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ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.

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Presentation on theme: "ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk."— Presentation transcript:

1 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 1 Please pick up a copy of the course syllabus from the front desk.

2 Introduction to AI ECE457 Applied Artificial Intelligence Fall 2007 Lecture #1

3 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 3 Outline What is an AI? Russell & Norvig, chapter 1 Agents Environments Russell & Norvig, chapter 2

4 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 4 Artificial Intelligence Computer players in video games Robotics Assembly-line robots, auto-pilot, Mars exploration robots, RoboCup, etc. Expert systems Medical diagnostics, business advice, technical help, etc. Natural language Spam filtering, translation, document summarization, etc. Artificial intelligence is all around us

5 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 5 What is an AI? Systems that… Rationality vs. Humans: emotions, instincts, etc. Thinking vs. acting: Turing test vs. Searle’s Chinese room Engineers (and this course) focus mostly on rational systems HumanlyRationally Think Neural networks Theorem proving Act ELIZADeep Blue

6 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 6 Act Rationally Perceive the environment, and act so as to achieve one’s goal Not necessary to do the best action There’s not always an absolutely best action There’s not always time to find the best action An action that’s good enough can be acceptable Example: Game playing Sample approach: Tree-searching strategies Problem: Choosing what to do given the constraints

7 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 7 Think Rationally Uses logic to reach a decision or goal via logical inferences Example: Theorem proving Sample approach: First-order logic Problems: Informal knowledge Uncertainty Search space

8 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 8 1. X = Y/Z  XZ = Y 2. X = Y  X + Z = Y + Z 3. X * Y + X * Z  X * (Y + Z) 4. b/c = AH/b 5. a/c = BH/a 6. AH + BH = c Think Rationally a. b² = AH * c b. a² = BH * c c. a² + b² = BH * c + AH * c d. a² + b² = c * (AH + BH) e. a² + b² = c²

9 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 9 Act Humanly “Turing-test” AI Improve human-machine interactions up to human-human level Drawbacks: In some cases, requires dumbing down the AI Lots of man-made devices work well because they don’t imitate nature

10 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 10 Think Humanly Cognitive science Neural networks Helps in other fields Computer vision Natural language processing

11 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 11 Rational Agents An agent has Sensors to perceive its environment Actuators to act upon its environment A rational agent has an agent program that allows it to do the right action given its precepts Environment Percepts Actions SensorsActuators Agent Program

12 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 12 Types of Agents Simple reflex agent Selects action based only on current perception of the environment Model-based agent Keeps track of perception history Goal-based agent Considers what will happen given its actions Utility-based agent Adds the ability to choose between conflicting/uncertain goals Learning agent Adds the ability to learn from its experiences

13 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 13 Simple Reflex Agent Environment PerceptsActions Sensors Actuators Selected Action Current State If-then Rules

14 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 14 Simple Reflex Agent Dune II (1992) units were simple reflex agents Harvester rules: IF at refinery AND not empty THEN empty IF at refinery AND empty THEN go harvest IF harvesting AND not full THEN continue harvesting IF harvesting AND full THEN go to refinery IF under attack by infantry THEN squash them

15 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 15 Model-Based Agent Environment PerceptsActions Sensors Actuators Selected Action Current State Previous perceptions Impact of actions World changes If-then Rules

16 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 16 Goal-Based Agent Environment PerceptsActions Sensors Actuators Selected Action Current State Goal Previous perceptions Impact of actions World changes State if I do action X

17 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 17 Utility-Based Agent Environment PerceptsActions Sensors Actuators Selected Action Current State Utility Previous perceptions Impact of actions World changes State if I do action X Happiness in that state

18 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 18 Learning Agent Environment PerceptsActions Sensors Actuators Problem Generator Learning Element Feedback Performance standard Changes Knowledge Learning Goals Performance Element Critic

19 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 19 Properties of the Environment Fully observable vs. partially observable See everything vs. hidden information Chess vs. Stratego Deterministic vs. stochastic vs. strategic Controlled by agent vs. randomness vs. multiagents Sudoku vs. Yahtzee vs. chess Episodic vs. sequential Independent episodes vs. series of events Face recognition vs. chess

20 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 20 Properties of the Environment Static vs. dynamic vs. semi-dynamic World waits for agent vs. world goes on without agent vs. world waits but agent timed Translation vs. driving vs. chess with timer Discrete vs. continuous Finite distinct states vs. uninterrupted sequence Chess vs. driving Single agent vs. cooperative vs. competitive Alone vs. team-mates vs. opponents Sudoku vs. sport team vs. chess

21 ECE457 Applied Artificial Intelligence R. Khoury (2007)Page 21 Crossword Puzzle Fully observable, deterministic, sequential, static, discrete, single-agent Monopoly Fully observable, stochastic, sequential, static, discrete, competitive multi-agent Driving a car Partially observable, stochastic, sequential, dynamic, continuous, cooperative multi-agent Assembly-line inspection robot Fully observable, deterministic, episodic, dynamic, continuous, single-agent Properties of the Environment


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