Knowledge management Definitions and Outline

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Knowledge Management and Engineering Class Notes2009

These notes are to supplement the textbook and provide other viewpoints. I apologize for

the lack of references. These notes were originally just lecture notes and not given out.

Need for intelligent systems (smart machines)

Acronyms (learning the language)

DSS decision support system

IS intelligent systems

BI business intelligence

KM knowledge management

AI Artificial intelligence

CRM customer response system

ERP enterprise resource planning

BAM business activity monitoring

SAP the enterprise software

SAS statistical software company

SOA service oriented architecture

OLAP online analytical processing

SQL query language

KPI key performance indicators/metrics

CSF critical success factors

Part 1 Overview

Todays Environment According to a recent survey, the top businesspriorities, in order, from one to 10 were business-process improvement,security breaches and disruptions, enterprise-wide operating costs,supporting competitive advantage, data protection and privacy, the need forrevenue growth, using intelligence in products and services, focus on internalcontrols, shortage of business skills and faster innovation and cycle times.

The top 10 technology priorities were security enhancement tools, businessintelligence applications (BI), mobile workforce enablement, workflowmanagement deployment and integration, enterprise resource planningupgrades (ERP), storage management, voice and data integration over

Internet protocol, customer-relationship management, business-processintegration tools and server virtualization.

remember the old days when1. people had to know prices, determine tax and then make change. Now hit product key

and the rest is done

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2. Programmable thermostats

Global Differences and the Flat World

The basics and fundamentals of the knowledge/DSS never talk about the decisions thatvary according to geographic locations. By geographic locations I mean, a problem in

decision in USA may not have the same solution for the same problem in India or else

where, because of geographic constraints. The constraints might be the people, their mindsets and most important "buying power-Money".

Of course geographic locations affect decisions but so does culture, history, religion,

social status, education, financial, intelligence etc.

We are focusing on developing DSS techniques to make better decisions. Better decisions

must take all aspects under consideration. Tailored approach

Fundamental change

Huge organizations are often the leaders in implementing and/or deploying

technology. New technology development and/or deployments require resources thatmany small companies do not have. Also many small companies develop products for

the big companies for their first customers.

Small companies are often at the mercy of the larger corporations and their decisions

and they will be a part of their system and as a system they will never be able to make

decisions on their own. Its all a chain mechanism. Example of UTA and

PeopleSoft, SAP

For example, in the 60s only large companies had computers; today everyone does. Infact at Lockheed Martin many other large companies use Excel. Excel does not cost

much and only requires a PC.

You could therefore ues Excel to schedule your labor pool, how to best buy power by

keeping records of energy costs, analyze them analytically and then make betterdecisions, etc. Use excel to determine how much taxes you owe.

Look at the many low cost technologies that available today. Internet, cell phones with

internet, GPS and camera, smarter cars, ATM, Gas pumps with credit cards, bar codes,RFID.

Companies are always at the mercy of something (government, competition, vendors,trends, consumers, economic conditions, new technologies etc.)

Many large companies transition to failure (Jeep, Digital Equipment, Enron, Pan Am,

locomotive companies, telegraph, etc. )

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New large companies are being born every year Google, Apple, Virgin Airlines,Inofsys, Face Book

Some thoughts:

Study history but:

o Do not believe that you can correctly predict the future? (economy,

technologies, world events)- only fools predict the futureo The consensus view is often wrong.

o You should be prepared to work in different companies, industries and

countries.o I can, however, guarantee that you will be using software to make better

decisions. Be a leader not a folllower

Part 2 - Overview of Knowledge/information/data, KM, KE, BI,DSS

Its all about data, information and knowledge and knowing the difference. J Priest,

2005.

Ordering pizza

Ordering A Pizza in 2010This is scary because it might possibly be true one day. Be

sure that your speakers are on and the volume is turned up.Just click the link below.

