals

• Web-based Data Miningand Text Understanding Systems

• Multimedia, 3D-Graphics and Animation

• Geographic information systems

• e-Learning Systems

Multi-Agent e-Government system for Social sphere of the Samara region

Designed for providing targeted state services based on social cards of citizens

Multi-Agent e-Government system for Social sphere of the Samara region

• Provides targeted state services• Based on social passports and smart cards of citizens• Knowledge Base contains more than 500 social laws (federal, regional and municipal): rules applicable to citizen data• Personalized agent attached to each citizen• Available via the Internet and “Internet-Kiosks”

Knowledge Bases of Social Legislation

Multi-agent e-government system for social sphere of the Samara region is based on Knowledge bases of social legislation (in the form of semantic networks) containing:

Integrated knowledge bases of federal, regional and municipal laws;

Regulations for state services provision.

DatabaseKnowledgeBase

Benefit

Category

Law

Organization

address

Rules Source of

financing

Human№ Name Year Post Address

1 Ivanov Ivan Ivanovich

1934 Samara, Sadovaya Street

34-7

Databases vs Knowledge Bases

Databases

• Rigid database scheme, new attributes require new programming•Data organized as a sequential indexed arrays•Database elements are data only•Queries are pre-defined and programmed in advance• Effective storage for simple homogeneous sets of data only (for example, years of birth, post addresses)

• Extensible «glossary of terms» for description of new laws and citizen characteristics• Data represented as a semantic network•Concepts/relationships and rules can be included into network•Queries should discover facts and can be carried out using complex logical reasoning• Effective storage for diverse data on citizens (social, medical and other information)

Knowledge bases

Databases vs Knowledge Bases

Databases

Social Insurance

Databases

Healthcare and Social Support

Databases

Pensions

Personal data are distributed

Social Cards and their extensions

A Social card is a way of providing services to each citizen on individual basis

Main features:

1. Identification of Citizen 4. Public Transport discounts

2. Social Benefits 5. Loyalty programs

3. Healthcare 6. Payments

Samara region: The results of the First stage of Deployment

• 37 towns & villages

•260 Internet kiosks

• Knowledge Base contains 534 laws and regulation acts

- 278 Federal

- 164 Regional

- 92 Municipal

• Works for social care, healthcare, electricity and water supply, education and other social domains

• Social benefits for veterans, disabled people and many other categories of citizen

• 120000 social cards

• 50 Social Manager workstations

• 37 Knowledge Engineer workstations

• 6 Chief Executive Authority workstations

Multi-Agent SchedulingMulti-Agent Scheduling

• Java-based• J2EE architecture• Scalable/Robust• Strong visualizations• Desk-Top & Web-Interface

Ontologies

EnterprisePlatform

Multi-Agent

Technologies

• Based on Semantic web technology• Ontology to capture Enterprise

Knowledge and keep it separately from source code

• Decision Making Logic based on Ontology

• Able to Learn (Using Pattern Discovery module)

• Swarm-based approach (vs mobile agents)

• Supports Complex networks• Influenced by real market mechanisms• Adaptive, Real time and Event-driven• Agents are Pro-Active• Provide Emergent Intelligence

Technological Platform Technological Platform

Demand and Supply Matching(orders and resources in logistics, words and

semantics in text understanding, data and clusters in Clustering)

Virtual Market

D S

D S

D S

D S

S

S

S

D

S

S

D

D

S

D

D

D S

Demand-Supply Match

Demand Agent

SupplyAgent

MatchContract

MAT Solutions based on Virtual Market of Demands and Resources

MAT Solutions for Real Time Logistics

Designed for resource scheduling in real-time mode, supply chains optimization, business performance enhancement

MAT Solutions for Real Time Logistics

Truck Scheduling Ocean tankers Scheduling Taxi Scheduling Courier Scheduling Car Rental Optimization Factory Scheduling Supply Chain Optimization

VOL: 10 PALLETSSLA: 10 DAYS

40%


80%


60%

20%20%

20%


60%

20%


120%60%

60%

100%

This order has a shortest journey route…

…but the capacity is not available on one of the legs.

