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Industrial applications of AI

The engineering of “intelligent” systems

by

C. Anantaram

TCS Innovations Lab, Delhi

IIT- Bombay AI lecture

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Agenda

Introduction

Part 1: Rule Based Systems

Part 2: Case Based Systems and Pattern based systems

Part 3: Complex Systems

Part 4: New ways to interact with Systems

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Introduction

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The real-world need

• Make application systems capable to handle changes in real-world requirements (business policies, methods etc.)

• Need for application systems to be more Flexible, Configurable and Adaptable

• Provide new abilities in real-world applications such as ability to negotiate, ability to predict, ability to analyze, ability towork with inexact and insufficient data, manage policies, participate in knowledge management, resolve problems…

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• Getting more and more embedded into systems : issues such as performance, integration, usefulness, etc. need to be addressed

• “Intelligent” processing of mundane tasks to “intelligent”processing of complex tasks

• Profile of system user is changing: from IT-professional to IT-trained to IT-uninformed

• View it as a “Force multiplier” for applications

The real-world need

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Authorization Request(CustomerID,Price)

PURCHASE ACCEPT

(Cu

sto

mer

ID,P

rice

)

Au

tho

riza

tio

n

Req

ues

t

PRO

DU

CT

REQ

UES

T

PUR

CH

ASE

AC

CEP

T

PRO

DU

CT

DEL

IVER

Y

Mobile Service Provider

Merchant

Rule Engine

Mo

difi

ed

Au

tho

riza

tio

n

Req

ues

t

Auth.Confirmed

Example 1: m-Commerce

Merchant specified Rules

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Example 2: Directory Assistance

System to assist Telephone call operator

SYSTEM

Telephone number of Dr. Manish Tiwari Ahm Ngr

Manish TiwaryFlat # 2B, Savvy Estate Ahim Nagar

Phone - 3443422, mobile - 9873922323

95%

Maneesh Tiwary23 B, Tenali Colony, Ahim Nagar

Phone - 346982

90%

Manish TiwariVilla - 101, New Colony, 12 M B Road

Phone - 231232

89%

Monish TrivediFlat - 341, Sanata Building, Nagar RdPhone - 238744, mobile - 981234523

75%

Matched records

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Example 3: Search documents

Retrieve documents related to ‘terrorism’

Level of exactness required -Medium

Case Based ReasoningSystem

...in the absence of an AL-QAIDA and Saddam their attention is already turning to the obvious candidate for demonization. IRAN hawks are rallying for a war against the perceived “dangerous nation” ... 28%

...an act of terrorism may have caused a Russian passenger plane crash during a flight from Siberia ...80%

Work on document collections

Level of exactness / inexactness need be tuned

Statistical technique - work on co-occurrence

relations

Allow updation of knowledge

...the bodies of four international journalists have been found and were identified by colleagues on Tuesday, a day after their convoy was ambushed in a narrow mountain pass on the road to the Afghan capital Kabul... 45%

Does not contain the word

“terrorism”

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Requirements or Needs

• Be able to represent and update knowledge

• Reason on that knowledge with what little information that is available

• Be easy to specify, modify and use

• Not necessarily talk in terms of logic and derivations

• Be integrated with various systems : don’t stand-alone !

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Part 1: Rule based systems

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Rule based representation

General Format for rules

IF <antecedents> THEN <consequent>OR

IF <conditions> THEN <actions>OR

WHEN <event> IF <conditions> DO <action>

Each rule represents a small piece of knowledge that can be combined, or chained together with other rules to infer conclusions or derive solutions to problems.

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Rule based representation

Example 1

IF patient has a runny nose ANDa harsh cough ANDbrownish rash ANDhigh temperature ANDbloodshot eyes ANDconjunctivitis ANDwhite spots ANDintolerance to light

THENpatient has measles.

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Rule based representation

Example 2

IF Account type is Corporate and Money Transferrequired immediately

THENWire Funds Transfer Charge = 1% of Transfer Amount

IF (IncurredClaimRatio > 10% ) AND(IncurredClaimRatio <= 20% )

THENSET discount TO (PremiumAmt OF policy * 0.25) ANDINFORM customer

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Logic

Consists of a set of declarative sentences

Propositional

• Primitive P India is a country

• Compound using connectives ∧ ∨ ¬ →Q → R If there is a flood

then crops are destroyed

Truth value same as ¬Q ∨ R

Predicate• Variables employee(X) → gets_salary(X)• Quantification (∃ x) ( number(x) ∧ prime(x) )

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Rules of inference in Logic

Modus Ponens P , P → Q ⇒ Q

Modus Tollens P → Q , ¬Q ⇒ ¬P

Disjunctive Syllogism¬P , P ∨ Q is TRUE ⇒ Q

Hypothetical Syllogism P → Q , Q → R ⇒ P → R

Prolog

grandfather(X,Y) :- father(X,Z), parent(Z,Y).

