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Artificial Intelligence

Artificial Intelligence. Dr. Paul Wagner Department of Computer Science University of Wisconsin – Eau Claire. Messages. Artificial Intelligence (AI) is an interesting sub-field of computer science that provides many contributions to the overall field

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Artificial Intelligence

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  1. Artificial Intelligence Dr. Paul Wagner Department of Computer Science University of Wisconsin – Eau Claire

  2. Messages • Artificial Intelligence (AI) is an interesting sub-field of computer science that provides many contributions to the overall field • CS 420, as the AI course at UWEC, is a good opportunity to begin to explore these issues

  3. Outline • Overview • AI Topics • Knowledge representation • Problem solving and search space manipulation • Planning • Learning • Communicating • Uncertainty • Intelligent agents • Robotics • AI Languages • MICS Robot Contest Video

  4. Overview of Artificial Intelligence • Definitions – four major combinations • Based on thinking or acting • Based on activity like humans or performed in rational way

  5. AI Definitions • Acting Humanly • Turing Test – computer passes test if a human interrogator asking written questions can distinguish written answers from computer or human • Computer needs: • Natural language processing • Knowledge representation • Automated reasoning • Machine learning

  6. AI Definitions (2) • Total Turing Test – includes video component (to test subject’s perceptual abilities) and opportunity to pass physical objects to subject • Computer also needs: • Computer vision • Robotics

  7. AI Definitions (3) • Thinking Humanly • Cognitive Modeling approach to AI • Involves crossover between computer science and psychology – cognitive science • Areas of interest • Cognitive models • Neural networks

  8. AI Definitions (4) • Thinking Rationally • “Laws of thought” approach to AI • Goal: solve any problem based on logical manipulation • Problems • Difficult to represent certain types of knowledge (e.g. common sense, informal knowledge) • Difference between solving problems in principle and in practice • E.g. computational limits

  9. AI Definitions (4) • Acting Rationally • “Design a rational agent” approach to AI • Advantages over logic approach • Logic is only one tool or many that can be used to design rational agent • Scientific advances can provide more tools for developing better agents

  10. Knowledge Representation • How to represent information? • Generally, we use some sort of tree, grid or network • Options • OO programming languages: classes/objects • Relational database system: tables/rows/columns • Problem • The world is more varied, with many types of things to represent

  11. Knowledge Representation (2) • Abstract Objects • Sets • Sentences • Measurements • Times • Weights • Generalized Events • Intervals • Places • Physical Objects • Processes

  12. Knowledge Representation (3) • Some things are very difficult to represent • Common sense • See http://www.cyc.com/ • Combinations of multiple types • Issues of: • Type • Scale • Granularity • Combination • Other Questions • How to distinguish knowledge and belief? • What is the best way to reason with this information?

  13. Problem Solving and Search Space Manipulation • Many Algorithmic Approaches to Problem Solving • Depth-First Search • Breadth-First Search • Variations • Depth-Limited Search • Iterative Deepening Depth-First Search • Bi-directional Search

  14. Problem Solving and Search Space Manipulation (2) • Smarter Search • Greedy best-first search • A* search (combine costs of path so far plus path from current node to goal) • Memory-bounded heuristic search • Heuristic – means of estimating a measurement such as cost of search

  15. Problem Solving and Search Space Manipulation (3) • Issues • Avoiding repeated search • Searching with partial information

  16. Problem Solving and Search Space Manipulation (4) • Adversarial Search • E.g. games and game trees • Minimax algorithm • Alpha-Beta pruning

  17. Problem Solving and Search Space Manipulation (5) • Applications of Problem Solving • Expert Systems • Approximating the functionality of an absent human expert • Robotics • Encountering unexpected obstacles

  18. Planning • Many types of problems • “Blocks world” • Getting yourself from Eau Claire to the AAAI conference in Boston • Changing a flat tire • Completing all of your projects at the end of the semester • Developing a large software application

  19. Planning (2) • Approaches • State-based search • Partial-order planning • Planning graphs • Issues • Time • Scheduling • Resources

  20. Learning • Definition - Building on current knowledge by using experience to improve a system • Various approaches • Supervised/unsupervised/reinforcement • Forms of learning algorithms • Inductive logic • Example: given a set of point, approximate a line • Decision tree (set of questions, act differently depending on answer)

  21. Learning (2) • Issues • Computational Learning Theory • Intersection of theoretical CS, AI, statistics • How many examples do you need?

  22. Communicating • Major issue - Natural language processing • Many issues • Syntax • Semantics • Context • Steps • Perception • Parsing • Analysis • Disambiguation • Incorporation

  23. Uncertainty • Much knowledge is not absolute • Boundary between knowledge and belief is gray • Techniques for dealing with uncertainty • Probabilistic reasoning • Probabilistic reasoning over time • Fuzzy sets / fuzzy logic • Simple decision-making (evaluating utility) • Complex decision-making (taking ability to reevaluate into account) • Applications • Expert systems

  24. Intelligent Agents • Everything we’ve talked about can be viewed in terms of embedding intelligence within an agent • Software system • Machine with embedded software • Robot

  25. Intelligent Agents (2) • Issues for agents • Limitations on memory • Perceiving its environment • Working with other agents • Affecting its environment (through actuators) • Processes • Simple – based on rules • Complex – based on multiple pieces of logic, dealing with uncertainty

  26. Robotics • Field encompassing elements of computer science/AI, engineering, physical systems • Issues • Many that we’ve discussed, plus: • Perception • Actuation • Recent successes • Worker bots (e.g. floor cleaners) • Intelligent navigation (DARPA vehicle contest) • Test environments • Lego Mindstorms • Other robot packages or custom systems

  27. AI Languages • Scheme / LISP • Functional • Simple knowledge representation (list) • Easy to apply functionality to represented elements • Prolog • Logic-based • Facts and rules easily represented • Built-in search engine • Specialized languages • Rule languages (e.g. CLIPS) • Planning languages (e.g. STRIPS)

  28. CS 420 • Spring semester, about every other year • Will be offered Spring 2007 • Prerequisite: CS 330 (to get Scheme and Prolog background) • Topics • All of the above!

  29. CS 420 (2) • Possible Projects • Neural network to simulate decision making, natural language processing • Software development planning through cooperating intelligent agents • Expert system for deciding which courses to take to complete a CS major • Sumo robots?

  30. MICS Robot Contest Video • http://video.google.com/videoplay?docid=7851913746457357108&hl=en

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