Fundamentals of AI Introduction

1. Fundamentals of AIIntroduction

2. Overview • Syllabus • Grading • Topics • What is AI? • Four competing views • Agents • Course Goals • Summary COSC 159 - Fundamentals of AI

3. Syllabus • Instructor information • Prerequisites • Programming Languages • Textbook • Russell and Norvig, AIMA, 2nd Edition • Attendance COSC 159 - Fundamentals of AI

4. Grading • Qualities of good work • Communication • Correctness • Validation • Comparison • Efficiency • Your work will be graded on all aspects COSC 159 - Fundamentals of AI

5. Topics Covered • Definitions of AI • Agents • Problem representation and solving • Searching, heuristics, optimization • Knowledge representation and reasoning • Logic • Planning problems • Uncertainty • Learning • More topics if we have time COSC 159 - Fundamentals of AI

6. What is AI? • Understand and build intelligent entities • Artificial refers to building entities • What is intelligence? • Understand and build an entity emulating a human? • Understand and build an entity that is rational? COSC 159 - Fundamentals of AI

7. Rationality • An ideal concept of intelligence • Doing the right thing given available information • How do we define the right thing? • Suppose put your hand down on a hot stove. What is the rational response? • Rationality does not always mean doing the best possible thing COSC 159 - Fundamentals of AI

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9. Rationality • Given the situation, was the boss’ action irrational? • What would make the boss’ action irrational? COSC 159 - Fundamentals of AI

10. Competing Views of AI • Many definitions that can be classified as follows (Russell and Norvig, 2003) COSC 159 - Fundamentals of AI

11. Acting Humanly • Turing test (1950) COSC 159 - Fundamentals of AI

12. Acting Humanly • Goal: Make computers/entities act like humans • Natural language processing • Knowledge representation • Automated reasoning • Machine learning • It is not important how the actions are chosen, as long as results in behavior indistinguishable from a human COSC 159 - Fundamentals of AI

13. Thinking Humanly • Understand cognition • Defined as the mental process of knowing, including aspects such as awareness, perception, reasoning, and judgment. • Simulate cognition on computers • Cognitive science • Experimental investigation of humans and animals • The how is important COSC 159 - Fundamentals of AI

14. Thinking Rationally • Attempt to codify “right thinking” • Aristotle’s syllogisms (reasonings or patterns of argument) • Logical approach • Formal methods of representing knowledge • Formal methods of reasoning • Again, how a conclusion is reached is important COSC 159 - Fundamentals of AI

15. Acting Rationally • Entities that do the right thing • The how isn’t necessarily important • Couple rational thinking with other methods • What if there is no provably correct action? • Consider the hot stove again • Did the action require rational thought? Are reflex actions intelligent? COSC 159 - Fundamentals of AI

16. Applications • Autonomous planning and scheduling • NASA’s Remote Agent • Game playing • IBM’s Deep Blue • Autonomous control • ALVINN, drove 98% of the time across the country • Diagnosis • Medical diagnosis • Pattern recognition • Data mining and bioinformatics COSC 159 - Fundamentals of AI

17. Characteristics of AI Problems • Frequently hard • NP-hard, which implies there is no known efficient general solution • Frequently complex • Messy data, such as images, pressure, locations, natural language, etc. • Frequently imprecise • Uncertain situations • Autonomy • Cannot require human intervention, must adapt COSC 159 - Fundamentals of AI

18. Agents • An agent is something that acts • In this class, we will build software agents • Agents that act rationally • How are agents different from other programs? • Autonomous • Perceptive • Persistent • Adaptable • Assume the goals of other agents COSC 159 - Fundamentals of AI

19. Agents Agent Percepts Sensors ? Environment Actions Actuators COSC 159 - Fundamentals of AI

20. Definitions • Percept sequence • History of everything agent has perceive • Agent function • Map from percept sequence to action • Agent program • Implementation of agent function COSC 159 - Fundamentals of AI

21. Example • Consider a world that has a starving monkey and a banana. Whenever the monkey is in the same location as the banana, the monkey will eat it. After eating the banana, the monkey falls asleep. • We would like to build a simulation for the environment with a software agent representing the monkey. • Consider a world with two locations. COSC 159 - Fundamentals of AI

22. Example U D COSC 159 - Fundamentals of AI

23. Example • Assumptions • Monkey can see the bananas and knows its location • Defines percepts: (Location, Contents) • Actions • Up, down, eat, sleep COSC 159 - Fundamentals of AI

24. Example • Agent function should move monkey to the bananas, eat the bananas, then sleep • One possible agent program is to create a table mapping a percept sequence to appropriate action • Table-driven agent COSC 159 - Fundamentals of AI

25. Table COSC 159 - Fundamentals of AI

26. Questions to ponder • Is a table driven agent a good way to implement rational behavior? • Are all sequences of percepts possible in the environment? • What if the monkey didn’t know its location, could you still devise a solution to the problem? How would the percepts change? COSC 159 - Fundamentals of AI

27. Measuring Rational Behavior • What does it mean for an agent to do the right thing? • The right action is the one causing the agent to be most successful. • A performance measure embodies the criterion for an agent’s success. COSC 159 - Fundamentals of AI

28. Performance Measures • Simple performance measure for monkey and bananas • The monkey has eaten and fallen asleep. • Suppose you have two monkeys, one that sleeps right after eating and one that wanders around and then falls asleep. Which one is better? Why? COSC 159 - Fundamentals of AI

29. Performance Measures • Consider more complex environments • What performance measure is appropriate for the economy? • What about for stocks? • How about medical diagnoses? • What about driving a car? • Performance measures are not easy to determine, but you must design one for each environment COSC 159 - Fundamentals of AI

30. Rationality • Rational behavior at any given time depends on four things • Performance measure • Agent’s prior knowledge • Actions agent can perform • Agent’s percept sequence COSC 159 - Fundamentals of AI

31. Course Goals • Understand and build intelligent entities • Rational agents • Formulate search problems • Solve using uninformed and informed algorithms • Represent and reason about knowledge • Logic • Formulate and solve planning problems • STRIPS, partial order planners COSC 159 - Fundamentals of AI

32. Course Goals (cont.) • Reason in uncertain situations • Probability, Bayesian networks • Introduce machine learning • Inductive learning, decision trees COSC 159 - Fundamentals of AI

33. For Next Time • Read through chapters 1 and 2. • Think about how you would implement a simulation for the two location monkey and banana world. COSC 159 - Fundamentals of AI

34. Summary • AI is the study and implementation of intelligent entities • Several perspectives on AI • We will take the rational action perspective • The agent framework provides a unifying approach to AI • Applications of AI are widespread and complex COSC 159 - Fundamentals of AI