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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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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