ORDERING PIZZA IN 2010Need to differentiate - 1967 Websters dictionary

1. Data factual material used as a basis for calculating or reasoning (factual may

not be correct!)2. Information Data that has been processed and presented in a form suitable for

human interpretation.

3. Knowledge the fact or condition of knowing something with familiarity gained

through experience or association4. Actionable knowledge knowledge where correct or best decision can be

taken. (satisficing efficient but not necessarily optimal)

Our focus

Knowledge (goal)- right collection of information at the right time (accurate, usefuland timely) or ( 1. contextual, 2. relevant, and 3. actionable) lets go with blue

Knowledge (our task) - ability to detect relationships between objects and events,and reason about those relationships to arrive at judgments, decisions and

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conclusions. The ability to discern patterns and make sense out a sea of information

or lack there of (know when we do not know).

Learning organization capability of learning from experience

Culture pattern of shared basic assumptions

KM Initiativeso Knowledge acquisition/creation

o Knowledge Representation - Organize, format

o Application - Seeking / sourcing pull Sharing push

COTS commercially off the shelf system

Concept ofintellectual capital

Human capital -The knowledge-power that comes and goes from an organization

each day, the knowledge that resides in the heads of employees and that has not

been shared with others

Intellectual capital -An effort by organizations to place a financial value on itstacit and explicit knowledge. Can quantify

knowledge workerin the knowledge economy

Why is Google and Microsoft so good? Technology or people? If you are so smart

try to get hired by Google!

the idea of the learning organization; always improving

various enabling organizational practices such as Communities of Practice, Best

Practices and corporate Yellow Page directoriesfor accessing key personnel and

expertise;

various enabling technologies such as knowledge bases and expert systems, helpdesks, corporate intranetsandextranets, Content Management,wikis, blogs and

Document Management.

Sharing reuse - communities of practice best practices lessons learned -

guidelines turning know-how into results o Best practices: An assessment recommending the most appropriate way of

handing a certain type of task, based on an observation of the way that

several organizations handle that task.o Communities of practice: Formal or informal groups of people who share

common work interests, such as a profession or work project. In the

context of knowledge management systems, these communities usuallyinteract online rather than in person.

o Content management tools: Software that helps developers track thelocation of information (especially important in medium and largesystems, where the location of content can be easily forgotten) and

relationships among the different pieces of information.

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[edit] Approaches to knowledge management

There is a broad range of thought on knowledge management with no agreeddefinition current or likely (this is not science). The approaches vary by author and

school.

A key distinction made by the majority of knowledge management practitioners is

Nonaka's reformulation of Polanyi's distinction between tacit and explicit knowledge. Inthe popular form of the distinction tacit knowledge is what is in our heads, and explicit

knowledge is what we have documented.

Nonaka and Takeuchi (1995) argued that a successful KM program needs to, on the onehand, convert internalized tacit knowledge into explicit codified knowledge in order to

share it, but also on the other hand for individuals and groups to internalize and make

personally meaningful codified knowledge once it is retrieved from the KM system.

Critics have however argued that Nonaka and Takeuchi's distinction between tacit andexplicit knowledge is oversimplified, and even that the notion of explicit knowledge is

self-contradictory.[1]

2 types:o Explicit, leaky (policies, procedures, documented)

o Tacit , embedded, sticky, subjective, cognitive, and experiential learning

Role of IEsIE skills aid knowledge management

-----------------------------------------------------------

-- IE knowledge a plus --

Knowledge management can be defined as the sum total of all activities

that enable the creation, storage, distribution, and application of

knowledge in organizations.

Similar to the problems faced by industrial engineers, the knowledge

management problem in organizations is one of managing a complex

system. Industrial engineers are involved with the design,

construction, installation, and advancement of complex systems.

The knowledge industrial engineers possess is varied, ranging from the

highly quantitative (such as mathematics and physics) to the

qualitative (such as the social sciences and management).

IEs can help plan for or even prevent redundancy and failures within an

organization by devising formulations that detail the fragility of an

organization's knowledge base.