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Rules of inference in Logic

Unification: Binding variablesOR finding substitution for variables

Resolution: Resolves two parent clausesto produce a derived clause.

parent(A,B) :- mother(A,B).parent(A,B) :- father(A,B).

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Reasoning

Inductive: From given facts derive facts that are moregeneric about the situation : generalization

Example: There was an accident once again.Three people were injured seriously.

Area is accident prone.Need accident avoidance systems.

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Reasoning

Deductive: From given facts derive facts that are morespecific about the situation: specialization

There must have been blood.There could be broken glass.

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Abductive: Find the best possible explanation for a given hypothesis: find cause

Example: Trains are not running between Pune and Mumbai

Must have rained heavily at Mumbai

Train tracks must be flooded

Reasoning

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Reasoning

Data-driven reasoning (also called Forward-chaining):

Start from set of given facts and known assertions

From the set of rules find the rules that can fire

Derive new assertions from the rules that fire

Re-examine the set of rules

Stop when no further rules can fire OR no new assertionscan be derived.

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Reasoning

Data-driven reasoning

Data: You have 4 days leave and Rs 3000/- to spend

What can you do ?

1. Go to Mahabaleshwar for a vacation2. Throw a party3. Buy the DVDs you wanted4. Buy the books you wanted

Cannot go to Europe for a vacation

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Reasoning

Goal-driven reasoning (also called Backward-chaining):

• Start from a given goal and break into sub-goals

• Solve each sub-goal recursively

• For each sub-goal consider known and derivedassertions

• Stop when there are no further sub-goals to solve

• Solution is the collection of all assertions madeduring the solving processORNone if some sub-goals cannot be solved.

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Reasoning

Goal: Reach Germany by tomorrow evening

Sub-goals: Apply for visa, Get Tickets, Pack

Sub-sub-goals: Get invitation letter, Send to Embassy,…

New fact: All direct flights are full. No seats

New sub-goal: Try to go via Dubai.

Goal-driven reasoning

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Examples of real-world RBS

• Knowledge-based systems:

– Front office Manager’s-aid of Oberoi

– Crew Rostering System of Indian Airlines

– Production scheduling system of Indian Oil

– Urban transportation planning system of UNCHS

• Integrated into commercial systems such as charge calculation, insurance premium renewal, straight-through processing

• Analysis systems, Negotiating agents, Situation assessment systems, Threat assessment Systems

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Classification of RBS

Knowledge-weak, Reasoning-richKnowledge-weak, Reasoning-weak

Knowledge-rich, Reasoning-richKnowledge-rich, Reasoning-weak

Reasoning

Kn

ow

led

ge

Weak Rich

Rich

Battlefield Threat Assessment

Situation Assessment

Jet Engine Fault Diagnosis

Rules for charge calculation

Insurance premium rules

Customer call handling andescalation rules

Banking Risk Management rules / Loan rules

Process Plant preventive maintenance rules

Securities trading rules / m-commerce rules

Stock market fraud detection rules

Data fusion rules

Scheduling rules, planning rules

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Requirements of RBS

Knowledge-weak, Reasoning-richKnowledge-weak, Reasoning-weak

Knowledge-rich, Reasoning-richKnowledge-rich, Reasoning-weak

Reasoning

Kn

ow

led

ge

Weak Rich

Rich

Ability to define complex rules

High-speed processing

Powerful search algorithms

Truth maintenance, Probabilistic reasoning

Very simple rules: any user should be able to set it up easily

Procedural rules : no apparent logic

Application has tight control

Backward-chaining only

Allow complex rule definition

Logical deduction kept under wraps

Backward-chaining primarily; maybe Forward-chaining

Be able to store lots of rules and process at high speed

Fuzzy, temporal, spatial reasoning

Predicate logic, rules on objects

Data-driven, goal-driven, opportunistic reasoning

Pattern-matching, list manipulation, Prolog-kind of power

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Rules in Business applications

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• Businesses have rules that govern business functions

• These refer to policies / procedures (simple or complex)

What are business rules?

