Industrial applications of AI The engineering of

Industrial applications of AI The engineering of

“intelligent” systems

by

C. Anantaram

TCS Innovations Lab, Delhi

IIT- Bombay AI lecture

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

Introduction

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 to

work with inexact and insufficient data, manage policies,

participate in knowledge management, resolve problems…

• 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

Authorization Request (CustomerID,Price)

PURCHASE ACCEPT

(CustomerID,Price)

Authorization Request

PRODUCT REQUEST PURCHASE ACCEPT PRODUCT DELIVERY

Mobile Service Provider Merchant

Rule Engine

Modified Authorization Request

Auth.Confirmed

Example 1: m-Commerce

Merchant specified Rules

Example 2: Directory Assistance

System to assist Telephone call operator

S Y S T E M

Telephone number of Dr. Manish Tiwari Ahm Ngr

Manish Tiwary

Flat # 2B, Savvy Estate Ahim Nagar Phone - 3443422, mobile - 9873922323

95%

Maneesh Tiwary

23 B, Tenali Colony, Ahim Nagar Phone - 346982

90%

Manish Tiwari

Villa - 101, New Colony, 12 M B Road Phone - 231232

89%

Monish Trivedi

Flat - 341, Sanata Building, Nagar Rd Phone - 238744, mobile - 981234523 75%

Matched records

Example 3: Search documents

Retrieve documents related to ‘terrorism’

Level of exactness required - Medium

Case Based Reasoning System

...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

...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

...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”

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 !

Part 1: Rule based systems

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.

Rule based representation

Example 1

IF patient has a runny nose AND a harsh cough AND

brownish rash AND

high temperature AND bloodshot eyes AND conjunctivitis AND white spots AND intolerance to light THEN

patient has measles.

Rule based representation

Example 2

IF Account type is Corporate and Money Transfer required immediately

THEN

Wire Funds Transfer Charge = 1% of Transfer Amount

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

THEN

SET discount TO (PremiumAmt OF policy * 0.25) AND

INFORM customer

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) )

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).

Rules of inference in Logic

Unification: Binding variables

OR finding substitution for variables

Resolution: Resolves two parent clauses to produce a derived clause.

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

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

Reasoning

Inductive: From given facts derive facts that are more generic about the situation : generalization Example: There was an accident once again.

Three people were injured seriously.

Area is accident prone.

Need accident avoidance systems.

Reasoning

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

There must have been blood.

There could be broken glass.

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

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 assertions

can be derived.

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 vacation 2. Throw a party

3. Buy the DVDs you wanted 4. Buy the books you wanted

Cannot go to Europe for a vacation

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 derived assertions

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

• Solution is the collection of all assertions made during the solving processOR

None if some sub-goals cannot be solved.

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

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

Classification of RBS

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

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

Reasoning

Knowledge

Weak Rich

Rich

Battlefield Threat Assessment Situation Assessment Jet Engine Fault Diagnosis

Rules for charge calculation Insurance premium rules Customer call handling and

escalation 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

Requirements of RBS

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

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

Reasoning

Knowledge

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

Rules in Business applications

• 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.

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

APPLICATION APPLICATION

Security management

User Interface

Transaction processing

Business Rules Application Logic

Control Logic

Database CS component

Workflow

Targeted towards

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

• 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

RULE

IF (Customer.BillAmount > 200 ) AND

(Customer.mTransactions > 5 ) THEN

SET mPurchase.discount TO 5%

Example rules

RULE

IF Instrument_Type EQ “Share” OR Transaction.currency EQ “INR”

THEN

DECREASE Tolerance OF Transaction 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%.

Why AI ?

Rules

Not just IF-THEN Æ 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.

Application Flow { Data Structures

: :

: :

:

: }

class c₁ class c₂

: : class c_n

Call to rule engine

Call to rule engine

Made visible to rule engine

Business Rules Rule

Execution Engine

Conceptual Architecture in Application

Example: Rules in charge calculation

Example: Rules in charge calculation (contd)

Example: Rules in charge calculation (contd)

Example: Rules in process plant monitoring

Rule Engine Equipment and Inspection

parameters are passed to Infrex and 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

Example: Rules in process plant (contd)

Example: Rules in process plant (contd)

Example: Rules in process plant (contd)

Example: Rules in process plant (contd)

Example: Rules in process plant (contd)

Knowledge Based Systems

Developed for Indian Airlines

Installed at I.G.I. Airport, New Delhi, India in 1992 handles

Pilot, Flight Engineer and Cabin Crew roster features

Monthly Rostering of Crew for Flights,

Crew Rostering System

PROBLEM

Allocating Crew for Flights

Considering Flight and Duty Rules and Airline’s objectives

Max 65 hrs Rest 10 hrs 6 hrs/ day 26 hrs/wk.

