1. Where are we at??? Should have read 1.1 Should have skimmed rest of chapter 1. Should have read 2.1-2.3 Should start to read 2.4 Should be getting read to begin RP1a

2. What is RP1a? Due next Friday. Read a research paper that I have provided on the course website –Six pages –Published in AAAI –Deals with intelligent trip routing based on preferences of the driver –It’s not the most up to date research, but it is actually a good paper for this task.

3. The questions I asked you to consider Summarize - What is this paper about? Analyze - What do the authors do well? Analyze - What do the authors do poorly? Analyze - What question did the authors leave unanswered? Reflect - What did you learn about the topic they were discussing? Reflect - What did you learn about writing a short/concise paper?

4. Outline for the next few days Framing our course’s approach to AI Agents and environments Rationality PEAS (Performance measure, Environment, Actuators, Sensors) Environment types

5. 5 How this course will define AI Artificial Intelligence is a science that has defined its goal as giving machines the ability to perform tasks that, when performed by humans, require intelligence. These include the ability to solve problems, make decisions, to learn and to understand.

6. 6 Strong AI and Weak AI There are two entirely different schools of Artificial Intelligence: Strong AI: –This is the view that a sufficiently programmed computer would actually be intelligent and would think in the same way that a human does. Weak AI: –This is the use of methods modeled on intelligent behavior to make computers more efficient at solving problems.

7. 7 Strong AI  Strong AI is the belief that providing a computer with intelligent software somehow enables that machine to think. And it will possess consciousness (a sense of ‘I’) much as humans do.  Hollywood has long been a proponent of this viewpoint, e.g. the movie “AI” in which the android main character yearns to have his identity acknowledged.

8. Weak AI Weak AI: Intelligent behavior can be modeled and used by computers to solve complex problems. No presupposition is made that the computer is intelligent in the way that a human is. Most artificial intelligence researchers subscribe to this belief. 8

9. 9 Defining AI When you really think about this distinction, we are discussing the difference between: thinking vs. acting But we can also break this down even further by thinking about –humanly vs. rationally

10. How R&N define AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally humanly vs. rationally thinking vs. acting

11. 11 “Flavors” of AI Systems that think like humans STRONG AI Systems that think rationally STRONG AI Systems that act like humans WEAK AI Systems that act rationally WEAK AI humanly vs. rationally thinking vs. acting

12. How R&N define AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally humanly vs. rationally thinking vs. acting Turing Test

13. 13 Acting Humanly The Turing Test –Alan Turing, "Computing Machinery and Intelligence", Mind, Volume LIX, Number 236, 1950. Let me read you a small part of this paper.

14. 14 Acting Humanly The Turing Test –Alan Turing, "Computing Machinery and Intelligence", Mind, Volume LIX, Number 236, 1950. AI is defined by human-like behavior and a human like experience. Rather than define intelligence we operationalize it.

15. 15 Measuring Intelligence Alan Turing (1950) proposed two imitation games In the first: A series of questions is asked. Interrogator must determine gender of person on the other side If a man is successful in deceiving the interrogator, then we say that he has passed this imitation game

16. 16 The second… The Turing Test for Intelligence.  Loebner Prize of $100,000  Is it a computer or a human? If the computer is successful in deceiving the interrogator then we say that it has passed the Turing Test.

17. 17 The Turing Test /cont.  Proposed Questions:  Sqrt(1,000,017) = ? … not a good idea, why not?  Are you afraid of dying?  How does the dark make you feel?  What does it feel like to be in love?  Is this a valid barometer for intelligence?

18. 18 Block’s Criticism of the Turing Test  English text is encoded in ASCII  Hence a series of questions and answers may be stored as a (very large) number of look ups.  One could envision many instances of the Turing Test being stored on a very large database.  Passing the test could then be accomplished by table lookup.  Granted, such a computer system does not exist at present…  But if it did, would you feel comfortable in calling this computer intelligent?