Rules for Loan approval

A company's financial position is analyzed and modeled as risk classification process, where having good financial health can be classified in the low risk category, whereas having poor financial health can be classified in the high risk category. Companies falling in low risk category may be sanctioned the loan easily, whilst the loan applications of high risk companies may be rejected. Moderately risky companies can be sanctioned amounts lesser than what they ask for, depending on the lending policy.

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Why are they important?

Constitute an organization’s core business mechanism

Vary in different business environments

Rules change from time-to-time

Authorized users should be able to change rules

Such changes lead to changes in the software application

If business rules are hard-coded, then each change becomes expensive and time-consuming

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APPLICATIONAPPLICATION

Security management

User Interface

Transaction processing

Business Rules

Application Logic

Control Logic

DatabaseCS component

Workflow

Targeted towards

In many traditional applications, the modules are intermixed andmaintenance is cumbersome

Business Rules that change often and need constant updates

Business situations that cannot be frozen up-front

Rules that vary in different business environments

Implementation of rules for decision-support

Need of a rule engine

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• Premium calculation• Policy renewal• Claim processing• Bonus calculation

• Rules of charges for various services• Interest rate rules• Risk Management• Portfolio Management• Securities Trading rules

• Parameter setting and tuning rules• Fault Diagnosis and rectification

Process control

Insurance

Banking

Example domains

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RULE

IF (Customer.BillAmount > 200 ) AND

(Customer.mTransactions > 5 )THEN

SET mPurchase.discount TO 5%

Example rules

RULE

IF Instrument_Type EQ “Share” ORTransaction.currency EQ “INR”

THENDECREASE Tolerance OFTransaction BY 2%

If Billing Amount of customer for cell phone use per month is greater than Rs. 20000, and m-commerce purchases are more than 5, grant 5% discount on the purchases.

If instrument type is security or currency is INR, reduce transaction tolerance by 2%.

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Why AI ?

RulesNot just IF-THEN Rules have a state – the conclusion

Helps versioning of rules and nested rules

Reasoning

Primarily backward-chaining

Grouping of rules

Dynamic sequencing

Versioning of rules

Why not ECA ?Do not want event driven rules. No rules to be fired actively.Business application is the ‘boss’ and decides when to fire a rule.

# 34APPLICATIONAPPLICATION

Application Flow{ Data Structures

::

::

:: }

class c1

class c2

::

class cn

Call to rule engine

Call to rule engine

Made visibleto rule engine

Business Rules specified

RuleExecutionEngine

Conceptual Architecture in Application

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Example: Rules in charge calculation

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Example: Rules in charge calculation (contd)

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Example: Rules in charge calculation (contd)

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Example: Rules in process plant monitoring

Rule Engine

Equipment and Inspection parameters are passed to Infrexand it returns the rule index of rules fired.

CMFD Database

Causes, actions, observations and remedies are shown to the user.

Condition Monitoring & Fault Diagnosis

system

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Example: Rules in process plant (contd)

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Example: Rules in process plant (contd)

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Example: Rules in process plant (contd)

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Example: Rules in process plant (contd)

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Example: Rules in process plant (contd)

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Knowledge Based Systems

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Developed for Indian AirlinesInstalled at I.G.I. Airport, New Delhi, India in 1992

handlesPilot, Flight Engineer and Cabin Crew roster

featuresMonthly Rostering of Crew for Flights,Disruption Management and Reporting

Crew Rostering System

PROBLEM

Allocating Crew for Flights

Considering Flight and Duty Rules

and Airline’s objectives

Max 65 hrsRest 10 hrs6 hrs/ day26 hrs/wk.

PROPER CREW FOR ALL FLIGHTSEQUAL FLYING AND DUTY HOURSEASY HANDLING OF DISRUPTIONS

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In all during the rostering44 important parameters, 31 constraints and 16 major objectives are considered.

The system refers to about 145 rostering rules while doing monthly rostering.

Sample Rules:Sample Rules:1. Every Crew should get one weekly off in any 7 day period, and it must be for 24 hours with

a 2200 hrs to 0600 hrs period.2. A junior co-pilot should be rostered with a senior pilot.3. After a long night-stop flight, avoid giving another night-stop flight immediately.