Industrial engineers have training in logistics and scheduling

problems; much of it is applied to movement of digital goods and

scheduling of jobs on factory floors.

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These insights can be brought to bear on an organization's knowledge

transportation problem, for example. IEs can aid an organization by

studying the routing and movement of knowledge to determine the

efficient mechanisms for connecting people. IEs can also:

* Help resolve an organization's problems in many ways. Their knowledge

in the design of adaptive and self-repairing systems is pivotal.

* Aid organizations in devising appropriate measures that gauge the

quality of knowledge management systems.

* Help an organization in the difficult task of tacit knowledge

maintenance. Before an organization contemplates downsizing or mergers,

it should check how the new organization fits into the existing

knowledge map.

* Aid in the design of flexible systems that will enable growth and

evolution. Knowledge management systems in organizations should

accommodate modifications and updates that account for changes in an

organization.

The skills of IEs are valuable and salient for making knowledge

management a reality in organizations. For decades IEs have optimized,

industrialized, and engineered physical components of organizations.The talents of industrial engineers can also be applied to the non-

physical components of an organization.

(Excerpted from "Knowledge Management: A New Commission for Industrial

Engineers" in the January/February 2004 issue of Industrial Management

magazine

Business intelligence (BI) is a broad category ofapplications and technologies for

gathering, storing, analyzing, and providing access to datato help enterprise users make

better business decisions. BI applications include the activities ofdecision support

systems, query and reporting, online analytical processing (OLAP), statistical analysis,forecasting, and data mining. http://www.whatis.com

Harrahs GamblingData warehousing

Data mining, business analytics, BI

CRM customer relationship managementDSS decision support system

BAM Business Activity Monitoring

BPM Business Process Management or business process modeling

SOAP Simple Object Access Protocol, message based protocol based on XML forassessing web

SOA Service Oriented Architecture

Goals and Objectives for Project

Introduction group decisions with group think, bias conflicting objectives etc

(Lubock story)

Great decisions have not fond underlining reasons for great decisions:

individual vs. team, intelligence, luck, environment etc.

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Efficiency and effectiveness (goals achieved vs. cost, inputs to outputs)

Sub optimization conflicting goals

Satisficing often your goal is to identify several options for management or

you to decide on, 3 to 5 alternatives is the best , not too many, not too few

Risk and scenarios risk vs, sense of control scenarios are perfect for IEs, think of

time studies where the focus is on the decision making and tasks performed notthe time, worst, best most likely, average way to handle chaos

Model Types

strategic (top mgt, long term. Planning),

tactical (how to make it happen, middle mgt, allocate and control resources),

operational (day to day, all levels),

analytical (analysis of data, often specialists)

Other issues

User interface the future, text to speech, speech, 3d visual, animation, cell

phones PDA, RFID, wireless, UWB capabilities

Blackboard

Emerging use of spreadsheets (learn to use) this may be your tool

Fedex web focus , case application overall discussion how things have changed.

Overnight to tracking in real time of trucks

Static vs. Dynamic future focus will be on dynamic since we will soon be gettingreal time data then information

Book OutlineAn Introduction to Knowledge Engineering

Kendal and Creen

1. Introduction

a. Definitions - handout

2. Types of KBSa. Expert systems

b. Neural

c. CBR

d. Genetice. Intelligent agents

f. Data mining (databases)

3. Knowledge acquisitiona. Explicit, tacit, Deep, shallow

b. Unstructured

c. Structuredd. Event recall

e. Twenty questions

f. Added observation, questionnaire, scenario, storyboard

4. Knowledge representation and reasoning

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a. Heuristics

b. Propositional logic and symbols

c. Forward and backward chaining, Data driven, goal drivend. Problems of explanation and brittleness

