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MD 240 Intelligent Support Systems

Agenda. BackgroundArtificial IntelligenceNeural ComputingExpert SystemsFuzzy LogicIntelligent AgentsNatural Language Processing

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MD 240 Intelligent Support Systems

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    1. MD 240 Intelligent Support Systems

    2. Agenda Background Artificial Intelligence Neural Computing Expert Systems Fuzzy Logic Intelligent Agents Natural Language Processing & Voice Technology Computer Vision Collaborative Filtering

    3. 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

    4. 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)

    5. Artificial Intelligence

    6. Artificial Intelligence What 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)

    7. Artificial Intelligence Thought 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

    8. Artificial Intelligence Objectives of AI To understand what intelligence is To make machines smarter To make machines more useful

    9. Artificial Intelligence Why These Objectives? Computers are “stupid” Inflexible Rule-following Mechanical “Intelligent” computers Flexible Creative

    10. Natural Intelligence & Behavior What 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

    11. Artificial vs. Natural Intelligence AI 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”

    12. Artificial vs. Natural Intelligence Natural 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

    13. Commercial AI Technology Business (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

    14. Commercial AI Technology Business (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

    15. 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

    16. 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

    17. Neural Computing

    18. Neural Computing Artificial 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

    19. 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 (#)

    20. 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 (#, … , #)

    21. Neural Computing Potential 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.

    22. Neural Computing Building 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!)

    23. Neural Computing Neural Network Example

    24. Expert Systems

    25. 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)

    26. Expert Systems Basic Architecture

    27. Expert Systems Basic Architecture

    28. Expert Systems Basic Architecture

    29. Expert Systems How 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”

    30. Expert Systems How 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!)

    31. Expert Systems Knowledge Engineering

    32. Expert Systems Development 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

    33. Expert Systems Managerial 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?

    34. Fuzzy Logic

    35. 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.

    36. Intelligent Agents (IA)

    37. 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

    38. Intelligent Agents Major Tasks They May Perform Information access and navigation Decision support and empowerment Repetitive office activity Mundane personal activity Search and retrieval Domain experts

    39. Intelligent Agents Possible 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

    40. Intelligent Agents Examples 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

    41. Intelligent Agents Examples 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)

    42. Intelligent Agents The Future: Networks of Agents Distributed Intelligent Agents … “Multi-Agent Systems” “Agents that Interact” “Distributed Systems of Agents”

    43. Intelligent Agents The 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)

    44. Intelligent Agents The 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

    45. Natural Language Processing and Voice Technology

    46. Natural Language Processing Advantages of Speech Recognition Ease of access Speed Manual freedom Remote access Accuracy

    47. Natural Language Processing Classes 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)

    48. Computer Vision

    49. Computer Vision Scene 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)

    50. Collaborative Filtering

    51. 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”)

    52. 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

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