19. 19 Defense of Turing  Anti Turing Premise: It is not possible to gain insight on the internal state of something from external observations.  Pro Turing Rebuttle: Rutherford was able to deduce the internal state of matter – mostly space (before electron microscope) Matter high energy particles

20. How R&N define AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally humanly vs. rationally thinking vs. acting Cognitive Models

21. 21 Thinking Humanly Cognitive Science (1960s) Focus on the “rationale” that goes into making a decision. Claims that making the right decision isn’t intelligence if there isn’t a proper rationale. Critics state we know too little about the workings of the brain, and that this approach too often makes unfounded leaps.

22. How R&N define AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally humanly vs. rationally thinking vs. acting Laws of Thought Approach

23. 23 Thinking Rationally Laws of Thought Traces its roots back to Aristotle, who tried to approach things from a normative, or prescriptive, way of thinking rather than a descriptive one. The idea is that with proper logic a person (or computer) could always yield correct solutions when provided with correct information –( x) (D(x) ^ R(x)) → ( x) (D(x) → S(x) ) –Modus Ponens, Modus Tolens

24. 24 Thinking Rationally There are several problems with this approach: 1.It is not always easy (if possible) to codify a situation in the formal terms required by logical notation 2.We don't always have perfect information. 3.Even if we have all the info and how to interpret it, knowing how to solve a problem and doing so are still very different things. 4.Not all intelligent behavior is mediated by logical deliberation (blinking)

25. How R&N define AI Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally humanly vs. rationally thinking vs. acting Rational Agents

26. 26 Acting Rationally “Doing the right thing" … "that which is expected to maximize goal achievement, given the available information." Unlike the previous approach (thinking rationally) the process of "acting" rationally doesn't necessarily require "thinking." (blinking fits in here). In AI we define things that act rationally as “agents.”

27. Rational Agents It’s this last approach that we will focus upon this semester –This shouldn’t discredit the other approaches –In fact I think the line separating “humanly” from “rationally” is sometimes very blurry.

28. This is as far as we got in class

29. 29 One more idea before we “start” AI goes beyond “normal” CS… AI often is working on problems that we know are intractable… –Consider chess

30. 30 One more idea before we “start” In the early 1970s someone wrote the following bit of trivia: –If every man, woman, and child on earth were to spend every waking moment playing chess (16 hours per day) at the rate of one game per minute, it would take 146 billion years to use every variation of the first 10 moves.

31. 31 One more idea before we “start” Given this complexity, how the heck can humans EVER hope to play chess well??? We don't often arrive at the best solutions, but we usually do arrive at solutions that are good enough. This idea of satisficing rather than optimizing when confronted with an intractable problem is central to what AI is about.

32. 32 Agents “An agent is simply something that acts.” An agent is an entity that is capable of perceiving its environment (through sensors) and responding appropriately to it (through actuators).

33. 33 Agents If the agent is intelligent, it should be able to weigh alternatives. “A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.”

34. 34 Agents An agent should be able to derive new information from data by applying sound logical rules. It should possess extensive knowledge in the domain where it is expected to solve problems.

35. 35 Agents We will consider true intelligent, rational, agents as entities which display: –Perception –Persistence –Adaptability –Autonomous control

36. Agents and Environments Agents include humans, robots, softbots, thermostats, etc. The agent function maps from percept histories to actions: The agent program runs on the physical architecture to produce

37. Agents and Environments Vacuum-Cleaner World (Figure 2.2)

38. Agents and Environments Vacuum-Cleaner World (Figure 2.3)

39. Agents and Environments Vacuum-Cleaner World (Figure 2.3)

40. Agents and Environments Vacuum-Cleaner World

41. Rationality A rational agent does the right thing. What is the right thing? One possibility: –The action that will maximize success. But what is success? –The action that maximizes the agent’s goals. Use a performance measure to evaluate agent’s success. So what would be a good performance measure for the vacuum agent?

42. Rationality Fixed performance measure evaluates the environment sequence –One point per square cleaned up in time T –One point per clean square per time step, minus one per move? –Penalize for more than k dirty squares? A rational agent chooses whichever action maximizes the expected value of the performance measure given the percept sequence to date.

43. Rationality Rational agent definition: “For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built- in knowledge the agent has.”

44. Rationality Rationality is not –Omniscience –Clairvoyance –Success Rationality implies –Exploration –Learning –Autonomy