COMP3702/7702 Artificial Intelligence Week1: Introduction Russell & Norvig ch.1-2.3, Hanna Kurniawati

Transcription

1 COMP3702/7702 Artificial Intelligence Week1: Introduction Russell & Norvig ch.1-2.3, Hanna Kurniawati

2 Today } What is Artificial Intelligence? } Better know what it is first before committing the next 16 weeks of your life to it J } Some history } To predict the future } Agent defined } How to design an agent, so that we can program it } Properties of an agent } What representations & methods should be used, so that the agent can solve its problem(s) well? } Intro to Search } A way for an agent to solve its problem(s)

3 Today } What is Artificial Intelligence? } Better know what it is first before committing the next 16 weeks of your life to it J } Some history } To predict the future } Agent defined } How to design an agent, so that we can program it } Properties of an agent } What representations & methods should be used, so that the agent can solve its problem(s) well? } Intro to Search } A way for an agent to solve its problem(s)

4 What is Artificial Intelligence (AI)? } AI is an attempt to build intelligent computers. } What is intelligent? } Act like humans. } Think like humans. } Act rationally.

5 What is Artificial Intelligence (AI)? } AI is an attempt to build intelligent computers. } What is intelligent? } Act like humans. } Think like humans. } Act rationally.

6 Act like humans: Turing Test } An attempt to make intelligent machine less vague. } Goal: Test if a computer can fool humans into thinking that the computer is human. } The computer is asked questions by a human interrogator. It passes the test if the interrogator cannot tell whether the responses come from a human or a computer. } By Alan Turing in 1950.

7 Critique to Turing Test: The Chinese Room } Thought experiment by Searle } A person who only knows English is locked in a room with } Stack of papers containing Chinese symbols. } An instruction manual in English. } People outside the room send questions in Chinese. } Suppose by following the instruction manual, the man in the room can pass out Chinese symbols which are correct answers to the questions. } The person appears to know Chinese even though he is not.

8 Searle s point } No matter how intelligent the computer seems to be, if it does not understand the meaning of the symbols it process, it is not really intelligent.

9 Applications of Turing Test } Regardless of the philosophical debate, Turing s idea on trying to define (artificial) intelligence more concretely has yielded useful results. } Chatterbots: Eliza, A.L.I.C.E., automated online assistance, etc. } CAPTCHA: Completely Automated Public Turing test to tell Computers and Humans Apart. } Turing test, but the interrogator is a computer.

10 What is Artificial Intelligence (AI)? } AI is an attempt to build intelligent computers. } What is intelligent? } Act like humans. } Think like humans. } Act rationally.

11 Think like humans } Mimic the working of human brains. } Not just appear human. } To what level? } Very high level: vision, memory, } Neurons. But, people who study neurons still argue what neurons can and cannot do } Cognitive science & neuroscience.

12 What is Artificial Intelligence (AI)? } AI is an attempt to build intelligent computers. } What is intelligent? } Act like humans. } Think like humans. } Act rationally.

13 The question is } Do we really want computers to be exactly like humans? } How useful would a computer be if its shortcomings are exactly the same as our shortcomings?

14 Act rationally } Always make the best decision given the available resources (knowledge, time, computational power and memory). } Best: Maximize certain performance measure(s), usually represented as a utility function. } More on this throughout the semester.

15 What is Artificial Intelligence (AI)? } AI is an attempt to build intelligent computers. } What is intelligent? } Act like humans. } Think like humans. } Act rationally. } And many more

16 In this class, } We will take the act rationally view } We are interested to build systems (usually called agents) that behave rationally; systems that accomplish what it is supposed to do, well, given the available resources. } Don t worry about how close the systems resemble humans and about philosophical questions on what is intelligent. } We may use inspirations from humans / other intelligent beings.

17 Today What is Artificial Intelligence? Better know what it is first before committing the next 16 weeks of your life to it J } Some history } To predict the future } Agent defined } How to design an agent, so that we can program it } Properties of an agent } What representations & methods should be used, so that the agent can solve its problem(s) well? } Intro to Search } A way for an agent to solve its problem(s)

18 Some history } 1943: The beginning of neural network, started by McCulloch & Pitts. } 1950: Turing test introduced. } : The promise of AI. } 1956: The name Artificial Intelligence is coined by John McCarthy at the Dartmouth Conference. } : AI meets computational complexity. } : Development of expert systems. } : Expert systems industry booms. } : Expert systems industry busts.

19 Some history } 1988-now: Probability enters AI. Start of machine learning & soft computing. } 1995-now: Intelligent agents popularised. } 2000-now: AI is back } 2013-now: Rise of deep learning, Rush for AI in industry.

20 Of course, there s the debate

21 In the city,

22 In marine environments,

23 Today What is Artificial Intelligence? Better know what it is first before committing the next 16 weeks of your life to it J Some history To predict the future } Agent defined } How to design an agent, so that we can program it } Properties of an agent } What representations & methods should be used, so that the agent can solve its problem(s) well? } Intro to Search } A way for an agent to solve its problem(s)

24 In this class, } We will take the act rationally view } We are interested to build systems (usually called agents) that behave rationally; systems that accomplish what it is supposed to do, given the available resources. } Don t worry about how close the systems resemble humans and about philosophical questions on what is intelligent. } We may use inspirations from humans / other intelligent beings.