e. Semantic networks

f. Framesg. Ontology - handout

5. Expert shells

a. What are shellsb. AI languages, PROLOG, Facts, inferences

6. Life cycles and methodology

a. Product development

b. Importance of prototypesc. Blackboard architecture

d. Problem solving

e. KADS

f. Hybridg. AION BRE

7. Uncertain reasoninga. Handout

b. Confidence factors

c. Probabilistic reasoning, Bayesian

d. Fuzzy Logic - handout8. Hybrids

Chapter 2 KBS or Applied AIMethods

Expert systems - rules

Machine learning

Text and information extraction data mining, google like

Scene recognition identify issues in a large scene, face recognition

Neural network data mining, similar to regression

Genetic algorithms optimization, 0/1

Fuzzy logic - quantify how people think

Key questions

When are these methods used?

Advantages and disadvantages

Concerns

Chapter 2 F Databases

1. Database Shared collection of logically related data designed to meet theinformation needs of multiple users in an organization

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2. Relational database DBMS that manages data as a collection of tables in

which all data relationships are represented by common values (ID #) in

related tables3. Object oriented database

4. Entity thing about which an organization chooses to record

5. Attribute Named property or characteristic of an entity that is of interest tothe organization

6. Object Structure that encapsulates attributes and methods that operate on

those attributes7. Object class - Logical grouping of objects that have the same or similar

attributes and behavior

8. Primary key A candidate key that has been selected as identifier for an entity

type, may not be null9. Candidate key An attribute that uniquely identifies each instance of an entity

10. Business rule Specifications that preserve the integrity of the logical data

model

11. Trigger Assertion or rule that governs the validity of data manipulationoperations such as insert, update, and delete

12. SQL fourth generation query language

Data warehouse, Business Intelligence

1. Data warehousingbecoming so large government, walmart, fedex etc.

2. Sources of data growing exponentially, tom thumb card, rfid, your mav card, webhits, cookies discuss (pizza delievery)

3. Auto data collection

RFID, PDA, Sensors etc mems, nano

4. Data quality !major problem, garbage in garbage out,start of your responsibility

5. Data integrity note difference,, does a change in one place change others6. Database organization relational, hierarchial, object, network and othersincluding hybrids

1. Effects what you can and cannot do

2. Granularity, grain, dimensional models, fact tables7. Data Warehouse

1. Why , access with replication/backup

2. Characteristics3. Wal-Mart

4. Data marts

5. Metadata

8. Business Analytics (BA) and Business Intelligence (BI)1. OLAP - drill down, slice and dice level depends on details/access

(granularity, grain etc,)

9. Dashboards (Handout), real time looks10. OLAP and SQL

1. Should you learn SQL?

Are BI Making Firms Better

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Larger companies more likely

Successful companies spend 50% more on BI, necessary but not sufficient for

success

Dissatisfaction usually derives from distribution of the results

Data MiningData mining Most important, can be IE job

2. Used to describe knowledge discovery in databases

3. Fancy words for data analysis not just statistical, find newknowledge/information

4. Key word search, lack of depth, Google itus

5. Statistics6. 3 types of methods (simple statistics, intermediate (regression, decision

trees, and clustering) and complex (neural, rule induction, genetic, CBR)

7. Predictive or descriptive8. Uses

1. Typical predictive, who is buying what, loans, explain what ishappening at a store, etc.

2. Multiple decisions decision trees

3. Early warning (anomaly detection) identify problems quickly, notify

right people, respond automatically or manually, identify emerging

threats, sudden increases, certain conditions, tire recall, keeping trackof patent disclosures

4. Survival data mining time to event problem, came from medical

studies of dying patients, churn, migration, identifying lapsedcustomers, when will a customer make a new purchase or upgrade

