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CSE 471/598

This course provides an introduction to artificial intelligence, covering topics such as intelligent agents, search algorithms, constraint satisfaction problems, and more. The instructor is Subbarao Kambhampati and additional help is provided by Binh Minh Do. Competence in Lisp programming is expected. Attendance and attentiveness in classes are mandatory and projects/homeworks/participation account for approximately 55% of the final grade.

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CSE 471/598

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  1. CSE 471/598 Introduction to Artificial Intelligence (aka the very best subject in the whole-wide-world) The Class His classes are hard; He is not.

  2. General Information • Instructor: Subbarao Kambhampati (Rao) • Office hours: after class, T/Th • TA: Sreelakshmi Vaddi • Office hours: TBD • Additional help by Binh Minh Do. • Course Homepage: http://rakaposhi.eas.asu.edu/cse471

  3. Subject to (minor) Changes Grading etc. 471 and 598 students are treated as separate clusters while awarding final letter grades (no other differentiation) • Projects/Homeworks/Participation (~55%) • Approximately 4 • Expected background • Competence in Lisp programming • Attendance to and attentiveness in classes is mandatory • Homeworks may be assigned piecemeal.. • Midterm / final (~45%)

  4. Life with a homepage.. • I will not be giving any handouts • All class related material will be accessible from the web-page • Home works may be specified incrementally • (one problem at a time) • The slides used in the lecture will be available on the class page • I reserve the right to modify slides right up to the time of the class • When printing slides avoid printing the hidden slides

  5. Course Overview • What is AI • Intelligent Agents • Search (Problem Solving Agents) • Single agent search [Project 1] • Constraint Satisfaction Problems • Adversarial (multi-agent) search • Logical Reasoning [Project 2] • Reasoning with uncertainity • Planning [Project 3] • Learning [Project 4]

  6. Mechanical flight became possible only when people decided to stop emulating birds…

  7. Do we want a machine that beats humans in chess or a machine that thinks like humans while beating humans in chess?

  8. It can be argued that all the faculties needed to pass turing test are also needed to act rationally to improve success ratio…

  9. Playing an (entertaining) game of Soccer Solving NYT crossword puzzles at close to expert level Navigating in deep space Learning patterns in databases (datamining…) Supporting supply-chain management decisions at fortune-500 companies Bringing “Semantics” to the web

  10. What AI can do is as important as what it can’t yet do.. • Captcha project

  11. Playing an (entertaining) game of Soccer Solving NYT crossword puzzles at close to expert level Navigating in deep space Learning patterns in databases (datamining…) Supporting supply-chain management decisions at fortune-500 companies Bringing “Semantics” to the web

  12. Class of 8/28 Architectures for Intelligent Agents Wherein we discuss why do we need representation, reasoning and learning Office hours for TA for this week: 10—11:30 GWC 367 Regularly Wed 10:30—12 Class accounts available if needed… Lisp assignment deadline…

  13. PEAS (Performance, Environment, Actuators,Sensors)

  14. Partial contents of sources as found by Get Get,Post,Buy,.. Cheapest price on specific goods Internet, congestion, traffic, multiple sources

  15. Rational != Intentionally avoiding sensing “history” = {s0,s1,s2……sn….} Performance = f(history)

  16. Yes Yes No Yes Yes #1 No No No No No >1 #Agents Accessible: The agent can “sense” its environment best: Fully accessible worst: inaccessible typical: Partially accessible Deterministic: The actions have predictable effects best: deterministic worst: non-deterministic typical: Stochastic Static: The world evolves only because of agents’ actions best: static worst: dynamic typical: quasi-static Episodic: The performance of the agent is determined episodically best: episodic worst: non-episodic Discrete: The environment evolves through a discrete set of states best: discrete worst: continuous typical: hybrid Agents: # of agents in the environment; are they competing or cooperating?

  17. Booo hooo 

  18. Additional ideas/points covered Impromptu • The point that complexity of behavior is a product of both the agent and the environment • Simon’s Ant in the sciences of the artificial • The importance of modeling the other agents in the environment • The point that one reason why our brains are so large, evolutionarily speaking, may be that we needed them to outwit not other animals but our own enemies • The issue of cost of deliberation and modeling • It is not necessary that an agent that minutely models the intentions of other agents in the environment will always win… • The issue of bias in learning • Often the evidence is consistent with many many hypotheses. A small agent, to survive, has to use strong biases in learning. • Gavagai example and the whole-object hypothesis.

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