This order has a shortest journey route…

…but the capacity is not available on one of the legs.

It is important to be able to assess alternate routes, to meet services levels and

minimum cost.

It is important to be able to assess alternate routes, to meet services levels and

minimum cost.

Imagine the power of having a single system that can

automatically plan and re-plan a network like this, as events

occur, such as new orders being added or resource availability

changes.

Imagine the power of having a single system that can

automatically plan and re-plan a network like this, as events

occur, such as new orders being added or resource availability

changes.

Example: European transportation Network

Transport Logistics Network Complexity Real-time scheduling with shrinking time windows Large & complex networks (> 1000 orders per day, > 100

locations, > 50 vessels ) Less-than-Truck loads requiring effective consolidation Need to find backhaul opportunities Intensive use of crossdocking operations Trailer swaps Numerous constraints on products, locations, dock doors,

vehicles: types, availability, compatibility Individual Service Level agreements with major clients Own and third-party fleet Fixed and flexible schedules Dependent schedules (trailers, drivers, dock doors) Real time economy Activity Based Cost Model, etc

Most of large & complex networks are still scheduled manually!

Pattern Discovery

Resulting Plan and KPIAdaptive

SchedulerEvents Flow

Network DesignerOntology

EditorSimulator

Ontology

Network (Scene)

Modeling Data

Patterns and Ongoing

Forecast

Current Situation and Ongoing Plan

Modeling Plan and KPI Domain Knowledge

Evolutional Design

Re-Design of Network

Architecture of Multi-Agent Platform

Multi-agent Scheduler: Screen Example

Truck 1

08:00 16:0012.00 20:00

Time

Заказ 1

Order 2

Order 3

•Consider a schedule

•New order arrives

•Preview

•New order ‘wakes up’ Truck 3 agent and starts negotiations with him

•Truck 3 evaluates the options to take New order

•Truck 3 ‘wakes up’ Order 3 agent and asks it to shift to the left

•Order 3 analyzes the proposal and rejects it

•Truck 3 asks New order if it can shift to the right

•Truck 3 decides to drop Order 3 and take a New order

•Order 3 starts looking for a new allocation and finally allocates on Truck 1 by shifting Order 1

Truck 2

Truck 3

New order

Which truck is best for me?

I can take new order if I:

•Shift Order 3 to the left

•Shift New order to the right

•Drop Order 3

Will you take me?

Can you shift to the left?

I can’t shift

Can you shift to the right?

No

Logic of Multi-Agent Scheduling

A

Consider logistic network of a company

1.Order1 goes from Point C to Point Z

2.Order2 goes from Point B to Point X

3. Заказ3 appears, and goes from Point A to Point Z

4.Order3 decides to go to B and then travel with Order 2 via cross-dock1

5.Order4 appears and goes from Point A to Point Y

6.Order3 decides to travel the first leg with Order 4 and the second leg with Order 1 via cross-dock 2, to avoid going alone from A to B

Cross dock 2

Cross dock 1

B

C

Z

Y

X

Logic of Multi-Agent Routing

Case Study: UK Logistics Operator

Network Characteristics: 4500 orders per day Order profile with high complexity

Many consolidations should be found Few Full Truck Load orders Few orders can be given away to TPC Majority of orders require complex planning –

the price of a mistake is high 600 locations Large number of small orders 3 cross docks 9 trailer swap locations 140 own fleet trucks, various types 20 third party carriers

Carrier availability time Different pricing schemes

Key Problem: Real-time planning in a highly complex network with X-Docks and Dynamical Routing

Problems to be Solved:

Location availability windowsBackhaul ConsolidationVehicle capacityConstraint stressingPlanning in continuous modeDynamic routingCross-dockingHandling driver shifts

