Download CSE 571: Artificial Intelligence

CSE 571: Artificial Intelligence
Instructor: Subbarao Kambhampati
rao@asu.edu
Homepage: http://rakaposhi.eas.asu.edu/cse571
Office Hours: Right after the class
3:15—4:15pm BY560
History
• At ASU, CSE 471/598 has been taught as the main
introductory AI course
– Normally taught by either Rao or Huan Liu
• 571 has been taught as a graduate level AI course
– Didn’t necessarily require 471
– Didn’t necessarily have a breadth aspect
• Nick Findler taught it for a long time and would focus on
distributed AI
• Chitta Baral taught it after Nick and would focus on knowledge
representation
• Last time Rao taught it was in 1996
– Looking back at that syllabus, it looks like 571 I taught then is a subset of
471 as I teach now
CSE 571 This time?
• “Run it as a Graduate Level Follow-on to CSE
471”
• Broad objectives
– Deeper treatment of some of the 471 topics
– More emphasis on tracking current state of the art
– Training for literature survey and independent
projects
Who are you &
what do you want?
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What we did in 471
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Week 1: Intro; Intelligent agent design
[R&N Ch 1, Ch 2]
Week 2: Problem Solving Agents [R&N Ch
3 3.1--3.5]
Week 3: Informed search [R&N Ch 3 3.1-3.5]
Week 4: CSPs and Local Search[R&N Ch
5.1--5.3; Ch 4 4.3]
Week 5: Local Search and Propositional
Logic[R&N Ch 4 4.3; Ch 7.1--7.6]
Week 6: Propositional Logic --> Plausible
reasoning[R&N Ch 7.1--7.6; [ch 13 13.1-13.5]]
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Week 7: Representations for Reasoning
with Uncertainty[ch 13 13.1--13.5]]
Week 8: Bayes Nets: Specification &
Inference[ch 13 13.1--13.5]]
Week 9: Bayes Nets: Inference[ch 13 13.1-13.5]] (Here is a fully worked out example
of variable elimination)
Week 10: Sampling methods for Bayes net
Inference; First-order logic start[ch 13.5; ]
Week 11: Unification, Generalized ModusPonens, skolemization and resolution
refutation.
Week 12: Reasoning with
changePlanning
Week 13: Planning, MDPs & Gametree
search
Week 14: Learning
Chapters Covered in 471 (Spring 09)
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Table of Contents (Full Version)
Preface (html); chapter map
Part I Artificial Intelligence
1 Introduction
2 Intelligent Agents
Part II Problem Solving
3 Solving Problems by Searching
4 Informed Search and Exploration
5 Constraint Satisfaction Problems
6 Adversarial Search
Part III Knowledge and Reasoning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
Part IV Planning
11 Planning (pdf)
12 Planning and Acting in the Real
World
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Part V Uncertain Knowledge and
Reasoning
13 Uncertainty
14 Probabilistic Reasoning
15 Probabilistic Reasoning Over Time
16 Making Simple Decisions
17 Making Complex Decisions
Part VI Learning
18 Learning from Observations
19 Knowledge in Learning
20 Statistical Learning Methods
21 Reinforcement Learning
Part VII Communicating, Perceiving,
and Acting
22 Communication
23 Probabilistic Language Processing
24 Perception
25 Robotics
Part VIII Conclusions
26 Philosophical Foundations
27 AI: Present and Future
Schindler: I could've got more...I could've got more, if I'd just...I could've got more...
Stern: Oskar, there are eleven hundred people who are alive because of you. Look at them.
Schindler: If I'd made more money...I threw away so much money, you have no idea. If I'd just...
Stern: There will be generations because of what you did.
Schindler: I didn't do enough.
Stern: You did so much.
