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MD 240 Intelligent Support Systems Agenda Background Artificial Intelligence Neural Computing Expert Systems Fuzzy Logic Intelligent Agents Natural Language Processing & Voice Technology Computer Vision Collaborative Filtering Background General objective of AI: Machine Learning

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agenda
Agenda
  • Background
  • Artificial Intelligence
  • Neural Computing
  • Expert Systems
  • Fuzzy Logic
  • Intelligent Agents
  • Natural Language Processing & Voice Technology
  • Computer Vision
  • Collaborative Filtering
background
Background
  • General objective of AI: Machine Learning
    • How can we construct computer programs (embedded in machines) that automatically improve with experience?
    • This is a problem of interest to many disciplines
      • AI: symbolic representation of knowledge, learning
      • Statistics: Bayes’ methods, forecasting error
      • Philosophy
      • Psychology/Neurobiology: how does learning improve?
      • Control Theory: control processes to meet pre-defined objectives
      • Computational Complexity Theory: computational bounds for the complexity of learning tasks
      • Information Theory: measures of information, learning approaches
    • You will find much overlap across these disciplines
background4
Background
  • Artificial
    • “made by work or art, not by nature”
  • Intelligence
    • understanding natural language, having the ability to process even potentially ambiguous messages
    • recognizing speech
    • seeing, hearing, tasting, touching, smelling
    • learning new concepts or procedures
    • planning an itinerary
    • having common sense

(Bronzino and Morelli, Expert Systems, 1989)