Summary of Benefits (Before / After)

BEFORE IMPLEMENTATION AFTER IMPLEMENTATION

Two operators worked for a dayto make a schedule for 200 instructions

Planning day 1 for day 3: no chance to Support backhauls and consolidations in real time

8 minutes to schedule 200 transportationinstructions

Planning day 1 for day 2 and even day 1 for day 1

No software for schedule 4000 ordersWith X-Docks and Drivers (manual procedure only)

Hard to consider various criteria quickly and choose the best possible option

4 hours to plan orders 4000 orders via X-Docks and ability to add new orders incrementally (a few seconds for a order)

Choosing the best route from the point of view of consolidation or other criteria

Knowledge was hard to share, it was “spread” among different experts

Capture best practice and domain knowledge in ontology. New knowledge can be inserted quickly.

Key Customers

Avis (UK): Leading car rental provider Innovative dynamic scheduling system for downtown market

reducing car assets required and improving service levels Addison Lee (UK): largest private hire car firm in London

delivering core operational systems and dynamic scheduling Tankers International (UK): Manage a large oil tanker

fleet development of dynamic scheduling software for shipping

fleet One Network (USA): logistics software provider

providing development services to implement new core, scheduling and visual features/components for their platform

GIST (UK): supply chain specialist real-time scheduling software tool for increased fleet

utilisation and reduced transportation costs Enfora (USA) : major manufacturer of handheld devices

development of a wide range of software modules and market partnership for a dynamic scheduling web service

Move forward with Multi-Agent Systems

That Was Then This is Now

Batch

Optimizers

Rules Engines

Constraints

Real-time

Manage Trade-offs

Decision-Making Logic

Cost/value equation

Visualize Learn, Simulate and Forecast

Adaptive Factory SchedulerMain features include:

Creation of production plans; Planning of production equipment,

operations, resources based on ontology; Adaptive rescheduling in response to

unexpected events (equipment failures, operation delays, etc.);

Visualization of current production plan; Description and updating system

knowledge through the ontology; Semiautomatic editing of production

plans. For example, a user may change the initial plan for any machine or equipment or add new production tasks, change or cancel some of previous tasks and operations, etc.

Results of scheduling are presented in Gantt chart form showing the level and the intensity of resources utilization in the course of production plan fulfillment

User plans production processes by assigning resources for their fulfillment (machines, equipment etc.)

Adaptive Factory Scheduler

Data («knowledge») about resources are entered and stored in ontology.

Adaptive Factory Scheduler allows operating with ontology data, updating, modifying and deleting them…

…and visualize factory ontology with adjustable detailing level


Factory ontology example Factory ontology example

In order to manufacture a driving mirror it is necessary to make a

form, to cut glass, to paste glass to a substrate, etc.

For this purpose we need the following materials: a plate, glass,

substrate, glue, and other raw materials.

Each operation should be carried out by skilled worker…

• Actively developing in Semantic Web for Internet pages semantic description

• Factory Ontology contains description of basic domain objects and relationships between them.

• Ontology allows to represent knowledges of certain domain separately from program code

• Ontologies usage allows to build flexible and scalable applications easily adopted to any business by means of changing «system knowledge» by demand.

• Ontologies can be successfully applied for decision support, learning, knowledge integration and other areas.

