Knowledge Genesis Group & Smart Solutions

Knowledge Genesis Group & Smart Solutions Petr Skobelev Multi-Agent Technology: Ideas, Experiments and Industrial Applications Small, but coordinated forces, produce magic. Prof. A. Konovalov. Lectures on supramolecular chemistry Ekaterinburg, 12-13May 2011

Introduction Key Challenges of Real Time Economy Multi-Agent Technology First Experiments with Multi-Agent Solutions Industrial Applications in Real Time Scheduling Future Agenda

Knowledge Genesis Group • Started 1997, Samara, Russia • Originally from Russian Academy of Science and Aerospace Industry • 15+ years of experience in Multi-agent systems and Semantic web • Expertise in application development, large-scale systems, web-applications, GPS navigation and e-maps, data bases, mobile solutions • 100+ J2EE and .net programmers • Knowledge Genesis Group companies: • Magenta Technology (UK) - 2000 • Knowledge Genesis Germany – 2008 • Knowledge Genesis UK – 2009 • Emergent Intelligence, USA – 2010 • Smart Solutions, Russia– 2010 • Advanced technology & product vision for solving complex problems • Own development platform • International network of partners • Strong links with universities

In Samara Office of Magenta Technology (UK) 15 June 1990 – The beginning … Prof. George Rzevski (Open University, UK) and Prof Vladimir Vittikh (Institute of Complex Systems of Russian Academy of Science) Company Growth (Number of Employees)

Key Challenges of Real Time Economy Uncertainty, Complexity & Dynamics of business are growing Clients, partners & resources demand more individual approach High efficiency of business requires to become more open, flexible and fast in decision making Solutions for Real Time Decision Making can help to optimize resources, balance and reduce cost & time, service level, risks and penalties Activity-Based Cost (ABC) model is required to analyze options and provide dynamic pricing in real time Pro-actively negotiate with clients and resources “on the fly” Solutions need to support not only optimization of resources but also provide opportunities for business growth, learning and adaptation Use full power of Internet services, GPS navigation, mobile phones, RFID, etc New generation of software solutions for smart decision making support and sophisticated user interaction is required on the market!

Hierarchy of programs Sequential Processing Top-down instructions Centralized Data-driven Predictable Stable Reduce Complexity Full Control Multi-Agent Technology Differentiation Traditional Systems Multi-Agent Systems • Networks of agents • Parallel Processing • Negotiations & Trade-Offs • Distributed • Knowledge-Driven • Self-Organization • Evolution • Thrive with Complexity • Managing growth Modules are working as a co-routines simultaneously

Distributed Approach Wins!

Started in the beginning of 1970’s … Based on achievements in Artificial Intelligence + Object-Oriented and Parallel Programming + Telecommunications Traditionally focused on logic reasoning (Wooldridge, etc) Our approach is bio-inspired (Van Brussel, Paulo Letao, etc) but strongly influenced by: Ilya Prigozhin in Physics (auto-catalytic reactions), Marvin Minsky in Psychology (society of mind), Artur Kestler in Biology (holonic systems) Key focus: self-organisation and evolution, synergy, non-linear thermodynamics, collective (emergent) intelligence First Applications: Internet e-commerce Current Applications: logistics, data mining, text understanding, etc Future: Web-Intelligence The Beginning of Multi-Agent Systems

Classification of Agents Agent Type Simple Agents Smart Agents Intelligent Agents Truly Intelligent Agents Autonomous execution a Communication with other agents and users a a Monitoring of environment a a Ability to use symbols a a Problem Domain Knowledge a Goals and Behavior a Adaptive Learning from Environment a a Tolerant Reaction to Input Errors a Errors Processing a Real Time a Natural language a Current Focus

A new agent is created at runtime whenever there is a task to be performed The agent begins its life by analysing the task and studying rules of engagement Agent activities include: analysing situation composing messages receiving & sending messages to other agents or humans interpreting received messages deciding how to react acting upon their decisions This enables agents to run concurrently When an agent completes its task it is destroyed How Agents work?

