intelligent systems and business...
TRANSCRIPT
Intelligent Systems and
Business Intelligence
Sang Hoe Koo
Korea University Sejong Campus
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Overall Outline
Part I Artificial
Intelligence
Part II Database and Data
warehouse
Part III Business
Intelligence
(Data Mining)
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Outline of AI
1. AI and Intelligent Systems
2. Search and Knowledge
1. Problem Solving using Search
2. Knowledge Representation
3. Reasoning and Learning
1. Reasoning Systems
2. Learning Systems
4. Business Applications
1. Intelligent Agent
2. Business Rule Engines
3. Data Mining
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Artificial Intelligence
1. AI AND INTELLIGENT SYSTEMS
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Artificial Intelligence
Definition
◦ Artificially generated intelligence
Science View
◦ The study of the principles and mechanisms whose application
can result in intelligent action. (What is intelligence?)
Engineering View
◦ The study of ways to make our machines perform better using
intelligence
◦ Emphasis on applications to machines
Goal of AI
◦ How to make computers do things which, at the moment,
people do better
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Intelligent systems architecture
◦ self-modifying (learning) problem solving (reasoning,
decision making) systems
◦ intelligence? knowledge? learning?
Intelligent Systems
Learning
Module
Perception
Module
Action
Module
Reasoning
Module (decision making)
Memory
Module (knowledge)
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Intelligent Systems
Intelligent systems characteristics
◦ Goal-oriented behavior
Be a millionaire, become less hungry.
◦ Perception/action loop
Perception: vision, auditory, smell, touch, language, ports
Action: hands, legs, speaking, ports
◦ Reasoning
Make decisions in service of goals based on knowledge
Should I invest in the stock market or rob a bank?
Should I eat an orange or a steak?
Examples: Rule-based systems, artificial neural networks
◦ Learning
Self-modification: acquire new knowledge
Machine Learning: various machine learning methods
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Reasoning Systems
Reasoning systems requires:
◦ knowledge representations and reasoning algorithms
Knowledge representations
◦ predicate logic(술어논리학), decision tree, neural network,
cases, semantic net
Reasoning algorithms
◦ forward chaining/backward chaining, top-down tree traversal,
back propagation, case-based reasoning, intersection search, etc.
Practical reasoning systems
◦ Rule-based systems (IF/THEN rules)
◦ Decision trees
◦ Artificial neural networks
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Architecture
Example
Rules: if animal(x) then mortal(x)
if human(x) then animal(x)
Facts: human(TOM)
IE infers: animal(TOM) and mortal(TOM)
Cases
◦ CMU Mycin medical expert system
◦ CA’s Aion
Rule-Based Systems
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Facts
Rules
IE Inferred facts
Actions
Decision Tree
Decision trees
humidity
outlook
humid dry windy
wind
no-wind
sunny rain cloudy
y n y n y
Knowledge on the weather Tom likes
Knowledge on loan approval
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Artificial Neural Networks
Rules ? ANN !!
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Artificial Neural Networks
Computer architecture inspired by neuron architecture
◦ Suited for non-structured pattern matching problems such as
character recognition, voice recognition, stock forecasting, etc.
◦ A stock forecasting program example based upon Perceptron
주가 상승?
경상수지비율 총자본회전율 고정비율
Input Layer
Hidden Layer
Output Layer
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부채 비율
Learning Systems
Learning systems requires:
◦ knowledge representations and learning algorithms
◦ Increases or modifies knowledge base
Usually closely related with reasoning algorithm
◦ Logic – increases (if-then) rules or facts
◦ Decision trees – construct decision trees
◦ Neural network – construct (or adjust weights on connections)
neural networks
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AI History
60+ years old (Alan Turing, Turing test)
General Problem Solvers using Search
◦ general knowledge
Expert Systems
◦ with deep but narrow domain knowledge
◦ mostly use rule based systems
Neural Network
◦ brain-like intelligent systems
Intelligent Agent
◦ solving practical problems, autonomy, mobility, sociality
Humanoid Robots
◦ human-like electro mechanical intelligent information systems
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Fields of AI
Knowledge representations and reasoning: formalisms for capturing and using knowledge.
Problem solving using search: strategies for achieving goals.
Machine Learning
Natural Language Processing: understanding and generating written or spoken language.
Robotics: computer vision, robot arms, walking.
Artificial Neural Network: brain-like architecture, useful for pattern based reasoning.
Expert systems: intelligent systems for intellectual tasks.
Intelligent Agents: very practical AI applications.
Business Intelligence: intelligent business decision making using techniques such as data mining, business analytics, information visualizations, etc.
Business Rule Engines : business applications for business rules
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Relationships to other fields
Computer Science: Software involving computer languages,
development tools, and algorithms.
Linguistics: NLP
Electric Engineering: Hardware - machines for perception, action,
robotics, neural nets.
Mechanical Engineering: Robotics (arms and legs).
Neuroscience: neural nets, vision, image understanding.
Operations Research: optimization (LP), search techniques, etc.
Philosophy: logic, philosophy of mind, etc.
Mathematics: logic, theorem proving, etc.
Psychology: cognitive psychology, psycholinguistics(심리 언어학), etc.
Cognitive Science: AI is one part (neuroscience, philosophy, psychology,
linguistics are also their parts).
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DSS vs. ES?
General Programs and Intelligent Systems
+ Intelligence
(Reasoning and Learning) General
Programs
Intelligent
Systems
IS
MIS OIS
EIS DSS IRS OAS PCS TPS
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Summary
Artificial Intelligence
Intelligent Systems
◦ Components
Reasoning Systems
◦ Rule based systems, Decision Trees, ANN
Learning Systems
◦ Rule based systems, Decision Trees, ANN
AI Fields
IT문명세상을 바꾸다2부 – 인간의 아바타, 컴퓨터 (EBS 다큐프라임)
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