Why AI for CRS ?

Crew qualifications: aircraft cleared, valid licence, stations cleared,…

Flight Duty Time Limitations (FDTL): rest after flight, maximum duty in 24 hrs, maximum weekly, monthly flying allowed, etc.

Flight duration and requirements: day-return, night-stop, international, domestic,…

Airline objectives: equal trips, equal flying hours, equal night-stops, equal duty load, equal rotation of flights, etc.

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Crew Rostering System (contd.)

StrategyConstraint monitoring heuristics-based strategy

Works on progressive reduction of the search space

Parameters and constraints are classified into 3 categories.

1st category: all essential parameters and constraints which makes a crew eligible for a flight -- the ESet.

2nd category: parameters and constraints which decide the subset of crew (from ESet) available for flight -- the ASet.

3rd category: remaining parameters and constraints help decidewhich crew from the ASet should be assigned a flight.

Why not Optimization Techniques ?Wanted a good working model rather than the bestas the ‘best’ may not have been the most practical one.

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• Structural Verification with the help from the tool– Graphical representation of rules– Rule-level testing– Sample testing– Get the rules reviewed by another engineer– Module testing

• Validation– Run system against old input data and compare against decision taken– Walk-through the system with experts– Random testing– Systematic testing– Parallel testing– Simultaneously on-line with human decision-making– System runs by itself; output is reviewed by experts

Crew Rostering System (contd.)

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Output: assignment of crew to the various flights and duties.

Crew Rostering System (contd.)

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Disruption ManagementList of alternate crew for disrupted flight is displayed.

Crew Rostering System (contd.)

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Other real-world systems

• FOM-AID: Oberoi Hotels

• PSMS: Indian Oil Corporation

• EXCELMAN: Indian Oil Corporation

• ADB Proposal Validator: Asian Development Bank

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Part 2: Case based reasoning and Pattern based reasoning

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Case-based reasoning

Case SearchEngine

Case-baseCase C1Case C2Case C3….Case CnCase CNEW

Matched CasesC1 - 92%C7 - 67%

Input Problem

Nearest solution

Case-baseadministrator

SolveCNEW

Add unsolved case as CNEW to case-base

• Use prior experiences to guide current functioning• The CBR principle - Retrieve Reuse Revise

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The case search process

• Is inexact– Computes similar rather than exact

correspondences• Is not brittle

– Will always retrieve the ‘nearest’ possible solution• Learns incrementally

– As new cases arise they can be learnt and reused• Problem solving power improves over time• Simple model of human memory

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Where will CBR be useful?

• Situations similar but rarely identical– Call center problem solving, estimation

• Precedent driven problem solving– Legal reasoning, Medical diagnosis

• Rapidly changing situations– Disaster management

• Boundaries between rules – Handling exceptions– Credit risk assignment, loan evaluation, …

• As an inexact reasoning system– Job placement, directory assistance

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Example

Waybill Processing System

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CBR in a Large Railroad Company

Current solution -

90 Rate Analysts/Clerks manually correct the rejected waybills

Average time to rectify discrepancies is two-weeks per waybill

Even after this, there are about 20,000 disputes every month

Consequences of disputes -

Delays the financial realization

Affects reputation of the company

Number of waybills processed per day : 15000

On an average about 2000 waybills get rejected daily by the auto rate

application due to discrepancies in bill processing.

PROBLEM

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Need for CBR expert system

• The methodology used by each Rate analyst is unique, not standardized and

specific to the individual. It cannot be shared / reused across the organisation

• The time required to manually rectify the errors is huge

• In Auto rating, the changes made by Rate analyst are based on their past

experience and the resolution may not be consistent

• Each rejection is itself a case and the organisation needs to build on these cases

to improve continuously

• The knowledge gained may be lost on account of persons retiring , leaving , or

getting transferred

Why CBR ?