(10% within one year, 30% one to two year, etc.), customer half life

9. How organizations growi. What happened - reporting

ii. Why did it happen - analysis

iii. What will happen prediction

iv. What is happening - operationsv. What do I want to happen active BI

10. Predictive analysis to avoid traffic jams GPS and radio traffic updates

11. Recognizing customers and what they want before they enter a fast foodrestaurant, monitor # of cars entering, 50% people at lunch order

cheeseburger, avg. 2 people per car, gives chance to prepare food quicker

12. Tools, list notice rehash

13. Text mining major area for future14. Why training, validation, verification

11. Clustering analyze historical data and automatically generate a model that cab

predict future behavior. Partition a database into segments.Most effective when number of variables involved is very large and

the relationships between them is complex and imprecise

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Neural networks difficult to provide good rationale for the

predictions. Need considerable training data. Limits to size of

database (large and small)Types

1. Nearest neighbor kNN, LRW

2. Neural network3. Rule induction

4. Decision trees

5. Time series12. New territory

1. Unstructured and semi-structured data (web content and real time)

2.

13. Data visualization future area1. Haptics, action virtual touch

14. Multidimensional dimensions, measures, and time

15. Real time data collection and analytics, and control/action (Autonomous)

16. Web intelligence BI and the web17. Case study, Cluster Analysis handout

Chapter 3 Knowledge Acquisition (elicitation, discovery, creation, etc)

Knowledge elicitation obtaining knowledge from a human expert (e.g. domain expert)

(or a database)

Knowledge discovery process that attempts to identify and interpret patterns

in information that are important to performing some task. Adds value by

making it more accessible, tractable, and usable.

Drowning in information but starved for knowledgeJohn Naisbitt

Real genius lies in looking at old facts in a new way Unknown

Knowledge acquisition includes the elicitation, collection/organization,analysis, modeling and validation of knowledge for knowledgeengineering and knowledge management projects.

Some of the most important issues in knowledge acquisition are asfollows (VI):

Most knowledge is in the heads of experts

Experts have vast amounts of knowledge

Experts have a lot of tacit knowledge

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o They don't know all that they know and use

o Tacit knowledge is hard (impossible) to describe

Experts are very busy and valuable people

Each expert doesn't know everything

They may not want to let your document their knowledge

Knowledge has a "shelf life"

Knowledge discovery process that attempts to identify and interpret patterns

in information that are important to performing some task. Adds value by

making it more accessible, tractable, and usable.

Discovery find something that already exists

Knowledge creation form novel associations among items and information

by trying to identify coincident timing, location, or participants

Token logical unit of data

Representation abstraction of the token(s) in a form that a user can

manipulate in a user interface.

Query expressed information need for some task. Can be in natural languageor contrived language such as SQL

Filters processes that identify to what extent a specific token is relevant to a

query. Select a subset of a database by selecting only those records thatcontain a certain value, range, or lack a value.

Language means of communication is organized in a system of complex

rules

Syntax grammar rules for expressing meaning in a string of words

Semantics study of meaning itself Semantic networks collection of all the relationships that concepts have to

other concepts, to precepts, to procedures, and to motor mechanisms

Pragmatics how basic meaning is related to the current context and listeners

expectations

Synonym Two different names that describe the same thing

Traditional grammar informal rules that are taught in schools

Conceptual graphs logical forms that state relationships between persons,things, attributes, and events

Content mapping - Identifying and organizing a high-level description of the

meaning contained in a collection of electronic document Memory

o Episodic detailed facts about individual things, history

o Semantic universal principles, dictionary definitions

Ontology formal explicit specification of a shared conceptualization, set of

primitive concepts or symbols used to model an application, provide a shared

and common understanding of a domain that can be communicated betweenpeople, promote knowledge sharing

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Heuristics - "rules of thumb" used to make a decision.

Requirements for KA Techniques

Because of these issues, techniques are required which:

Take experts off the job for short time periods

Allow non-experts to understand the knowledge

Focus on the essential knowledge

Can capture tacit knowledge

Allow knowledge to be collated from different experts

Allow knowledge to be validated and maintained

Typical Use of KA Techniques

To illustrate the general process, a simple method will be described.This method starts with the use of natural techniques, then moves tousing more contrived techniques. It is summarized as follows.