Winestein Technologies– http://www.weinstein.com NuTech – http://www.nutech.com Living Systems –http://www.livingsystems.com AgentBuilder - http:// www.agentbuilder.com Quarterdeck - http:// arachnid.qdeck.com GeneralMagic - http://www.genmagic.com Intelligent Reasoning System - http://members.home.net:80/marcush/IRS BiosGroup – http: www.eurobios.com LostWax – http://www.lostwax.com About 30 companies on the market. More than 100 University projects are known. Examples of Multi-Agent Systems

Single-Agent Approach – no self-organization Based on results of traditional AI research (Prolog-style deductive machine for reasoning) – not effective for dynamical environments with high uncertainty Concept of Mobile Agents: problems with security Traditionally Oriented on e-Commerce Do not have Knowledge Base and Reasoning Tools to support Decision Making Processes of End-Users Do not have Re-Negotiations support Memory intensive and slow – low performance, only a few Agents can work on server in parallel Not supported with development tools (basic platforms only) - very expensive and difficult to design & develop Existing Multi-Agent Systems

Our Multi-Agent Systems working in Swarms consisting of a large number of small autonomous programs (objects) called Smart Agents Smart Agents have special in-built tools for decision making and ontology-based scene support Main feature of Smart Agents is the ability to solve complex problems through negotiations Every complex problem can be solved by self-organization and evolution, in competition and cooperation of Smart Agents Examples: real time logistics, pattern recognition, text understanding, data mining, etc Our Approach: Main Ideas

Demand and Supply Matching on Virtual Market Engine - is Core Part of Real Time Multi-Agent Solutions for Any Type of Complex Problems Swarm: Demand and Supply Networks Virtual Market Engine S S Demand-Supply Match D D S S MatchContract D S S D D D S Demand Agent SupplyAgent S S D S D D S D

Software Agents model physical objects, people and abstract concepts forming Virtual Markets in which they allocate supply to demands The agent interaction is based on the free-market model – Demand Agents purchase resources from Supply Agents and Supply Agents sell resources to Demand Agents, all working concurrently Logistics: orders to resources Text understanding: words to meanings Data mining: records to clusters Agents learn how to accomplish their tasks by accessing Ontology where they consult the detailed knowledge of the domain in which they work Our Approach: Main Ideas

Ontology by definition is “knowledge as it is” or “conceptualisation of abstraction” (Gruber) Knowledge can be represented by semantic network of concepts and relations OntologyEditors: OntoEdit, WebODE, WebOnto, Protégé-2000, OWL/RDF/RDQL Our ontologies are used for pecification of situations (scenes) Ontologies are the combination of declarative semantic network and operational knowledge (scripts) Concepts and relations can represent objects, roles, properties, processes, attributes, etc. Some ontologies can be hard-coded to improve performance Ontologies

Example of Ontology /Scene

Multi-Agent Platform • Adaptive, Real time and Event-driven • Swarm-based approach (vs mobile agents) • Virtual Market as a Core engine • Highly Reactive & Pro-Active • Provide Emergent Intelligence • Based on Semantic web innovations • Ontology to capture Enterprise Knowledge and keep it separately from source code • Decision Making Logic instead of rules • Able to Learn in Future (Using Pattern Discovery module) - source codes separated from knowledge MAS driven by Inner virtual market Knowledge BasedDecision Making • Java-based / .net • Peer-to-peer architecture • Scalable/Robust • Strong visualizations • Desk-Top & Web-Interface EnterprisePlatform

Smart Clash Analysis for Airbus Wings • When change happen (let’s assume that in our example it is size of part C) we create agent of changed part • This agent will investigate scene and find his neighbors (Part B) • Agent of part C will create agent of part B and to inform him on changes and his new boundaries • Agent of Part B will compare new size or position of Part C and will check his boundaries according with the type of relation • If these changes not affect his position – it will be recognized as the end of wave of changes. • If yes – the situation will be repeated for other neighbors of the network in the same way (Part A, Part E, Part D) • As a result of this process it will be ripple-effect from initial change which will take place until it affects positions of other parts Semantic network: scene of wing Part E Is-Assembly Part A Part D Fix-Link Fix-Link Agent of Part C Part B Can-Rotate 1, 2 Part C • Value for Client • Analysis can be made in real time (and even for dynamically reconfiguring complex objects) • The approach proposed can be applied for any complex object or machine without full re-programming (need another ontology and scene mainly and change of interpretation of links) • Many threads of activities can go in quasi-parallel mode starting from any point (changed part) of network when needed and even in parallel for engineers 3 4 Ripple effect of Part C changes Agent of Part B