Schindler: This car. Goeth would've bought this car. Why did I keep the car? Ten people, right there. Ten people, ten more people...(He rips the
swastika pin from his lapel) This pin, two people. This is gold. Two more people. He would've given me two for it. At least one. He would've
given me one. One more. One more person. A person, Stern. For this. I could've gotten one more person and I didn't.
Top few things I would have done if I had more time
• Statistical Learning
• Reinforcement Learning; Bagging/Boosting
• Planning under uncertainty and incompleteness
• Ideas of induced tree-width
• Multi-agent X (X=search,learning..)
• PERCEPTION (Speech; Language…)
• Be less demanding more often (or even once…)
Adieu with an Oscar Schindler Routine.
Rao: I could've taught more...I could've taught more, if I'd just...I could've taught more...
Yunsong: Rao, there are thirty people who are mad at you because you taught too much. Look at them.
Rao: If I'd made more time...I wasted so much time, you have no idea. If I'd just...
Yunsong: There will be generations (of bitter people) because of what you did.
Rao: I didn't do enough.
Yunsong: You did so much.
Rao: This slide. We could’ve removed this slide. Why did I keep the slide? Two minutes, right there. Two minutes, two more minutes.. This
music, a bit on reinforcement learning. This review. Two points on bagging and boosting. I could easily have made two for it. At least one. I
could’ve gotten one more point across. One more. One more point. A point, Yunsong. For this. I could've gotten one more point across and I
didn't. 
Things I Know I want to Cover
• Search
– Local vs. Systematic
– Optimization in continuous domains
• Constraint networks
– Tree-width concepts; temporal constraint networks
• Reasoning: Planning
– Temporal planning; belief-space planning, stochastic planning
– POMDPs; DecPOMDPs?
• KR: Templated Probabilistic Networks
– Dynamic probabilistic networks
– Relational Probabilistic networks
• Learning:
– Relational Learning
– Reinforcement learning
Reading Material…Eclectic
• Chapters from the new edition (in
preparation) of R&N (in some cases)
– First reading: Advanced Search
Techniques chapter (Will be distributed
in hardcopy)
• Chapters from other books
– POMDPS from Thrun/Burgard/Fox
– Templated Graphical models from Koller
&Friedman
– CSP/Tree-width stuff from Dechter
• Tutorial papers etc
“Grading”?
• 3 main ways
– Participate in the class actively. Read assigned
chapters/papers; submit reviews before the class; take
part in the discussion
– Learn/Present the state of the art in a sub-area of AI
• You will pick papers from IJCAI 2009 as a starting point
• http://ijcai.org/papers09/contents.php
– Work on a semester-long project
• Can be in groups of two (or, in exceptional circumstances, 3)
Deadlines..
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AAMAS deadline: 10/8/09
KR deadline: 11/10/09
ICAPS deadline: 12/16/09
AAAI deadline: 1/15/10
ICML deadline: ~2/10/10
Discussion
• What are the current controversies in AI?
What are the hot topics in AI?
Pendulum Swings in AI
• Top-down vs. Bottom-up
• Ground vs. Lifted representation
– The longer I live the farther down the Chomsky
Hierarchy I seem to fall [Fernando Pereira]
• Pure Inference and Pure Learning vs.
Interleaved inference and learning
• Knowledge Engineering vs. Model Learning
• Human-aware vs.
The representational roller-coaster in CSE 471
FOPC
First-order
FOPC
w.o. functions
relational
CSP
propositional/
(factored)
atomic
Sit. Calc.
Prop logic
Bayes Nets
State-space
search
Decision
trees
MDPs
Min-max
Semester time 
The plot shows the various topics we discussed this semester, and the representational level at which we discussed them. At the minimum
we need to understand every task at the atomic representation level. Once we figure out how to do something at atomic level, we
always strive to do it at higher (propositional, relational, first-order) levels for efficiency and compactness.
During the course we may not discuss certain tasks at higher representation levels either because of lack of time, or because there simply
doesn’t yet exist undergraduate level understanding of that topic at higher levels of representation..