artificial intelligence what is artificial intelligence
Artificial IntelligenceWhat is Artificial Intelligence?
  • Artificial Intelligence is not easily defined
  • Even AI “experts” cannot agree
    • “Artificial Intelligence is the science of making machines do things that would require intelligence if done by men.” (M. Minsky, The Society of Mind, 1985)
    • “Artificial Intelligence is … concerned with designing intelligent computer systems, that is, systems that exhibit characteristics we associate with intelligence in human behavior.” (A. Barr and E. A. Feigenbaum, The Handbook of Artificial Intelligence, 1982)
    • “Artificial Intelligence is the study of intelligent behavior. One of its goals is to understand human intelligence. Another is to produce useful machines.” (Garnham, Artificial Intelligence, 1987)
artificial intelligence thought processes and intelligent behavior
Artificial IntelligenceThought Processes and Intelligent Behavior
  • Artificial intelligence (AI) is concerned with two basic ideas
    • studying the thought processes of humans
    • representing those thought processes via machines
      • computers
      • robots = machines with embedded “intelligent” computing capabilities that can “sense” their environment, and “respond” appropriately
artificial intelligence objectives of ai
Artificial IntelligenceObjectives of AI
  • To understand what intelligence is
  • To make machines smarter
  • To make machines more useful
artificial intelligence why these objectives
Artificial IntelligenceWhy These Objectives?
  • Computers are “stupid”
    • Inflexible
    • Rule-following
    • Mechanical
  • “Intelligent” computers
    • Flexible
    • Creative
natural intelligence behavior what are humans good at
Natural Intelligence & BehaviorWhat are Humans Good At?
  • Uniquely Human Capabilities
    • Learning or understanding from experience
    • Making sense of ambiguous or contradictory messages
      • Hand-counting paper ballots in Florida
    • Responding to new situations quickly and successfully
    • Using reason to solve problems and direct actions effectively
    • Dealing with complex situations
    • Applying knowledge to manipulate the environment
    • Recognizing the relative importance of different elements in a situation
artificial vs natural intelligence ai advantages over natural intelligence
Artificial vs. Natural IntelligenceAI Advantages over Natural Intelligence
  • AI is more permanent
    • Human intelligence encoded in a machine
  • AI offers ease of duplication and dissemination
    • Duplicate human intelligence into many machines
  • AI can be less expensive
    • Once you model and store human intelligence, you don’t have to pay humans who hold that intelligence any more
  • AI is consistent and thorough
  • AI can be documented
    • A computer program holds the “intelligence”
artificial vs natural intelligence natural intelligence advantages over ai
Artificial vs. Natural IntelligenceNatural Intelligence Advantages over AI
  • Natural intelligence is creative
    • Humans can “think outside of the box” … machines process inside some boundaries of their accumulated “intelligence”
  • Natural intelligence enables people to benefit from and directly use sensory experiences
  • Natural intelligence enables people to recognize relationships between things, sense qualities, and spot patterns that explain how various items interrelate
  • Human reasoning is always able to make use of a context of experiences
commercial ai technology business mis considerations
Commercial AI TechnologyBusiness (MIS) Considerations
  • Potential Benefits of AI/ES Applications
    • Increased output and productivity
    • Increase quality
    • Potential to capture and disseminate scarce expertise
    • Operation in hazardous environments
    • Wider accessibility of knowledge
commercial ai technology business mis considerations14
Commercial AI TechnologyBusiness (MIS) Considerations
  • Potential Benefits of AI/ES Applications
    • Reliability
    • Increased capabilities of other computerized systems
    • Ability to work with incomplete or uncertain information
    • Ability to provide training to humans
    • Enhanced problem solving capabilities
    • Decreased decision making time
commercial ai technology
Commercial AI Technology
  • Expert systems (ES)
  • Natural language processing
  • Robotics and sensory systems
  • Computer vision and scene recognition
  • Intelligent computer-aided instruction (ICAI)
  • Handwriting recognizers
ai technologies
AI Technologies
  • AI Substituting for the Brain
    • Brain cell activities … Neural Networks
    • Human reasoning … Expert Systems, Fuzzy Logic
    • Intelligent behavior … Intelligent Agents
  • AI Substituting for Sensory Organs
    • Eyes … Computer Vision
    • Eyes … Natural Language Processing
    • Speech … Natural Language Processing
  • AI Substituting for Social Behavior/Groups
    • Social group activities … Collaborative Filtering
neural computing artificial neural networks ann
Neural ComputingArtificial Neural Networks (ANN)
  • ANNs provide a practical method for learning
    • discrete-valued functions
      • boolean (0/1) functions can be represented
    • real-valued functions
      • bounded continuous functions can be estimated arbitrarily closely
    • vector-valued functions
      • any function can be approximated to arbitrary accuracy
artificial neural networks ann business applications
Artificial Neural Networks (ANN)Business Applications
  • Discrete functions
    • Tax fraud - identify irregularity (Y/N)
    • Financial services - identify trading pattern (Y/N)
    • Loan applications evaluation - judge worthiness of loan (Y/N)
    • Solvency prediction - will corporation fail? (Y/N)
    • Credit-card fraud detection - is a purchase fraudulent? (Y/N)
    • New product analysis - will market segment accept product? (Y/N)
  • Real-valued functions
    • Financial services - forecast foreign exchange rates (#)
    • New product analysis - sales forecasting (#)
    • Foreign exchange rate - forecast risk rating (#)
    • Stock, bond, commodities selection and trading - estimate IPO price (#)
artificial neural networks ann business applications20
Artificial Neural Networks (ANN)Business Applications
  • Vector-valued functions
    • Airline fare management - seat demand & crew scheduling (#,#)
    • Evaluation of personnel and job candidates - based on characteristics, estimate requirements and performance (#,#)
    • Resource allocation - choose multiple inputs to maximize multiple outputs (#,#)
    • Data mining
    • Signature validation - match shapes of signatures (#, … , #)
    • Face identification - match shapes of faces (#, … , #)
neural computing potential benefits
Neural ComputingPotential Benefits
  • Provides some human characteristics of problem solving
    • Recognition of patterns and characteristics
  • ANNs may provide characteristics of human “neural networks”
    • Fault-tolerance
      • If you damage or kill a few brain cells, the others should adapt and still solve problems successfully
    • Generalization
      • Reasonable inputs (generalizable to inputs of ANN) lead to reasonable outputs
    • Adaptability
      • New cases (inputs, outputs) can lead to retraining of network to update and improve decision making model
    • Forecasting
      • Assume historical process correct. Obtain future input. Predict future output.
neural computing building neural networks
Neural ComputingBuilding Neural Networks
  • Build ANN program to represent neurons
    • Input layer (sensory layer)
    • Middle “hidden” layer(s) - discrete/real func.: # =1, arbitrary func.: # = 2
    • Output layer (decision or solution layer … “respond”)
  • Train ANN: use historical data set to “train” system on how to make decisions
    • Actual inputs (e.g. consumer credit information)
    • Actual outputs (e.g. did consumer default on loan?)
    • Each hidden “neuron” contains an estimated function
  • Use of “trained” NN system
    • Real input data (e.g. consumer credit application)
    • Hidden layer estimates a value, which determines output
    • Real decision output (e.g. Greg’s credit is DENIED!)
neural computing neural network example
Neural ComputingNeural Network Example

1

1

Input Hidden Values

10000000 .89 .04 .08

01000000 .15 .99 .99

00100000 .01 .97 .27

00010000 .99 .97 .71

00001000 .03 .05 .02

00000100 .01 .11 .88

00000010 .80 .01 .98

00000001 .60 .94 .01

* after 5000 training examples

** the hidden values encode an estimate of the standard binary encoding for eight distinct values

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Hidden

Layer

Input

Output

expert systems es definitions
Expert Systems (ES)Definitions
  • What is an Expert System?
    • An Expert System is decision-making software that can reach a level of performance comparable to - or even exceeding that of - a human expert in some specialized problem area (Turban, et al., 2000)
  • What do they do?
    • “Expert systems attempt to mimic the expert’s knowledge and problem solving ability …[and] to assist human decision-making in a wide variety of ways, including advising, consulting, designing, diagnosing, explaining, forecasting, learning, monitoring, and tutoring.” (Bronzino and Morelli, 1989)
expert systems basic architecture
Expert SystemsBasic Architecture

