CMPUT 366 Intelligent Systems:

1. CMPUT 366 Intelligent Systems: Introduction to Artificial Intelligence

2. Instruction Team • Prof: Dekang Lin • Office hours: Tue, Thur: 3:30-4:30, or by appointment • Phone: 492-9920 • TAs: Yaling Pei, Mark Schmidt, Gang Wu • E-mail: c366@cs.ualberta.ca • Home Page: http://www.cs.ualberta.ca/~lindek/366 • Announcements • Slides • Assignments

3. Textbooks • Required  • S Russell and P Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 1995. • Recommended • D Poole, A Mackworth and R Goebel, Computational Intelligence: A Logical Approach , Oxford, 1998. • Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, 1998.

4. Evaluation • 4 Assignments • 16% each. Solo! (see code of conducts) • Paper/Pencil • Submit hard copy on due date before class, write ligibly • Implementations (C++/Java) • Submit using ‘try’. The deadline is 11:59pm on the due date. • The implementations must run on the lab machines (in CSC 219) • Final Exam • 36%

5. Other Issues • Prerequisites • Programming skills (C++, Java) • Elementary probability theory • AI Seminar • http://www.cs.ualberta.ca/~ai/seminars • Friday noons, CSC333 • Neat topics, great speakers, FREE PIZZA!

6. Course Overview • Introduction: intelligent agent • Search and constraint satisfaction • Logical agent and planning • Probabilistic reasoning • Natural language and speech • Perception (if there is time)

7. What is Artificial Intelligence (AI)? Discipline that systematizes and automates intellectual tasks to create machines that:

8. Act Like Humans • AI is the art of creating machines that perform functions that require intelligence when performed by humans • Methodology: Take an intellectual task at which people are better and make a computer do it • Prove a theorem • Play chess • Plan a surgical operation • Diagnose a disease • Navigate in a building

9. Turing Test • Alan Turing, a mathematician who not only cracked the German code making machine, Enigma during the Second World War, but invented the concept of computers as we know them. • Turing asserted that if you can fool a human into believing that he/she is receiving answers from another human when in fact it is a computer, this proves that computers are doing essentially what human brains do.

10. “Can machines think” -> “Can machines behave intelligently?” • Operational test of intelligence: Imitation Game: • Problem: • Turing Test is not reproducible, constructive, or amenable to mathematical analysis.

11. Think Like Humans • How the computer performs functions does matter • Comparison of the traces of the reasoning steps • Cognitive science  testable theories of the workings of the human mind

12. Examples • Garden-Path Sentence: • The horse raced past the barn fell. • Center-embedding: • The cat that the dog that the mouse that the elephant admired bit chased died. • The elephant admired the mouse that bit the dog that chased the cat that died. But, do we want to duplicate human imperfections?

13. Think Rationally: Laws of Thought • Normative (or prescriptive) rather than descriptive • Aristotle: what are correct arguments/thought processes? • Several Greek schools developed forms of logic: notation and rules of derivation for thoughts. • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?

14. Act Rationally • Rational behavior: doing the right thing • “The right thing”: • that which is expected to maximize goal achievement, given the available information • Limited resource, imperfect knowledge • Rationality ≠ Omniscience, Rationality ≠ Clairvoyance, Rationality ≠ Successes • Doesn't necessarily (but often) involve thinking • Ignores the role of consciousness, emotions, fear of dying, … • Doesn’t necessarily have anything to do with how humans solve the same problem.

15. Example: Semantic Orientation • In many tasks, it is necessary to determine the semantic orientation of words • Mining movie reviews • Routing custermer e-mail • Turney 2002 • Determine the semantic orientation of words using internet search engines.