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CBR solution

• Captures experience of analysts using a CBR Engine called Consult

• Helps reapply experience in future

• Interfaced with existing applications to provide a smooth flow of waybills

• Proactively checks for potential disputes

• Ranks results on matches from the past experience

• Inexact matching aids in approximation

• Manual as well as automatic entry and resolution of problems

• Interfaces with legacy systems

CBR in action

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Pattern based reasoning

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• Mechanism to specify patterns over dataPatterns can be approximate and time dependentFuzzy Temporal logic extensions to Prolog

• Detect if specified pattern is present in dataParts of data that almost meets the patterns

Reasoning framework

TradeDate Scrip High Low Open Close 52Week 52Week NetTrade

Price Price Price Price High Low Quant

16032002 Co1 41 41 40.55 41 44 29.5 1602

17032002 Co1 44 42 41 44 44 29.5 1137

18032002 Co1 44 42.5 42 44 44 29.5 1200

19032002 Co1 45 44 42.55 45 44 29.5 2554

20032002 Co1 46.05 43 44 45.5 45 29.5 3056

21032002 Co1 46.45 43 46 46 46.05 29.5 1074

22032002 Co1 46.45 43 46.45 46 46.45 29.5 875

23032002 Co1 48 45.9 45.95 48 46.45 29.5 526

Closingprice

Time

Pattern for Double Top

Close price of the given scrip varies as follows in the recent history

Prior Trend is greater than some price X and

Price increases from base B1 to peak P1 and

immediately followed by Price decreases close to B1 and

immediately followed by Price increases again approximately close to P1 and

immediately followed by Price decreases to B1

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Part 3: Complex systems

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1. Commander needs to use all the information available to make decisions and issue orders

2. CCI systems need to process large sets of data rapidly and extract out information that is desired

3. CCI systems to be responsive to commander's mission of defeating the enemy on the battlefield

Objectives of Processing and Reasoning in Military domain

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DSS System for Defense

Reports andother data

Images, photos, video, audio and other multi-modal data

User inputs

Reconstructed battlefield scenario

Symbolic representation (non-pictorial)Model-basedHierarchical (increasing abstraction,

increasingly complex concepts)Multi-modal signal databaseOther database systems

Intelligent DSS

System

Signal fusionObject recognition

Object feature extractionFormation recognition

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♦ Available data may be incomplete

♦ Uncertainty due to limitation of sensors

♦ Deceptive enemy behaviour

♦ Dynamic situations

♦ Very Complex analysis

♦ Many possibilities

♦ Not precisely defined procedures always

♦ Increasing complexity of weapon systems

♦ Increasing volume of complex information

Major Challenges posed

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Broadly a DSS system should:

• Assess possible situations from the data and present it to the Commander

• Answer ‘what-if’ kind of queries on the situations

• Information-based Search facility to yield alternatives and their evaluations

• Analyze based on Multiple objectives and multiple criterias.

• Given a military objective ability to plan out operations considering current resources and ground situations.

• Sift through various situations to highlight the potentially dangerous versus the profitable ones.

Example: Operations Planning (contd.)

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• Various modes of inference and reasoning (such as Data-driven reasoning, Goal-driven, opportunistic reasoning, deductive, inductive, abductive inference etc.)

• Search techniques(such as Branch-and-bound, Hill-climbing, Best-first, Heuristic search etc.)

• Pattern-matching mechanisms(such as Existential, Universal, Inexact, etc.)

• Handling and reasoning with uncertainty(such as probabilities, confidence factors, etc.)

• Belief revision and Truth Maintenance(Assumption-based TMS, Justification-based TMS, etc.)

Example: Operations Planning (contd.)

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• Different ways of representing Knowledge • (rule-based, case-based, model-based etc.)

• Fuzzy, Imprecise and Temporal data handling

• Different knowledge sources work with each other• (blackboard systems, agent-based systems)

• Natural language interface

• Classification, generation and Learning• (neural networks, genetic algorithms, a-life)

Example: Operations Planning (contd.)

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Situation AssessmentTo identify probable situation from observed dataand events (i.e. terrain, geographical, intelligence data,enemy activities, events, maneuvers, etc.)Judge what is happening or predict what is going to happen.

Threat AssessmentTo gauge possible threats from situations afterproper analysis. Determine threats and degree of threat.Concentrate on degree and severity of future events.Identify force capability and Intent.

Example: Operations Planning (contd.)

