CS/EE 710 Artificial Intelligence. Co-taught with CS 422 (Spring 2016 only) Course taught partially on-line. Professor Susan McRoy

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1 CS/EE 710 Artificial Intelligence Co-taught with CS 422 (Spring 2016 only) Course taught partially on-line. Professor Susan McRoy Description: Artificial intelligence (AI) is the intelligence exhibited by machines or software. The course is organized around the problem of designing an intelligent agent, which is a system that perceives its environment and takes actions that maximize its chances of achieving some goal. AI systems address problems that are typically too hard for a direct solution, using heuristics to direct the algorithms to a likely solution. Core tasks include the representation of knowledge and data and algorithms for searching the representations. Topics: Intelligent agents Lisp programming Uninformed search, Informed search, Local search, Adversarial search Constraint satisfaction Rule-based expert systems Logic-based agents FOL, inference, logic-based representations (e.g. Of events and change) Planning Machine learning Communication Textbook Russell, Stuart J.; Norvig, Peter (2009), Artificial Intelligence: A Modern Approach (3rd ed.) Prentice Hall. Other Course Information: Students with Disabilities: Students who have special needs requiring special accommodations please contact the instructor. The Student Accessibility Center

2 Mitchell 112, ) is also an excellent resource and staff is available to discuss concerns. Religious Observances: Students will be allowed to complete examinations or other requirements that are missed because of a religious observance if prearranged with the instructor. Academic Misconduct: The University has a responsibility to promote academic honesty and integrity and to develop procedures to deal effectively with instances of academic dishonesty. Students are responsible for the honest completion and representation of their work, for the appropriate citation of sources, and for respect of other s academic endeavors. University policy prohibits and punishes misconduct, including any act by which a student seeks to claim credit for the work or efforts of another without authorization or citation (plagiarism), forges or falsifies documents, falsely represents his or her academic performance (cheating), or assists other students in any of these acts. Students who violate academic standards as set forth in UWS Chapter 14 and UWM Faculty Document 1686 will be confronted and must accept the consequences for their actions. Students who engage in academic misconduct are subject to a range of sanctions including but not limited to: a failing grade on an assignment or test, a failing grade in the course, and expulsion from the university. Sexual Harassment: Sexual harassment is reprehensible and will not be tolerated by the University. It subverts the mission of the University and threatens the careers, educational experience, and well being of the students, faculty, and staff. The University will not tolerate behavior between or among members of the University

3 community, which creates an unacceptable environment. Incomplete: A notation of incomplete may be given in lieu of a final grade to a student who has carried a subject successfully until the end of a semester but who, because of illness or other unusual and substantiated cause beyond the student s control, has been unable to take or complete the final examination and/or to complete some limited amount of term work.

4 CS/EE 710, Spring 2016 Co-taught with CS 422 Course taught partially on-line. The class meets about monthly, with online quizzes and primary lectures. In class discussion and supplemental content are: 5:30-6:45 Specific dates are: 1/27, 2/10, 2/24, 3/9 (midterm), 3/23, 4/6, 4/20,, 5/4; Final: WEDS 5/18 5:30 pm Week Topic Relevant reading (R&N) 1-2 Jan 27 Introduction (in class) Intelligent Agents Ch. 1, Feb 10 Overview of representation strategies for problem solving in AI Intro to Lisp Discussion of Agents Ch 12.1, 12.2 LISP reference, eg. Graham - Chapters 1-9 Ch 3 ( ) Uninformed Search (1 & 2) Uninformed Search ( 3) Informed Search (1 & 2) Ch 3 ( ) Ch 3 ( ) 5-6 Feb 24 Discussion of search Ch 4 (4.1, 4.2 only) 7-8 Mar 9 Local searching Adversarial search (parts 1-3) Midterm Exam Constraint satisfaction (part 1) Constraint satisfaction (part 2) Ch 5 Ch 6

5 Rule-based expert systems 9-11 Mar 23 Constraint satisfaction and ES discussion; Logical agents (parts 1 & 2) First Order Logic Inference in FOL Logic-based Representation of Events and Change Ch 7 Ch. 8 & 9 Section Apr 6 Discussion of Logic Planning (parts 1 & 2) Machine learning (part 1) Machine learning (part 2) Ch 10 & 11 Sections Apr 20 Discussion 15 May 4 May 18 5:30 PM Communication Term project presentations Final Exam Ch 22 & 23 (parts) Grading Scheme: Undergraduate students: Online quizzes (20 %), 3 programming assignments (30%), midterm (20%) and a final (30%) Graduate Students: Online quizzes (10 %), 4 programming assignments (30%), a term (software) project (25%), a midterm (15%) and a final (20%).

6 Artificial Intelligence 2015 Term Project: An application of AI In class oral presentation on May 4, 2016 Final due date May 20, 2016 This class requires each student to do a software term project.. The purpose of this project is two-fold: the first is to gain additional experience in AI and the second is to give you a chance to explore the application of AI to a problem that is relevant to your academic, professional, or personal life. Common domains for AI have included health, shopping, education, and entertainment. It is expected that you will implement a system that makes effective use of approaches from Artificial Intelligence that are appropriate to your topic. Your project must be implemented using only software that runs without purchasing any licenses on class machines (preferably using clisp, jess, java, or python). You may make use of other freely available existing software, as long as it is attributed correctly to the original sources. You may not submit a project you have already submitted for credit to another course. You may work with others on this project. You must submit a preliminary design, including a 1 page description of the goal of your project, a list of 2-3 references to related work and brief plan for the approach you intend to use, to the Project Design Dropbox by March 12, Your design must specify the topic of the problem and what aspects of AI will be involved and to what aspect of the problem they will be applied. Your final submission will include software, documentation that explains the use and goals of your project, Also, you should submit a narrated screen capture file that includes your 4 slide presentation and a demo of your system that can be up to 10 minutes more. For example, you can record your screen capture with the free version of screencast-o-matic Grading Preliminary Design (5%) Oral presentation (20%) ; (the recorded version will be graded) Software and writeup 75% (you must provide a use case and an implementation that involves AI. Your writeup should explain what makes the approach AI, and provide citations to related work.)