Noble Group Solution: Smart Coal Mining in Indonesia • When change happen (let’s assume that in our example it is bad Weather in Region A: heavy rain!) we create agent of region and send message about weather event. • This agent will use ontology for find out the consequences. Usually bad weather affects Jetty Loading Rate and Waterway availability. Then agent finds all affected instances of jetties and waterways in this region and inform them about bad weather. • Agents of all these objects will estimate impact and make changes in their schedules. This can leads to new changes in a network. For example due to the new jetty schedule some barge will be late on anchorage. Agent will inform his operator immediately and will present current options, for example barge will come 3 days late. • If this decision will be confirmed by operation, Agent of Barge will create agent of Vessel and FC and will inform them about delay, and they will check options and inform their operators if needed. • If these changes will not be possible to solve inside region and they will affect client – it will be needed to inform clients and it will be the end of wave of changes. • As a result of this process it will be ripple-effect from initial change which will take place until it affects positions of other parts Agent of Weather in Region A Semantic network: scene of Noble Group network 1 Region A Is-Contain 2 Client 4 Jetty 1 Booked Contract Barge 2 3 4 Booked Booked Vessel 3 Crane 4 • Value for Client • Team of managers can be coordinated in real time according with events coming • The approach proposed can be applied for any team coordination without full re-programming (need another ontology and scene mainly and change of interpretation of links) • Flexibility: many threads of activities can go in quasi-parallel mode starting from any point (changed part) of network when needed and even in parallel for users Ripple effect of changes

Jarvis Solution: Smart Pattern Recognition Agent of House • Input image flow comes as binary digital photos taken on new landscapes with different configuration of patterns and high level of noise. • All agents of patterns start their work in parallel and compete because it is not known in advance where strong patterns will be recognized. • Looking into ontology all agents trying to make their best match with image fragments (and all of them can invoke some specific methods for this). • If for one of patterns matching is Ok then he adds object into scene specifying parameters of recognized pattern (lake, forest, etc) and links it with other objects. • If matching is not Ok (for example agents of house and cloud have conflict and are competing for the same fragment of image in brackets) – they need help and switch for cooperation based on domain semantics. • For this example agent of house will look in ontology and find out that usually there are garage and road near houses. Now he can investigate scene and will see that garage and road are already there. • Then probability of the fact that it is house (not a cloud) becomes higher because of this links (sometimes it is needed that garage and road will agree also that their neighbor looks like a house). • In this situation cloud also can know from ontology that it can move and will give priority to the house. Agent of Cloud Agent of Lake Agent of Garage Agent of Forest Agent of Road • Value for Client • Analysis can be made in real time or batch image processing • The approach proposed can be applied for any complex image processing system for pattern recognition without full re-programming (need another ontology and scene mainly and low level methods of image processing) • Flexibility of solution: many threads of activities can go in quasi-parallel mode starting from best recognized parts of image (unknown in advance) • Quality of pattern recognition can be very high because of semantic links and errors checking during the process of recognition • Proposed solution is very generic not only for image processing but also for text understanding and other applications (patterns of sense can compete for strings of texts, etc) Semantic network: partially reconstructed scene during patterns recognition House Placed-Near Between Garage Lake Road

Fax Number Fax Number From … From … To … To … Name Name List of Items List of Items List of Items List of Items Price Price OmPrompt Solution: Smart Fax Recognition • This task has the same solution as for images considered above. • When new fax is coming agent of first pattern according with fax template starts looking his part of image. If he finds 100% matching – he writes results in scene and initiates next agent looking into scene of fax template. • But if matching is not 100% a few agents of this area can compete for the same part of the image (for example Osipemagen – it is wrong end of one field and wrong beginning of the new field). • Agent of first field will recognized that it is beginning of the address and will ask agent of the next field – do you recognize rest of the string as a company name connected with this address? In general the best one will try to get support from other with whom he can cooperate investigating his local area via relations. • Recognized part of image is saved in scene and is used by all other agents to detect next parts and find solution of conflicts. Real fax Fax Number Is-Header on the top of page Next in line From… To … Below Below Name Semantic network: partially reconstructed scene of fax recognition • Value for Client • Analysis can be made in real time • The approach proposed can be applied for any complex fax or image processing without full re-programming (need another ontology and scene mainly and change of interpretation of links) • Flexibility: many threads of activities can go in quasi-parallel mode starting from any point (changed part) of network when needed and even in parallel • Quality of fax (image) recognition can be very high because of semantic errors checking during the process of recognition Fax template

