Tuesday Jan 8 2013 - Lecture 1

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Department
Computing and Information Science
Course
CIS 3700
Professor
Yang Xiang
Semester
Winter

Description
Tuesday, January 8/2013 CIS 3700 Lecture 1 Lectures • Lecture notes for this lecture not posted online (Unit notes will be posted by unit, but not by lecture) • Emphases ◦ Tasks of agents and algorithms to accomplish them ◦ Algorithm analysis: correctness and complexity ▪ How do you know that the algorithm actually does what it is supposed to do ▪ Can this algorithm accomplish the task in a reasonable amount of time and using a reasonable amount of resources ◦ Significant parts are formal through math expressions ▪ Correctness needs to be precisely defined and analyzed ▪ Because it is formal it is powerful and abstract ▪ Because it is formal it is complex and sometimes difficult to understand • Aspects of lectures ◦ Subject materials are highly coherent and interdependent ▪ Keep on top and do not fall behind ▪ Different agent techniques are built gradually from the simple to more complicated which means that early material is interdependent and the building blocks for later material ▪ Different units of the course are not loosely related, but tightly interwoven ▪ Earlier lectures should be thoroughly understood in order for later lectures to be appreciated ◦ Interaction during lecture ▪ Ask questions at the beginning and during lectures ▪ Study between classes because the professor will call on you by name to answer questions during class • Not meant to put students on the spot or make them feel embarassed, meant to explore the knowledge we have and build on what we know as a class ▪ Small class – he will know all the names ▪ Midterms and exams will include short answer questions that are taken from discussions in class ◦ Regular attendance correlates with high performance ▪ It is not efficient to skip lectures in order to catch deadlines ▪ Material is not particularly easy, and lectures help diffuse difficult concepts ▪ Skipping lectures means that material has to be learned from the textbook, which most students find more difficult ▪ Instead of using the deadlines provided by the instructor, create your own deadlines that will allow you to spread out your workload ◦ Please be on time ▪ We will start promptly at 2:30 Consultation After Class • Office hours ◦ Tuesday 4:00 – 5:00 • After lectures ◦ Brief is best • Appointment ◦ Email me preferred time slots • Questions through email ◦ [email protected] ◦ Questions whose expected answers are brief are preferred ▪ Example: yes/no questions, one-word answer questions ▪ It is difficult to address all portions of comlex questions over email ◦ Face to face discussion is preferred for complex questions ◦ Work on assignments well before the due date, s.t. Face to face discussion can place to address complex questions ◦ Assignments are generally given two weeks to complete to avoid the situation of a flood of complex questions being asked over email three days before the due date Lecture Notes andAssignments • Lecture notes are posted after lectures ◦ Hyperlink for a unit is continuously updated ◦ Ex. IntelligentAgents ▪ Hyperlink does not change as the lectures progress, but the contents of the file will grow as lectures continue ▪ New lectures are continually tagged onto the end of the previous set of lectures until the end of the unit • Assignments ◦ 3 assignments ▪ each assignment will have both short answer questions as well as a programming excercise which need to be submitted differently ◦ Good Practice: work on problems weekly, not monthly ▪ Do not do the assignment all at once in bulk ▪ The questions are organized such that you will be able to complete sections progressively with each set of material covered that week ◦ Hardcopy and softcopy submissions ▪ Short answer portion needs to be submitted in class at the beginning of lecture ▪ Hardcopy submission at start of lecture time ◦ Electronic submission to [email protected] ▪ Due date and time for both portions is the same (ie. Soft submission needs to be done before class) ◦ Late policy • Grading policy • Feedbacks on assignments are given in labs ◦ We do not have a TA this semester, so the labs are not used for learning purposes except immediately following assignment submissions ◦ Labs will be used to hand back and review assignments ◦ Labs will not be held every week, labs will be announced during lecture the week before ◦ If no lab is announced, no lab is being held ◦ NO LAB THIS WEEK • You must complete each assignment and test ◦ Only one component of the course may be failed Other Issues • Electronic device use policy ◦ When students use their laptops for non-course related purposes is distracting to other students ◦ Because this course is a senior course, devices are not banned, but are discouraged unless you are taking notes • Academic integrity ◦ All homework in this course are indiviidual-based ▪ Learning from each other are encouraged on concepts, algorithms, interpretation of assignments, and high level design of implementations • What are the main class structures, what are the main control structures etc... these questions are high level design questions that can be discussed with other students ▪ Written answers and programming codes must be completed independently ▪ Giving your answers to others is also an offence ◦ Minimum penalty: Zero for the homework and report to the school director ▪ Both the copier and the provider receive zero and a report to the school director IntelligentAgents • Objectives ◦ Alternative approaches toAI ▪ In what situation can we say that we succeeded in creatingAI? ▪ The answer to this question is not particularly straight-forward ▪ There are many schools of thought that pertain to when we have succeeded in genuinely creating intelligent systems ▪ We will choose one of the alternatives as our standard ◦ Agent and environment ▪ One way to look at an agent is the entirety of a stand-alone program • This view is actually not practical • Root of this thinking is trying to give birth to a “baby” that can “learn” anything ▪ The modern view ◦ Factors affecting agent evaluation ▪ How can you compare two agents designed to do the same task and say that one is superior than the other (and in what situations) ▪ Th
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