25 What is an agent? } A computer program that: } Gathers information about an environment, and } takes actions autonomously based on that information. } Examples: } A robot. } A web crawler. } A spam filter. Agent Percepts Action Environment This class: Focus on rational agents: Agents that act rationally.

26 Recall our goal: To build a rational agent } To achieve our goal, we need to define our agent in a way that we can program it. } So, more formal definition is needed. } The problem of defining a particular agent formally is usually called the agent design problem. } Basically, it s about defining the components of the agent, so that when the agent acts rationally, it will accomplish the task it is supposed to perform, well

27 Overview of a rational agent Percepts Agent Environment Action } An agent performs the ``best action in the environment, the environment generates a percept. The percept generated by the environment may depend on the sequence of actions the agent has done.

28 The components Agent Percepts Environment } A: Action space } The set of all actions the agent can do. } P: Percept space } The set of all things the agent can perceive in the world. } S: State space } Internal state of the agent & the environment that matters for the interaction between the agent & the environment. } World dynamics: T: S X A à S } Perception function: Z: S à P Action Model the relation between the agent & the world

29 The components } Recall: } Best action: The action that maximizes a given performance criteria. } A rational agent selects an action that it believes will maximize its performance criteria, given the available knowledge, time, & computational resources. } Utility function: A function that assigns a value to each state (or sequence of states or state-action or state-action-observation), to indicate the desirability of being in such a (sequence of) state with respect to the agent s task.

30 The components summarised } The first step in designing an agent is to set the following components: } Action space (A) } Percept space (O) } State space (S) } World dynamics (T: SXA à S) } Percept function (Z: S à O) } Can also maps from SXA } Utility function (U: S à real number) } Can also maps from SXA or SXAXS or SXAXO

31 The problem the agent should solve } Find a mapping from sequences of percepts to action P* à A that maximizes the utility function. } Given the sequences of percepts it has seen so far, what should the agent do next, so that the utility function can be maximized.

32 Wait } Isn t this just an optimization problem? } Yes. Well, most problems can be framed as optimization problems. } Real-world AI problems are usually hard optimization problems. } We ll see computational representations & techniques that utilizes the problem & environmental structures to make solving the problem more feasible.

33 Throughout the semester, we will see } Various computational representation of the components. } Various ways to represent the problem (i.e., the mapping from perceptions to actions). } Computational techniques that compute a good solution to the above problem, efficiently. } The suitable representations & techniques highly depend on properties of the environment & the agent s knowledge about the environment } More about this soon

34 Example: 8-puzzle } Action space (A) } Move the empty cell left (L), right (R), up (U), down (D). } Percept space (P) Initial state } The sequence of numbers in left-right and up-down direction, where the empty cell is marked with an underscore. } State space(s) } Same as P } World dynamics (T) } The change from one state to another, given a particular movement of the empty cell. } Can be represented as a table Goal state

35 Example: 8-puzzle } Percept function (Z) } Identity map } Utility function: } +1 for the goal state. } 0 for all other states.

36 Example: Tic Tac Toe An agent that plays Tic Tac Toe 1 time step = a single move by the agent & the opponent.

37 Example: Tic Tac Toe An agent that plays Tic Tac Toe } Action space (A) } Make a mark at cell-i. } But, may also need more details. Suppose we use a robot: } Moving the arm to a position where the robot can make a mark at the specified (x, y) position. } Moving the arm down. } Make a mark at (x, y) position. } In real-world problems, we need to decide the level of details to use. } Usually, depends on the task & computational resources. } In this example, we ll use the more abstract : } Don t worry about how the mark will be made. } Just on where to place the mark strategically, to win the game.

38 Example: Tic Tac Toe } Action space (A) } Make a mark at cell-i. } Percept space (P) } The position of the marks (the Xs & Os), e.g., ordering of Xs & Os if we read the cells top-down, left-right: In this example: X_X_OXOXO } Similar to the action space, there s different levels of abstractions we may need. If we use a robot: } Bitmap image. } In this example, we ll use the higher level abstraction.

39 Example: Tic Tac Toe } Action space (A) } Make a mark at cell-i. } Percept space (P) } The position of the marks (the Xs & Os). } State space (S) } All possible combinations of the marks positions. } World dynamics (T) } The change from a snapshot of the game to the next, given an action. } Percept function (Z) } In this case, the percept is exactly the same as the current state.

40 Example: Tic Tac Toe } Utility function: } 10 when we make a straight line. } -10 when the opponents make a straight line. } 0 for any other state

41 Key difficulty in designing an agent } Ensuring that the best sequence of actions for the agent (as defined by its components) is equivalent to the best way for the agent to accomplish its task. } Essentially, the same as the difficulties in almost any modelling problem: } Ensuring that the model is a faithful representation of the problem.

42 Today What is Artificial Intelligence? Better know what it is first before committing the next 16 weeks of your life to it J Some history To predict the future Agent defined How to design an agent, so that we can program it } Properties of an agent } What representations & methods should be used, so that the agent can solve its problem(s) well? } Intro to Search } A way for an agent to solve its problem(s)

43 Next week We ll look into different problem classes & methods for the agent to solve its problem Please review computational complexity: Check out resources page in the website