Agent of Pork Agent of Dinner Customer Agent of Breakfast Agent of Lunch VineWorld Solution: Smart Diet Management • When new event happen (let’s assume that in our example it is user request to replace Fish by Pork at dinner time) we create agent of changed object • This agent of Pork will replace Fish informing other agents in dinner group and agent of dinner. • Immediately agent of white wine (good with fish) will leave the dinner and agent of red wine will propose Dinner agent to enter the menu as a good match with user preferences. • Agent of Dinner will calculate calories and find out that now it is more than 2000 calories for a day. • To solve the conflict agent of Dinner will try to find candidates to reduce number of calories calculating the difference. • If it is not possible to solve the conflict inside dinner – it will ask agent of Tuesday menu – who else can be involved in this process. Maybe both other groups (lunch and breakfast) will be recommended to start looking variants in parallel. • All potential candidates will be asked to find nearest possible food option according with user preference and less calories. • All options will be not simply sorted and presented to user for final decision – but will compete to be recognized as a best option. Best possible option (remove ice-cream) can also switch to cooperation with other options to get more points. • As a result of this process a few food items can drop out of menu, or size of portion will be reduced or physical exercises will be added to menu to reduce extra calories. • In all cases it will be ripple-effect from initial change which will take place until decision is found or not Semantic network: Scene of Tuesday menu Apple juice – 177 kcal x Omelet – 261 kcal Breakfast <empty> Soup – 205 kcal Lunch Pudding – 362 kcal ! 50% Ice cream – 450 kcal Strawberry – 41 kcal Pork – 537 kcal River fish – 216 kcal Dinner Red Wine – 180 kcal White Wine – 192 kcal Total – 2225 kcal Total – 1904 kcal Total – 1988 kcal Ice cream 50% Change Wine Refuse omelet Agent of Menu ! • Value for Client • Solution can be find in real time (and even during update of food items types) • Solution is open for adding new types of services: health, exercises, fridge, etc • Solution is flexible: changes can start from any point and run in parallel threads of activities Bicycle

Smart Content: Semantic Network of Celebrities

Upload and specify new photos

Ontology of Celebrities

Ontology/Scene Editor

Add new photo and agents will change network

New Photo is added to Semantic Network

Text Understanding Projects • Intelligent Documents Classifier (Rubus/Aon) • Classification of all documents into groups with the similar sense - semantic proximity • Ability to build the template document on the base of the group of similar documents • Intelligent Requests System (Integrated Genomics) • Intelligent search and comparison of the abstracts’ semantic descriptors on the basis of the problem domain ontology • Database Natural Language Requests System (Hotel Booking) • Intelligent partial matching on the base of the ontology to make complex search of several interconnected items • On-line clustering analysis of customers types and their patterns of requests thus generating new rules to enlarge the ontology

MEDLINE Database - Internet search for molecular biology abstracts The MedLine database contains brief abstracts of articles on biological themes, which are presented to users free of any charge. Search conditions - keywords and logical expressions If the abstract of a found article is satisfies the user, he can order the full version of the article for a certain price.

Text Understanding Process Syntax stage Example phrase: MagentA will provide support for Software Programs employed by the Client. Morphology stage Semantics stage

Text Understanding System 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.

Text Understanding Systems • Results • In “good” groups in general accuracy of finding correct article is higher than 81%, in certain requests it’s almost 90% • In “bad” groups the probability of still good article put there by mistake is less than 8% Intelligent Requests System statistics: Time to build one semantic descriptor ~ 1-2 min. Time to search through 1000 abstracts ~ 1 min. Ontology of problem domain contains ~150 concepts and ~3100 relations (with inheritance)

Text Understanding System • Comparison with keywords • The proposed approach demonstrated significant quality increase comparing to keywords • Keyword search even with all improvements (synonyms etc) still demonstrates rather bad results, clearly insignificant to the required task • Accuracy of proposed search higher than simple keyword search Intelligent Requests System statistics: Time to build one semantic descriptor ~ 1-2 min. Time to search through 1000 abstracts ~ 1 min. Ontology of problem domain contains ~150 basic concepts and relations

Multi-Agent Solutions for Real Time Resource Allocation, Scheduling and Optimization Your solution & application?

MAT Solutions for Real Time Logistics Truck Scheduling Ocean Scheduling Taxi Scheduling Courier Scheduling Car Rental Optimization Factory Scheduling Airport Scheduling Work forces ...