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Conduct an initial interview with the expert in order to (a) scope what knowledge is to

be acquired, (b) determine what purpose the knowledge is to be put, (c) gain some

understanding of key terminology, and (d) build a rapport with the expert. This

interview (as with all session with experts) is recorded on either audiotape or

videotape.

Transcribe the initial interview and analyze the resulting protocol. Create a conceptladder of the resulting knowledge to provide a broad representation of the knowledge

in the domain. Use the ladder to produce a set of questions which cover the essential

issues across the domain and which serve the goals of the knowledge acquisition

project.

Conduct a semi-structured interview with the expert using the pre-prepared questions

to provide structure and focus.

Transcribe the semi-structured interview and analyze the resulting protocol for the

knowledge types present. Typically these would be concepts, attributes, values,

relationships, tasks and rules.

Represent these knowledge elements using the most appropriate knowledge models,e.g. ladders, grids, network diagrams, hypertext, etc. In addition, document

anecdotes, illustrations and explanations in a structured manner using hypertext and

template headings.

Use the resulting knowledge models and structured text with contrived techniques

such as laddering, think aloud problem-solving, twenty questions and repertory grid to

allow the expert to modify and expand on the knowledge already captured.

Repeat the analysis, model building and acquisition sessions until the expert and

knowledge engineer are happy that the goals of the project have been realized.

Validate the knowledge acquired with other experts, and make modifications where

necessary.

Techniques have been developed to assist this, such as the use of ontologies and problem-

solving models. These provide generic knowledge to suggest ideas to the expert such asgeneral classes of objects in the domain and general ways in which tasks are performed.

This re-use of knowledge is the essence of making the knowledge acquisition process as

efficient and effective as possible.

Types of knowledge - experts, users, databases, web, standard ontologies

Shallow vs. Deep Knowledgeo Advantages and disadvantages of both

Categories of knowledge

o Declarative

o Procedural

o Metaknowledge

Methods of KA (very important topic)o Manual

Interviews

Walk through

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Unstructured best for innovation and very complex?

Structured most common, table 11.1 procedures

Process tracking or protocol analysis cognitive method

Observations (time studies)

Case analysis or SEP often best most organized

Critical incident Users rather than experts (Do you want to know the time or how to

make a watch)

Brainstorming

Prototypingo Semi-automatic

RGA

ETS (expertise transfer system)o Automatic

Machine learning

Inductive learning

Neural networks

Genetic algorithms

Multiple experts vs. one experto Almost have to use scenarios or cases

Automated rule inductiono Induction is the process of reasoning from the specific to the general

o For us, where a computer generates rules from example cases

Advantages of rule inductiono Certain, small, loosely coupled, or modular

o Exponential complexity

o Difficulties- too long a list

Knowledge verification and validation

o Critical for success

Representation of knowledge (very important)o Ontology and taxonomy words are important, handouts!!!

o Production rules

Declarative rules

Inference ruleso Semantic networks

Relationships between different concepts

Inheritanceo Frames and Objects

Heirarchy and inheritanceo Object oriented representation,

o Decision tables - popular

o Decision trees popular

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o Predicate calculus or predicate logic or propositional logic or first order

logic

Reasoning

Forward and backward chainingo Examples are very important, best method to explain concept

o Remember multi-level ruleo Real world problems in rule firing and process handout

Inference tree

Explanation important for professionals or when other factors outside of the

system can or should effect decision

Chapter 5 Expert SystemsIssues

Time of developing/implemented (expert system vs. neural networks)

Ease of use

Tools availability Explanations (can you explain the knowledge behind the results, expert yes,

neural no)

Data availability and quality

Symbolic processing important - uniquea. Numeric vs. symbolic

b. Algorithmic vs. heuristic

Heuristics intuitive knowledge or rules of thumb

Inferencing

Evolution of A.I.

a. adv. Of natural intelligence

Branches of AIa. Items we have not talked about- scene recognition, intelligent tudors,

fuzzy logic, intelligent agents (will later)

Expert s Expert System - a system to capture a portion of the experts decision-

making knowledge, codify it in a way that preserves it, and allow its effective

dissemination to user without the users needing to learn the subject area. Thebranch of artificial intelligence that develops computer programs to simulate

human decisions in many fields.