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Data Processing requirements in C4I2 domain

• Situational data: Battle situation at any given time: units, aircrafts, terrain, combat systems and supplies

• Stored data – Terrain data and features , Maps, Analysis, Enemy data, Historical data

• Large numbers of off-board and on-board sensors, high target densities, rapid sensor updates, significant data uncertainties

• Vast increase in the volume of strategic and tactical information available to commanders at all levels

• Must process all available data quickly and efficiently, extract out the relevant information while discarding the rest

• Fast detailed and accurate ISTAR (Intelligence, Surveillance, Target Acquisition and Reconnaissance)

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Data Characteristics in C4I2 domain

• Different types of data – Text / Documents (structured, unstructured, free-format), Positions (precise lat-long to ‘possibly present’), Numeric data, Electronic sensors, Pictures, Mixed

• Different sources of data –Sensor reports, Field Observations, Documents, Spy report, Interceptions, Maps, News articles, Internet

• Very large amounts of data – Continuous data feed, Data comes in bits and pieces, data comes in bursts

• Time-stamped data (mostly), Non-time stamped – features of equipment

• Quality of data – Trustable, Reliable, Probable, Informative, Unsure, Deceptive

• Data form – Encrypted, Plain text, Jumbled

• Hidden information, hidden patterns, etc.

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Motivation Reasoning to support complex analysis

Multimodal reasoning

Ability to reason in various ways over a set of datafor example: patterns, rules, cases

INPUT 1Radars

INPUT 2Airborne

INPUT nSpy

INPUT MONITORING

ANDDATA FUSION

Decision aid/ Planning support

Expert Analyst

Knowledge

Rules

Patterns

Experience / Cases

AnalystSituation

Assessment

Threat Assessment

:

:

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Surveillance Stock market, Military, Banking

Diagnosis Fault diagnosis, Process monitors

Assessment Situation, Threat, Risk assessments

Multimodal reasoning: Data-intensive domains

• Very large amounts of data – mostly time-stamped data

• Need to analyze that data quickly and determine what is going on

• Want to predict before event / disaster occurs

• Has various interwoven knowledge / expertise

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Processing Text and Numeric Data

Text Data

Information Extraction from large texts (gathered from reports, messages,

interceptions, documents, etc.)

Identify Similar incidents of past

Correlate and extrapolate

Numeric Data

Specify possible patterns of interest in data (gathered from sensors, field

observations, measurements, etc.).

Identify parts of the data where patterns can be identified

Evaluate rules of operation and reason to determine intent

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What is information extraction?

• Information extraction is a process that takes natural language texts as input

and extracts out potentially useful nuggets of information into a structured

database

• Can be used as a filter on text messages and documents that retains

potentially useful information and discards useless information

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Why do we need Information Extraction?

• Textual information overload– Volumes of textual information received or available– Relevant information buried within these volumes

• Processing semi-structured data– Most transactional systems have textual fields: e.g. Comments,

remarks, problem, solution, notes, ….– Dense, ungrammatical, misspellings– Ignored in structured analytics– But contains valuable information which can be very useful– IE can improve findings and quality of structured analytics

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What kinds of information can be extracted?

The 5 Ws– WHO: which unit is to accomplish the task.

– Normally identified by a Unit code / Unit_ID.– When exact Unit is not known, could be identified by location.– Could be identified by ROLE (Main Thrust, Support etc.)

– WHAT: the task to be accomplished.– Purpose (Defeat, Destroy, Contain, Clear, etc.)– Parameters: (dependent on the term but required for clarification: Destroy what?)– Could be either an operation or specific task.– Selection maybe dependent on the intent of the senior commanders

– WHEN: the timing of the task.– Control type (at a certain time, within a certain time, time after an Event (D+1, H+2, etc.))– Dependence on other entities

– WHY: the reason for accomplishing the task.– Objective (Offensive, Support, Defend, Pre-emptive etc.)– Necessary and just sufficient information for various levels

– WHERE: the location for accomplishing the task.– Lat/Long, Terrain, Reference Points etc.

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Technology in information extraction

• Normalization– Converting PDF / DOC / PS files to HTML or Text

• Cleanser– Misspelling correction, ellipses, synonym rewrites, …

• Pattern matching– Use of regular expression like patterns for tagging

• Shallow parsing– Identification of nouns, verbs, adjectives, adverbs, etc.

• Part of speech tagging and morphological analysis– Use of statistical processes for handling unknown words

• Anaphora resolution– Determining which noun a pronoun refers to

• Deep parsing and Named Entity Relations– Determining relations between nouns, verbs and other terms

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Part 4: New ways to interact with systems

Conversational Systems

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Current scenario of User Interface for business applications: GUI consisting of menus/forms E.g ERP packages,Online Banking, E-Commerce applications.