Achieved results:

Production planning on the basis of real resources characteristics (equipment, machines, workers), their availability at various time periods and information about changes

Combination of the planning stage with plan execution monitoring, flexible rescheduling

Monitoring of technology and production plans More efficient strategic and tactical planning in response to maximum

requirements in the condition of uncertainty, resources distribution conflicts and high risks

Enhanced visualization capabilities (Gantt charts, semantic networks) Higher adaptability and configuration capabilities Execution of orders just in time through flexible planning in the real

time mode


Solves “unsolvable” problems in complex logistic networks Supports event-driven, continuous planning in real time with

intelligent reactions to unexpected events Fast reaction: reactive and pro-active changes of parts of the

schedule without changing the whole schedule Provides smart decision support and sophisticated user interaction

Reacts on events and constantly generates new options proactively Provides individual & detailed cost calculations per order / resource Makes trade-offs to balance different criteria (cost, profits and service

levels) Provides ability to override constraints Supports collaborative team work with users Provides integration of scheduling processes across the company Makes decision making visual

Knowledge-based: Uses domain- and company-specific knowledge to produce feasible schedules and reduce dependency on key individuals

Customizable and configurable Platform for supporting business growth and performance increase Reduces cost & time, improve service, lower risks and penalties Supports «what-if» games for business optimization

Benefits of MAS for Real Time Logistics

KEIS: Intellectual data mining

Designed to discover patterns, hidden dependencies and business-critical knowledge in the databases, texts and other information resources


Traditionally, data analysis is carried out by human. However, human cannot find more than two-three

dependencies even in small data files, and at the same time mathematical statistics operates with averaged parameters and cannot help in practical recommendation preparation.

In contrast with the traditional methods of data analysis, KEIS discovers hidden rules and dependencies automatically.

KEIS is designed for analysis of data extracted from different sources and presented in different formats.

Problems of traditional data analysis


Cluster analysis basics

Clustering is one of the basic approaches used to discover hidden patterns in the huge information files

Cluster analysis allows to find previously unknown dependencies in data. These dependencies are hardly discovered using other approaches.

Clustering divides data into groups (clusters) where elements inside one group have more «similarity» among themselves than with elements in neighbor clusters

Clustering Technology

Data processing

Data transformation to possible input data formats

Data loading…

Discovery of clusters

Cluster 1

Cluster 2

Cluster 3

Cluster4

Файлы формата txt.(Блокнот)

Файлы формата mdb.(Microsoft Access)

Файлы формата xls.(Microsoft Excel)

Cluster analysis

Databases

Stages of KEIS data processing

1. Data loading2. Data processing3. Analysis by attributes4. Cluster analysis5. Cluster content analysis6. Automatic generation of semantic

rules

Basic stages

Stage 1: Data loading

System GUI

Data file opening

On the first stage data loading and pre-processing are executed

Pre-processing

Stage 2: Data processing

Initializing clustering process

On this stage clustering of full data set by selected attributes has to be executed

Information about discovered clusters then is shown in the table

Stage 3: Data analysis by attributes

Detailed research of cluster parameters using categories of selected attribute is carried out on this stage

Select an attribute

Stage 4: Cluster Analysis

All clusters discovered in loaded data are presented in pie chart.

Content of each cluster can be analyzed in details in the system…

…or exported for review and further processing to Microsoft Excel.

This stage is a visualization stage. Segments of pie chart correspond with discovered clusters, and its size allows to evaluate number of records in certain cluster

Stage 5: Analysis of cluster’s content

At this stage, detailed information about all categorial attributes for the selected cluster can be presented.

Each attribute is shown at the diagram using colored area.

Height of this area allows to evaluate total number of records with certain attribute, in selected cluster.

Selected cluster content visualization.

Stage 6: Semantic rules generation

At this stage system allows to formulate correspondences between different attributes in a logical form «if…then».

Selection of logical scheme (conditionconclusion) by the user

Automatically generated rules are shown by the system

generating…If user has a car, then he often travels with:1.Family; 2.Partners; 3.Friends

High performance High reliability of analysis results Flexible cauterization parameters settings Possibility to process big information files contains

hundreds of thousands of records where each record can has hundreds of attributes

Support of different formats of input data (txt/xls/mdb) Possibility of clustering using many parameters Possibility to handle both quantitative and non-

quantitative parameters


Basic advantages of KEIS

KEIS: Case study. Social sphere

Data analysis related to recipients of social support in Kinel town allowed to determine all groups of recipients and their basic characteristics

Discovered clusters

Cluster diagram

KEIS: Case study. Car insurance

Insurance company provides car insurance service and has staff experts who on the basis of several criteria (official requirements and personal expertise) makes decisions of business conditions for certain client. Guessed decisions include providing of insurance or rejecting of service, tariffs, potential legal costs, etc.

