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 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 CS 420, as the AI course at UWEC, is a good opportunity to begin to explore these issues

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

4. Overview of Artificial Intelligence Definitions – four major combinations Definitions – four major combinations –Based on thinking or acting –Based on activity like humans or performed in rational way Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally

5. AI Definitions Acting Humanly 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 Natural language processing Knowledge representation Knowledge representation Automated reasoning Automated reasoning Machine learning 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 Computer vision Robotics Robotics

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

8. AI Definitions (4) Thinking Rationally 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) Difficult to represent certain types of knowledge (e.g. common sense, informal knowledge) Difference between solving problems in principle and in practice Difference between solving problems in principle and in practice –E.g. computational limits

9. AI Definitions (4) Acting Rationally 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 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 Scientific advances can provide more tools for developing better agents

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

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

12. Knowledge Representation (3) Some things are very difficult to represent Some things are very difficult to represent –Common sense See http://www.cyc.com/ See http://www.cyc.com/http://www.cyc.com/ –Combinations of multiple types Issues of: Issues of: –Type –Scale –Granularity –Combination Other Questions 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 Many Algorithmic Approaches to Problem Solving –Depth-First Search –Breadth-First Search Variations Variations –Depth-Limited Search –Iterative Deepening Depth-First Search –Bi-directional Search

14. Problem Solving and Search Space Manipulation (2) Smarter Search 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 Heuristic – means of estimating a measurement such as cost of search

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

16. Problem Solving and Search Space Manipulation (4) Adversarial Search 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 Applications of Problem Solving –Expert Systems Approximating the functionality of an absent human expert Approximating the functionality of an absent human expert –Robotics Encountering unexpected obstacles Encountering unexpected obstacles

18. Planning Many types of problems 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 Approaches –State-based search –Partial-order planning –Planning graphs Issues Issues –Time –Scheduling –Resources

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

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

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

23. Uncertainty Much knowledge is not absolute Much knowledge is not absolute –Boundary between knowledge and belief is gray Techniques for dealing with uncertainty 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 Applications –Expert systems

24. Intelligent Agents Everything we’ve talked about can be viewed in terms of embedding intelligence within an agent 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 Issues for agents –Limitations on memory –Perceiving its environment –Working with other agents –Affecting its environment (through actuators) Processes 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 Field encompassing elements of computer science/AI, engineering, physical systems Issues Issues –Many that we’ve discussed, plus: –Perception –Actuation Recent successes Recent successes –Worker bots (e.g. floor cleaners) –Intelligent navigation (DARPA vehicle contest) Test environments Test environments –Lego Mindstorms –Other robot packages or custom systems

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

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

29. CS 420 (2) Possible Projects 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?doc id=7851913746457357108&hl=en http://video.google.com/videoplay?doc id=7851913746457357108&hl=en http://video.google.com/videoplay?doc id=7851913746457357108&hl=en http://video.google.com/videoplay?doc id=7851913746457357108&hl=en