16. AI History

17. Trends Since 90’s • Relying less on logic and more on probability theory and statistics. • More emphasis on objective performance evaluation. • Intelligent Agents • Accomplishments in • Game playing: Deep blue, Chinook, … • Space Probe • Biological sequence analysis • OCR • Consumer electronics ……

18. laser range finder sensors environment ? agent actuators sonars touch sensors Notion of an Agent Source: robotics.stanford.edu/~latombe/cs121/2003/home.htm

19. sensors environment ? agent actuators Notion of an Agent • Locality of sensors/actuators • Imperfect modeling • Time/resource constraints • Sequential interaction • Multi-agent worlds Source: robotics.stanford.edu/~latombe/cs121/2003/home.htm

20. target robot Example: Tracking a Target • The robot must keep the target in view • The target’s trajectory is not known in advance • The robot may not know all the obstacles in advance • Fast decision is required Source: robotics.stanford.edu/~latombe/cs121/2003/home.htm

21. What is Artificial Intelligence? (revised) • Study of design of rational agents • agent = thing that acts in environment • Rational agent = agent that acts rationally: • actions are appropriate for goals and circumstances to changing environments and goals • learns from experience

22. Goals of Artificial Intelligence • Scientific goal: • understand principles that make rational (intelligent) behavior possible, in natural or artificial systems. • Engineering goal: • specify methods for design of useful, intelligent artifacts. • Psychological goal: • understanding/modeling people • cognitive science (not this course)

23. Goals of This Course • Introduce key methods & techniques from AI • searching, • reasoning and decision making (logical and probabilistic) • learning (covered in detail in CMPUT466) • language understanding, • . . . • Understand applicability and limitations of these methods

24. Goals of This Course • Our approach: • Characterize Environments • Identify agent that is most effective for each environment • Study increasingly complicated agent architectures requiring • increasingly sophisticated representations, • increasingly powerful reasoning strategies

25. Intelligent Agents • Definition: An Intelligent Agentperceives its environment via sensors and acts rationally upon that environment with its acutators. • Hence, an agent gets percepts one at a time, and maps this percept sequence to actions. • Properties • Autonomous • Interacts with other agents plus the environment • Adaptive to the environment • Pro-active (goal-directed)

26. Applications of Agents • Autonomous delivery/cleaning robot • roams around home/office environment, delivering coffee, parcels,. . . vacuuming, dusting,. . . • Diagnostic assistant helps a human troubleshoot problems and suggest repairs or treatments. • E.g., electrical problems, medical diagnosis. • Infobot searches for information on computer system or network. • Autonomous Space Probes • . . .

27. Task Environments: PEAS • Performance Measure • Criterion of success • Environment • Actuators • Mechanisms for the agent to affect the environment • Sensors • Channels for the agent to perceive the environment

28. Example: Taxi Driving • Performance Measure • Safe, fast, legal, comfortable trip, maximize profit • Environment • Roads, other traffic, pedestrians, customers • Actuators • Steering, accelerator, break, signal, horn, … • Sensors • Cameras, sonar, speedometer, GPS, …

29. Types of Environments • Fully observable (accessible) or not • Deterministic vs. stochastic • Episodic vs. sequential • Static vs. dynamic • Discrete vs. continuous • Single agent vs. multiagent • competitive vs. cooperative

30. Example: Cleaning Agent

31. Performance Measure • ?? • Environment • ?? • Actuators • ?? • Sensors • ??

32. SurfBot • Automated web surfing • A SurfBot operates in the environment of the web. • takes in high-level, perhaps informal, queries • finds relevant information • presents information in meaningful way

33. Performance Measure • ?? • Environment • ?? • Actuators • ?? • Sensors • ??

34. Agent Function and Program • Agent specified by agent function • mapping percept sequences to actions • Aim: Concisely implement “rational agent function” • Agent program • input: a single percept-vector • (keeps/updates internal state) • returns action

35. Skeleton Agent Program function SkeletonAgent(percept) returns action static: memory, [agent's memory of the world] memory  UpdateMemory(memory,percept) action  ChooseBestAction(memory) memory  UpdateMemory(memory, action) return action

36. Types of Agents • Simple reflex agents • Actions are determined by sensory input only • Model-based reflex agents • Has internal states • Goal-based agents • Action may be driven by a goal • Utility-based agents • Maximizes a utility function

37. Simple Reflex Agent

38. Example • A LEGO MindStormTM program: if (isDark(leftLightSensor)) turnLeft() else if (isDark(rightLightSensor)) turnRight() else goStraight() • What’s the agent function?

39. Model-Based Agent

40. Goal-based Agent

41. Utility-based Agent

42. Summary • What is AI? • Rationality • A bit of History • Intelligent Agent • PEAS • Types of Agents