Cluster analysis allows «to discover» hidden dependencies between client characteristics and insurance accident risks by special client’s data processing.

Discovered clustersCluster analysis allowed to find out most secure and insecure

segments of clients

KEIS: Case study. Mobile operator

Mobile company database analysis allows to discover main groups of clients and their preferences

Main groups of clients.Different services usage statistics

Cluster №9 corresponds to the largest segment of clients (6141 records – 45%)

Local traffic two times more than average, roaming is tree times less than average, Long distance calls

at average level, additional services at average level. I. е. predominantly local calls.

Most likely, usual local residents

Text Understanding with generation of semantic network

Intellectual text processing and analysis implies understanding its semantics.

Text semantics can be presented in the form of a semantic network (scene) - the information structure reflecting concepts, objects, subjects mentioned in the text, and relations between them.

Domain ontologies are used in order to create scenes.

Instances: • Molecular biology article’s abstracts understanding• Insurance company contracts processing• Semantic information search• Perspective: Semantic-based terrorists SMS or e-mail messages (or even phone calls) recognition

Example: Generation of semantic descriptor for molecular biology article excerpt

Модуль построения семантических дескрипторов ориентирован на анализ реферата и создание на основе онтологии предметной области семантического дескриптора, однозначно описывающего данный реферат. Дескриптор для каждого реферата строится единожды, и далее работа осуществляется со сформированной базой дескрипторов.

Two pUC-derived vectors containing the promoterless xylE gene (encoding catechol 2,3-dioxygenase) of Pseudomonas putida mt-2 were constructed. The t(o) transcriptional terminator of phage lambda was placed downstream from the stop codon of xylE. The new vectors, pXT1 and pXT2, contain xylE and the t(o) terminator within a cloning cassette which can be excised with several endonucleases.



Analysis of the first sentenceTwo pUC-derived vectors containing the promoterless xylE gene (encoding catechol 2,3-dioxygenase) of Pseudomonas putida mt-2 were constructed. The t(o) transcriptional terminator of phage lambda was placed downstream from the stop codon of xylE. The new vectors, pXT1 and pXT2, contain xylE and the t(o) terminator within a cloning cassette which can be excised with several endonucleases.

Analysis of the second sentenceAnalysis of the third sentence

System Architecture

Parents-Children

Goods

…

Small business

SceneMAS for Text Understanding

Domain Ontology

SMS- messages, е-mails, etc.

Scenes Archive

MAS for pattern

detection

Language Options

Patterns Library

MAS for scene clustering

Signal of pattern detection

MAS for language queries

Clusters

Typical QueriesOntology extension requests

Where Vasya waslast week?

Semantic network generated in course of text analysis

Conclusion

1. Knowledge Genesis develops innovative multi-agent systems applicable to complex problems solving in various domains

2. First experience of multi-agent systems development for e-government, adaptive planners, text understanding, clustering demonstrates high efficiency and existence of good perspectives of the approach on world market

3. Currently Knowledge Genesis is working on new generation of the high-performance multi-agent systems functioning on distributed network of servers and allowing learning by experience

4. We will be happy to have new possibilities for further development and application of our technologies in different domains to solve complex problems

THANK YOU!

Russia, Samara, 443001,

Sadovaya street 221

Tel/fax: 007-846-3322101

www.kg.ru

[email protected]

Documents

Multi-Agent Technologies for Complex Problem Solving Dr. Petr Skobelev SEC «Knowledge Genesis» ( Samara, Russia ) Founder and Chairman of Board of Directors