Knowledge-based expert systems - to describe a wide range of approaches todelivering knowledge in computer programs.

Heuristics - "rules of thumb" used to make a decision.

Inference Engine - a program that reads and runs decision rules (created fromheuristics)

"Inspection obvious" development tool - A tool where just looking at the tool is

enough to understand how to use it. (eg: a coke vending machine)

systems

a. What is expertise

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b. Features

c. Structure

How expert systems work

a. Rules

b. Uncertainty processing

c. Inference engined. Forward and backward

Chapter 6 Decision Support System Development1. Alternative development methodologies software is different, lack of tools

a. Prototyping many and fast

b. Throwaway prototyping every Fridayc. Agile development

d. Extreme development extreme programming (XP) major : rapid

prototyping, 2 programmers per station, note scenariose. Evolutionary or iterative development

f. Team vs. outsourcing (consultant)s vs. end user development2. Change management management issue

3. End user development why not, why cant you do that4. Uh oh focus little mistakes can add up, do you check for accuracy and

reasonableness

Collaborative

(this is a fact of life because of expertise of each discipline, but some will not) lets all

communicate better1. groupwork, discuss role of portals

2. Groupware, brainstorming hard to do over internet

3. Group support systems technologies (Lotus Notes)4. No distance learning, but discuss

Enterprise Information Systems (EIS)

yes all of them, ERP, EIS, CRM, PLM, BPM, BAM, etc.1. Why EIS

2. Critical success factors (CSF) key performance indicators (KPI) important

3. Key performance indicators,4. Exception reporting some feel this is the role of management

5. Portal market,

6. Supply chain, RFID, GPS auto7. Problems in supply chain, uncertainty, demand forecast uncertainity so what do

you do?

8. Nikes famous problem, i2,9. Future EIS systems, 1. real time, 2. easier to use, 3. adaptable to each exec.

IS over Internet

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1. Service-oriented architecture, SOA, simply stated, is a computing architecture

where application functionality is available as shared services on a network.

Services are what we know as Web services

E-Commerce

Agents

5. Agent computer programs that simulate a human relationship by doing

something that another person could do for you (Selker), behave in a manner likehuman agents e.g. travel agents, insurance agents. Can be classified by

a. Autonomous, software, intelligent, interface, virtual, informational,

mobile, etc.

b. Applications such as imaging, business process, etc.c. Notions

i. Weak notion of agency involves autonomy or the ability to

function without intervention (Wooldridge and Jennings)

ii. Strong notion of agency uses mental components such as belief,desire, intention, knowledge, etc.

6. Multi-agent systems typically distributed systems in which several distinctcomponents, each of which is an independent problem-solving agent come

together to form some coherent whole. For multi-agents systems must consider

agent modeling of its world, multi-agent planning to coordinate their behavior,

social relationships with other agents (cooperation),interactions andcommunication

7. SMART Agent Framework (dInverno and Luck, 2001)

a. Entity and environment simple collection of attributesb. Object also a collection of attributes but a more detailed description may

be given and in addition describing their capabilities. Capabilities of an

object are defined by a set of action primitives which can be performed bythe object in some environment and consequently change the

environments state.

c. Agent an object with goalsd. Autonomous agent agent with motivation, self-motivated agents in the

sense they can create and pursue their agendas as opposed to functioning

under the control of another agent. Goals generated from motivations

within not from others.8. Primitives of agent models

a. Attributes perceivable feature, simple features of the world, tree is green,

in a park, 20 feet highb. Actions discrete event that can change the state of the environment when

performed, attach tires to a car, get tire, move from one wheel to the next,

delete the attribute that the robot is at the first wheel, add the attribute thatagent is at the second.

c. Goal state of affairs to be achieved in the environment, robots goal is to

attach tire to the wheel.