PROPER CREW FOR ALL FLIGHTS EQUAL FLYING AND DUTY HOURS EASY HANDLING OF DISRUPTIONS

In all during the rostering

44 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.

Crew Rostering System (contd.)

Strategy

Constraint 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 decide which crew from the ASet should be assigned a flight.

Why not Optimization Techniques ?

Wanted a good working model rather than the best

• 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.)

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

Crew Rostering System (contd.)

Disruption Management

List of alternate crew for disrupted flight is displayed.

Crew Rostering System (contd.)

Other real-world systems

• FOM-AID: Oberoi Hotels

• PSMS: Indian Oil Corporation

• EXCELMAN: Indian Oil Corporation

• ADB Proposal Validator: Asian Development Bank

Part 2: Case based reasoning and

Pattern based reasoning

Case-based reasoning

Case Search Engine Case-base

Case C₁ Case C₂ Case C₃

….

Case C_n Case C_NEW

Matched Cases

C₁ - 92%

C₇ - 67%

Input Problem

Case-base administrator

Solve C_NEW

Add unsolved case as C_NEWto case- base

• Use prior experiences to guide current functioning

• The CBR principle - Retrieve Reuse Revise

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

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

Example

Waybill Processing System

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

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 ?

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

Pattern based reasoning

• Mechanism to specify patterns over data

Patterns can be approximate and time dependent Fuzzy Temporal logic extensions to Prolog

• Detect if specified pattern is present in data

Parts 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

Closing price

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

Part 3: Complex systems

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

DSS System for Defense

Reports and other data

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

User inputs

Reconstructed battlefield scenario

Symbolic representation (non-pictorial) Model-based

Hierarchical (increasing abstraction,

increasingly complex concepts) Multi-modal signal database

Other database systems

Intelligent DSS System

Signal fusion Object recognition Object feature extraction

Formation recognition

♦ 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

Major Challenges posed

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.)

• 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.)

• 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

•

• Natural language interface

• Classification, generation and Learning

• (neural networks, genetic algorithms, a-life)

Example: Operations Planning (contd.)

Situation Assessment

To identify probable situation from observed data

and events (i.e. terrain, geographical, intelligence data, enemy activities, events, maneuvers, etc.)

Judge what is happening or predict what is going to happen.

Threat Assessment

To gauge possible threats from situations after

proper analysis. Determine threats and degree of threat.

Concentrate on degree and severity of future events.

Identify force capability and Intent.

Example: Operations Planning (contd.)

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)

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

Motivation Reasoning to support complex analysis

Multimodal reasoning

Ability to reason in various ways over a set of data

for example: patterns, rules, cases

INPUT 1 Radars

INPUT 2 Airborne

INPUT n Spy

INPUT MONITORING

AND DATA FUSION

Decision aid/

Planning support

Expert Analyst

Knowledge Rules Patterns Experience /

Cases

Analyst Situation

Assessment Threat Assessment

: :

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

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

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

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

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.

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

Part 4: New ways to interact with systems

Conversational Systems

¾ 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

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

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

¾ 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.

¾ 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.” ?

Objectives

™ Simplify 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.

Procedure

™ User 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.

¾ Provides easily learned and easily remembered human-computer interaction.

NATAS

Intelligently seeks

clarification and responds

Correct Syntax and semantics

not required

Text Based Interface between humans

and

Business Applications

Natural Language enabled

Application Data

BROAD ARCHITECTURE

MySQL Oracle

SfleX

Business Application

APIs Services

Ontology Creation

Seed Ontology

Application Ontology

Rules

CWM

RDF (Domain Ontology)

RDF GRAPH

NATAS

EMAIL CLIENT

Email Spooler

Email Parser

Conversation Manager

N A T A S P R O C E S S I N

Domain Ontology

Application Ontology + Rules (N3)

CWM

Application Database + Seed Ontology Ontology Generator

Domain Ontology

Domain Ontology created

RDF Format in

Concepts are loaded into the memory

Parts of Speech Identified from the query posed

Key Concept identified based on

domain ontology Email

based NL Interface

Query Posed By User Based on concepts

either SPARQL, API, Ontology Traversal

Initiates Conversation for clarification

Examples

Seed Ontology

Asset

Version

Asset_id

Limitations

State Reason

Has_id name

posses ^Co_nsi sts_

of has Has_name

Can_have

Subject Predicate Object

Architecture _description

Licen ce_typ

e_id Asset_id

Asset_name state

version

Total_benefit

SDLC_Phase_id R

1.2

15000

4

JOA

8

N-tier coding 412 4

Architecture_id

Subject Object

Predicate

Domain Ontology

Ass et_C

lass ification

_id 17

SDLC_phase_id _Des

cription

214

Dom ain_id

1

User_size_id

Second Life

Future Areas

• Agent based systems

• Intelligent control

• Advisory Systems

• Negotiating Systems

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

Thank you!

The potential is immense !!

Good books

Understanding 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