How It Works in Transportation Networks VOL: 10 PALLETS SLA: 5 DAYS VOL: 10 PALLETS SLA: 10 DAYS VOL: 5 PALLETS SLA: 2 DAYS 20% 20% 60% VOL: 10 PALLETS SLA: 10 DAYS VOL: 5 PALLETS SLA: 8 DAYS 60% 20% 120% 80% 20% 60% It is important to be able to assess alternate routes, to meet services levels and minimum cost. 100% 60% 40% 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. This order has a shortest journey route… …but the capacity is not available on one of the legs.

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, etc) Activity Based Cost Model Other client-specific requirements Most of large & complex transport networks are still scheduled manually!

Vision of MAT Scheduling Solutions Current Situation and Ongoing Plan Advise on How-To make Network More Efficient Pattern Discovery Evolutional Design Patterns and Ongoing Forecast Resulting Plan and KPIs Adaptive Scheduler Input Events Flow (New order, Resource unavailable, etc) Network Configuration & Situation specs (Scene) Domain Ontology Ontology Editor Simulator Network Designer Network Assets & Real Situation Domain Knowledge Modeling Plan and KPIs Modeling Data (Flow of orders, fleet size, etc)

MAT Schedulers: Screens Example

Ontology as a Way to Capture Domain Knowledge Describe your classes of concepts and relations

Examples of Concepts and Relations Ontology concepts: • Client • Order • Cargo • TI • TIConsolidation • Fleet • Trailer • DD Trailer • Standard • Truck: • Tractor • Rigid • Dock • Trip • Location: • Cross Dock • RDC • TI Operations: • Collect • Drop • Truck operation: • Stop • Move • Idle Ontology Relations: • ClientHasOrder • OrderHasCargo • OrderHasTI • FleetHasTruck • FleetHasTrailer • TruckHasSchedule • TIConsolidationHasTI • JourneyHasTI • TIHasTISchedule • TIHasTIOperation

Truck Logistics Scene Example Scene objects: • 27 clients • 154 cargoes • Fleet: • 22 DD Trailer • 12 Rigid Truck • 72 locations: • MANCH • MILTO • EXEBOTFR • CHIPP • CONIC • YORFI • PENRITFR • … Create a situation (scene)

Logic of Multi-Agent Scheduling New order Order 1 Order 2 Order 3 Can you transport me? • I can take New order if I: • Shift Order 3 to the left • Shift New order to the right • Drop Order 3 Which Truck looks like the best for me? Can you shift to the left? • Existing schedule • New Order arrives • Pre-matching • New order ‘wakes up’ Truck 3 agent and starts talking to him • Truck 3 evaluates the options to take New order • Truck 3 ‘wakes up’ Order 3 agent and asks it to shift • 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 New order • Agents of New Order and Truck 3 disappear • Order 3 starts looking for a new allocation and finally allocates on Truck 1 by shifting Order 1 08:00 12.00 16:00 20:00 No Time Truck 1 My time window is too tight – I cannot shift Truck 2 Can you shift to the right? Truck 3 Back Next

A B C Z X Logic of Multi-Agent Routing Cross dock 1 Cross dock 2 Y Consider business-network of a company 1.Order1goes from Location C to Location Z 2.Order2 goes from Location Bto Location X 3.Order3 appears, which goes from Location Ato Location Z 4.Order3 decides to go to B and then travel with Order 2 via cross-dock1 5.Order4 appears, which goes from Location A to Location 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 Back Next

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 Problems to be Solved: Location availability windows Backhaul Consolidation Vehicle capacity Constraint stressing Planning in continuous mode Dynamic routing Cross-docking Handling driver shifts Key Problem:Real-time planning in a highly complex network with X-Docks and Dynamical Routing

Case Study: UK Market leader in supply chain management Network Characteristics: Employs around 5,000 staff, rising to 7,000 during Christmas peak Has 40 operating sites Manages 300,000 sq m of warehouse space Has sites across Europe Has a turnover of £400 million Moves in excess of £10bn worth of merchandise each year Services over 3,000 retail outlets around the globe Travels 75m miles each year Operates a fleet in excess of 1,300 vehicles Has over 35 years of supply chain experience Problems to be solved: Maximise utilisation of capacity – minimise need for ad hoc journeys Comply with constraints – temperature regimes, collection and delivery times, customer priority, product compatibilities, product weight, etc Optimise trunking through best use of changeovers and cross docks Do not over split orders to prevent problems on reconsolidation Make best use of subcontractors versus own fleet Make best use of store returning vehicles Key Problems: Automatic Search for effective scheduling decisions using own fleet Adaptively distribute orders among the journeys of static schedule

Knowledge Genesis Group & Smart Solutions