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d. Motivation any desire or preference that can lead to the generation and

adoption of goals and that affects the outcome of reasoning or behavioral

task intended to satisfy those goals

Book Chapter 7 Uncertainty

Life is full of uncertainty. Even the universe and math is full of uncertainty.

Engineering of Mind, Albus and Meystel, 2001.Intelligence is the ability of a system to behave appropriately in an uncertain

environment, where appropriate behavior is that which maximizes the likelihoodof success in achieving the systems goal. Success is achievements of the goal.

Goal is inherent to intelligent behavior. A goal is something that intelligentbehavior is designed to achieve or maintain.

Higher levels of intelligence can be: Thinking ahead

Planning before acting

Reasoning about the probable results of alternative actions

Learning: modification of knowledge caused by experiences

Source: Intelligent Systems for Engineers; Adrian Hopgood, CRC Press, 2001

For example. If transducer output is low then water level is low

3 types of rule uncertainty

1. Uncertainty in the rule itself low level of water is not theonly possible explanation for a low transducer output. Maybe the

float is stuck. It is probably low.2. Uncertainty in the evidence - transducer may have failed

3. Use of vague language what is low? 1 volt, 1.5 mv, etc.

Another authors 3 types:http://www.nada.kth.se/kurser/kth/2D1431/02/lecture12.pdf

Stochastic uncertainty; example: rolling a dice

Linguistic uncertainty; example: low price, tall people, young age

Informational uncertainty; example: credit worthiness, honesty

Overall there are situations in nature that are truly random. Some can beanticipated on a statistical basis. For example the roll of a dice has a 1/6 changeof being a 2.

Uncertainty can be presented in a general way, using an estimate and a range. It

can also be presented more formally, as a "confidence interval." A "95 percent

confidence interval" should include the true value 95 percent of the time.>

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http://www.nada.kth.se/kurser/kth/2D1431/02/lecture12.pdfhttp://www.cdphe.state.co.us/rf/uncertainty.htmhttp://www.nada.kth.se/kurser/kth/2D1431/02/lecture12.pdfhttp://www.cdphe.state.co.us/rf/uncertainty.htm


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Exceptions and uncertainty are different.

Uncertain means not certain to occur, unreliable.

Exception is a case to which a rule does not apply. An occurrence

that is not normal and usually unexpected.

Random elements with probability of occurrence

One way to think about is that uncertainty (the unknown future) causes exceptions

(actual situations not normal and/or not seen before).

In a practical sense, we can sometimes transform or handleexceptions/uncertainty by:

1. Uncertainty avoidance, gathering more information that

reduces uncertainty (listed in an earlier chapter)2. Ignore, just too hard

3. Identifying all possible things that can happen and then

develop a plan for each. (making it deterministic)Risk assessmentand management

4. Develop higher level or more intelligent rules that deal

when exceptions caused by uncertainty occur

5. Probabilistic methods such as:

1. Probability ratios2. Bayesian(extensions, updating) based upon

probability theory, requires independence between different

pieces of evidences3. Demster-Shafer theory of evidence, belief functions

4. Certainty theory less rigorous than Bayesian

Certainty factors, disbelief5. Hidden Markov chains6. Stochastic programming, plan on randomness

6. Knowledge engineering or applied A.I. techniques:

Case based reasoning similarity

Neural networks learning as you go

Fuzzy logic or possibility theory- for vague language type

#37. Others:

Fancy Logics (default logic, modal logic, etc.)

Monte Carlo statistical simulation

Markov Chains depends upon the value of thenumber at the previous time

Representing uncertainty

Numeric - %, many prefer ranking, influence diagrams

Symbolic Likert scale

1. Influence diagrams look at figure

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2. Spreadsheets

3. Decision tables and trees **

4. LP and more OR you should know this5. Simulation, especially when to use

6. Visual interactive randomness in video games

Knowledge management Definitions and Outline

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