Knowledge

Database

User

Interface

Inference

Engine

expert systems basic architecture27
Expert SystemsBasic Architecture

Knowledge Database

(Knowledge Base)

contains

axioms (facts, truths,

representations of knowledge)

theorems (logically derived facts)

logical derivation

(proofs)

expert systems basic architecture28
Expert SystemsBasic Architecture

Inference Engine

problem solving mechanisms

general knowledge (non-domain specific) needed to solve problems

expert systems how they work
Expert SystemsHow They Work
  • Knowledge representation (rule based)
    • Declarative knowledge (i.e., facts)
      • If [two individuals have the same father], then [they are brothers]
    • Procedural knowledge (i.e., typical procedures)
      • If [engine won’t turn over after using key], then [check the battery]
    • Strategic knowledge (i.e., strategies for approaching problem)
      • If [goal is to start car], then [start by inserting and turning key]
    • Heuristic knowledge (i.e., judgmental heuristics used by humans)
      • If [goal is to prevent contraband], then [“spot check” passenger luggage]
        • Heuristic: “spot check”
expert systems how they work30
Expert SystemsHow They Work
  • Knowledge inference
    • Predicate Logic/Sentential Logic/Propositional Logic
      • Sentences/propositions have a truth value … they are either “T” or “F”
      • Modus Ponens
        • A then B (is a true statement) = “IF A, THEN B”
        • A (is a true statement)
        • Therefore B (is true, according to truth tables for “A then B”)
    • Example
      • If [two individuals have the same father], then [they are brothers]
        • “Joe is male”
        • “Jim is male”
        • “Jane is female”
        • “Tim is the father of Joe”, “Tim is the Father of Jim”, “Tim is the father of Jane”
          • DERIVED FACT: “Joe and Jim are brothers” (Sounds good!)
          • DERIVED FACT: “Jane and Jim are brothers” (OOPS!)
expert systems knowledge engineering
Expert SystemsKnowledge Engineering

Knowledge Acquisition

with Domain Expert

Conceptualize Expert System

Preliminary Model

of

Expert System

Test & Debug

Formalize & Implement

Prototype

of

Expert System

Feedback?

End User Uses

expert systems development tools
Expert SystemsDevelopment Tools
  • General Purpose Languages
    • Procedural: C, Pascal, PL/1, Fortran
    • Symbolic: LISP, Scheme
  • AI Languages
    • Object-Oriented: Smalltalk, Actor
    • Logic Programming: Prolog
  • Expert Systems Shells
    • EXSYS, MacSmarts, Emycin, KEE

Very flexible, but must build components from ground up

Built-in capabilities …

knowledge representation schemes, inference mechanisms, interface features, debugging tools, etc.

expert systems managerial implications
Expert SystemsManagerial Implications
  • Upshots
    • Expert Systems would be valuable to ISD, operations, upper-level management, etc.
    • Representing and storing knowledge is HARD
    • Knowledge engineering is HARD … evolutionary
    • Management decisions about ES are complex
      • Costs vs. Benefits … How to justify?
        • The time of an EXPERT is valuable
        • Using their time to build ES is presumably expensive
      • Expectations … How not to Over-Sell?
      • Acquiring Knowledge … How to get Experts to participate?
      • ES Acceptance … How to get End User to use ES?
      • ES Integration … How to integrate ES into legacy MIS?
fuzzy logic35
Fuzzy Logic

Fuzzy logic deals with uncertainties by simulating the process of human reasoning, allowing the computer to behave less precisely and logically than conventional computers do.

