Transcript lecture 1 - Knowledge Representation & Reasoning at UIUC!

CS 440 / ECE 448
Introduction to Artificial Intelligence
Fall 2006
Instructor: Eyal Amir
TAs: Deepak Ramachandran (head TA),
Jaesik Choi
Today
• Artificial Intelligence
– Motivation – the dream
– Long-term goals
– Short-term applications
• What you will learn
– Tools, concepts, thought
– What you should know
• Administration of this class
What is Artificial Intelligence?
• Examples:
– Game playing? (chess)
– Robots? (Roomba)
– Learning? (Amazon)
– Autonomous space crafts? (NASA)
• What should AI have?
Example: Shakey (1971)
Example: Mobile Robots (2001)
Example: (2005)
DARPA Grand Challenge
Example: (2005)
DARPA Grand Challenge
Motivation of AI
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Autonomous computers
Embedded computers
Programming by telling
Human-like capabilities – vision, natural
language, motion and manipulation
• Applications: learning, media, www,
manipulation, verification, robots, cars,
help for disabled, dangerous tasks
Long-Term Goals
• Computers that can accept advice
• Programs that process rich information
about the everyday world
• Programs that can replace experts
• Computer programs that can decide on
actions: control, planning, experimentation
• Programs that combine knowledge of
different types and sources
• Programs that learn
Short-Term Goals
• Inferring state of the world from sensors
– Vision
– Natural-language text
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Planning & decision making
Diagnosis & analysis
Learning, pattern recognition
Knowledge & reasoning – acquire,
represent, use, answer questions
What This Course Covers
• Major techniques used in artificial
intelligence
– Vision
– Probabilistic reasoning
– Learning
– Knowledge representation - logic & probability
– Logical reasoning
– Robotic control, Stimulus-Response
– Planning and sequential decision making
Today’s Handouts
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Syllabus and Dates
Readings
Midterms, Final exam
Home assignments
Project assignments
Class website:
http://reason.cs.uiuc.edu/cs440
Project: Autonomous Car
• Project divided into milestones:
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Vision
Tracking
Reactive control
High-level control
Combination
• Teams of 4-6 people – send team composition to
TAs
• After the end of semester... Project continues
until the DARPA Urban Competition (Dec 2007).
What you should know
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Matrix Algebra
Probability and Statistics
Logic
Data structures
C++
What You Will Know
• Matlab
• LISP / Prolog
• Building and reasoning with complex
probabilistic and logical knowledge
• Build autonomous agents
• Create vision/sensing routines for simple
detection, identification, and tracking
• Create programs that make decisions
autonomously or semi-autonomously
Administration of This Class
• Homeworks every week
– see homework #0 given today
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Handouts given in class
Homeworks collected: beginning of class
Homeworks returned within a week
7 days flexible grace period – once spent,
20% off for every late day
More Administration
• Cheating policy:
– 0 on first occurrence, F on second
– Honor code
• Grading formula:
– 3hr credit =
Mid1(20%)+Mid2(20%)+Final(20%)+HW(20%)+Proj(20%)
– 4hr credit =
Mid1(15%)+Mid2(15%)+Final(15%)+HW(15%)+Proj(40%)
– Extra credit for class participation (3%)
• Project grading
– Grade given to group every milestone (5 milestones total)
– Adjusted grade to individual contribution
• Final exam only on last 1/3 material
Office Hours
• Eyal: Thu 4pm-5pm – SC 3314
• TAs (Deepak, Jaesik): Tue 3pm-5pm – SC
0207
• Communication:
– Newsgroup: class.cs440 on nntp.cs.uiuc.edu
– Special requests: [email protected]