• Menus restrict choice• Control implicit• Context at top

• Novice, Skilled, Expert users• Graphical menus – slow skilled user ?• Expert users type ahead of screen• Can user find the menu item they need ?

Motivation

Users pick what they want to do from a list of alternatives

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What if there are too many options ?

Confusing to display 100 items –cognition

Hard to find one item in 100 items

Hard to find the right menus

Context sensitive menus

User defined menus

You know what you want – but do not know where to specify it ; nor do you have the time

Problems of Menu-driven systems

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What is Conversational Interface?

Interface that enables communication with a machine by establishing a dialog in natural language as used in human-to-human interaction

Advantages

• Provides easily learned and easily remembered human-computer interaction.

• Linguistic structures, such as connectives, conditionals, and quantifiers, allows users to group sets of basic actions

• Reduces navigation time (number of clicks) for experienced users.

Motivation

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Problems in developing unrestricted natural language UI:

• Highly ambiguous words having more than one meaning .

• Use of jargons requires domain knowledge for correct interpretation.

• Continuous evolution and extension of NLs.

Solution

• Making the language restrictive to cover only a limited subset of the

vocabulary and syntax of a full natural language.

• Coverage of only domain specific concepts to reduce ambiguities.

Why “Restricted Natural Language Conv. Sys.” ?

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ObjectivesSimplify user interaction with systems by establishing a dialog

Drill down to what the user wants, and carry out the request.

Develop a generic NLI pluggable to any application.

ProcedureUser describes task to be carried out in natural language.

System identifies key concepts from user input based on domain ontology.

Initiates a conversation for additional information to clarify the required task.

Computes weight of all possible tasks based on concepts raised.

Performs the maximum weighted task after confirming with user.

Provides easily learned and easily remembered human-computer interaction.

Linguistic structures, such as connectives, conditionals, and quantifiers, allows users to group sets of basic actions.

# 87

NATAS

Intelligently seeks

clarification and responds

Correct Syntax and semantics

not required

Text Based Interface

between humans

and

Business

Applications

Natural Language enabled

# 88

Application Data

BROAD ARCHITECTURE

MySQL OracleSfleX

Business Application

APIs Services

Ontology CreationSeed Ontology

Application OntologyRules

CWM

RDF (Domain Ontology)RDF GRAPH

NATAS

EMAIL CLIENT

Email Spooler Email Parser

Conversation Manager

NATAS

PROCESSING

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Domain Ontology

Application Ontology + Rules (N3)

CWM

Application Database + Seed Ontology

Ontology Generator

Domain Ontology

# 90

Domain Ontology created

in RDF Format

Concepts areloaded into the memory

Parts of Speech Identified from the query posed

Key Conceptidentified based ondomain ontology

Emailbased

NL Interface

Query PosedBy User

Based on concepts either

SPARQL, API, Ontology Traversal

InitiatesConversation

for clarification

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Examples

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Seed Ontology

Asset

Version

Asset_id

Limitations

State Reason

nameHas_id

posses

Consists_of

has

Has_name

Can_have

SubjectPredicateObject

# 95

Architecture_description

Licence_type_id

Asset_id

Asset_name

state

version

Total_benefit

SDLC_P

hase

_id

R

1.2

15000

4

JOA

8

N-tier

coding

4412

Architecture_id

Subject

ObjectPredicate

Domain Ontology

Asset_Classification_id17

SDLC_phase_id Description

214

Domain

_id

1

User_size_id

3

# 96

Second Life

# 97

Future Areas

• Agent based systems

• Intelligent control

• Advisory Systems

• Negotiating Systems

# 98

Tom Mitchell President AAAI

"The goal of AI is not to build computers that replace people except, of course, for those chores that people don't want to do because they are dangerous, mundane, or otherwise unappealing.

The goal is to build machines that enhance what people can do."

Won't be long before robots will be involved in search & rescue operations, elderly care, highway driving, etc.

Future

# 99

Thank you!

The potential is immense !!

# 100

Good booksUnderstanding Artificial Intelligence Henry C. Mishkoff

Artificial Intelligence Elaine Rich and Knight

Artificial Intelligence: An MIT perspective Patrick Henry Winston

Introduction to artificial intelligence Eugene Charniak

Essentials of Artificial Intelligence Matt Ginsberg

Principles of Artificial Intelligence Nils J Nilsson

Foundations of Logic Programming J.W.Lloyd

AI magazine, IEEE Intelligent Systems, Artificial Intelligence