intelligent agents ia37
Intelligent Agents (IA)
  • Intelligent Agents
    • software entities
    • carry out some set of operations on behalf of a user or another program
    • with some degree of independence
    • in so doing, employ some knowledge or representation of the user’s goals
intelligent agents major tasks they may perform
Intelligent AgentsMajor Tasks They May Perform
  • Information access and navigation
  • Decision support and empowerment
  • Repetitive office activity
  • Mundane personal activity
  • Search and retrieval
  • Domain experts
intelligent agents possible characteristics
Intelligent AgentsPossible Characteristics
  • Autonomous - acting on its own, goal-oriented
  • Proactive response - taking the initiative
  • Unobstructive - works by itself without constant monitoring
  • Module - transportable across systems & networks
  • Dedicated & automated - designed for specific task
  • Interactive - works with humans or software
  • Conditional processing - make decision based on context
  • Friendly & dependable
  • Able to learn - observe end user and predict end user’s actions, sense environment and respond
intelligent agents examples of ia applications
Intelligent AgentsExamples of IA Applications
  • User interface
    • Microsoft Agent
  • Operating systems agents
    • Windows NT wizard
  • Application agents
    • The happy paperclip!
    • MS Excel wizards, MS Word wizards, MS Access wizards
intelligent agents examples of ia applications41
Intelligent AgentsExamples of IA Applications
  • Workflow and administrative management agents
  • Software development
    • suggesting changes to computer program code
  • Discovery and negotiation in electronic commerce
    • Boo.com - shopping “agent”
    • automated product finding,
    • date finding (Friendfinder.com)
    • employment discovery (Monster.com)
    • airline ticket bots that intelligently search all major airlines (QIXO.com, SideStep.com)
intelligent agents the future networks of agents
Intelligent AgentsThe Future: Networks of Agents
  • Distributed Intelligent Agents …
    • “Multi-Agent Systems”
    • “Agents that Interact”
    • “Distributed Systems of Agents”
intelligent agents the future networks of agents43
Intelligent AgentsThe Future: Networks of Agents
  • Why?
    • Technological and application needs
    • Just seems natural … intelligence and interaction are deeply coupled
    • Speed-up and efficiency
      • IAs can operate both alone and together … whichever is appropriate
    • Robustness and reliability
      • One or several IAs may fail, the system will still work (similar to ANN)
    • Scalability and flexibility
      • easily add IAs to scale system, each individual IA still functions with same flexibility
    • Costs
      • use many low cost IAs to do same job as one expensive IA
    • Development and reusability
      • easier development and redevelopment of IA … reuse IAs

(G. Weiss, Multiagent Systems, 1999)

intelligent agents the future networks of agents44
Intelligent AgentsThe Future: Networks of Agents
  • Applications
    • Electronic commerce … IAs represent buyer/seller
    • Monitoring/management of telecommunication networks
      • Groove.net - identifying group members online
    • Optimization of transportation systems
    • Optimization of industrial manufacturing processes
      • Holonic flexible manufacturing systems - scheduling resources
    • Analysis of business processes within/between businesses
    • BBN is building networks of NASA probes for exploring the surface of planets
natural language processing advantages of speech recognition
Natural Language ProcessingAdvantages of Speech Recognition
  • Ease of access
  • Speed
  • Manual freedom
  • Remote access
  • Accuracy
natural language processing classes of technologies
Natural Language ProcessingClasses of Technologies
  • Speech to Text
    • Speech (voice) recognition and understanding
      • Dragon’s Naturally Speaking
  • Speech to Computer Command
    • Cell phone voice commands
  • Text to Speech
    • Voice synthesis
      • Scan-Soft’s Omni Page - voice readback of OCR results
  • Text to Text
    • Translation software
      • AltaVista’s Babelfish (babelfish.altavista.com)
computer vision scene recognition
Computer VisionScene Recognition
  • Optical Character Recognition (OCR)
    • Scan page as a bitmap (black and white picture elements)
    • Translate bitmap picture into an ASCII file of characters
    • Scan-Soft’s Omni Page
  • Sensing Visual Field
    • Eagle-Eye Project (BC)
      • sense eye movement of handicapped, translate into computer commands
    • NASA’s Mars probe
      • sense rocks, adjust movement of probe
  • Toys
    • Lego Mindstorms (mindstorms.lego.com)
collaborative filtering51
Collaborative Filtering
  • Collaborative filtering
    • Social “intelligence”
      • Many of us behave similarly, due to having similar preferences
        • Human minds have ability to infer tastes from wardrobe and prior tastes
        • “He’s a punk rocker. He’s weird. He must like The Ramones, The Replacements, Husker Du, and Nirvana.”
      • If we can identify behaviors, we can help individuals find things of use to them
    • Usually related to human tastes
      • Books -- preferences for certain authors
      • Music -- tastes in music
      • Movies -- tastes in entertainment
      • News -- tastes in stories
      • Restaurants -- tastes in food
      • Website Pages -- tastes in information content
      • Cross-Selling -- common related purchases (“complements”)
collaborative filtering52
Collaborative Filtering
  • Automated Collaborative Filtering
    • Provides recommendations and disrecommendations
    • Based on statistical matches between peoples’ stated or actual (implied) tastes
      • Stated: “Rate this movie from 1-7, 1=really hated, 7=loved”
      • Actual/Implied: “People who bought this book also bought these”
    • Technology
      • Database of records for individuals
        • Fields are ratings of items (books, movies, Web documents, etc.) they have rated
      • Vectors of your ratings are compared to the vectors provided by other users
      • People with similar opinions can be discovered