CSE 571: Artificial Intelligence. Instructor: Subbarao Kambhampati Class Time: 12:40—1:55 M/W email@example.com Homepage: http://rakaposhi.eas.asu.edu/cse571 Office Hours: TBD (probably M/W 8-9AM). CSE 571. “Run it as a Graduate Level Follow-on to CSE 471” Broad objectives
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CSE 571: Artificial Intelligence
Class Time: 12:40—1:55 M/W
Office Hours: TBD (probably M/W 8-9AM)
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]]
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 Modus-Ponens, skolemization and resolution refutation.
Week 12: Reasoning with changePlanning
Week 13: Planning, MDPs & Gametree search
Week 14: Learning
Table of Contents (Full Version)
Preface (html); chapter mapPart 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 9Inference in First-Order Logic10Knowledge RepresentationPart IV Planning 11 Planning (pdf) 12 Planning and Acting in the Real World
Part V Uncertain Knowledge and Reasoning 13 Uncertainty14 Probabilistic Reasoning 15 Probabilistic Reasoning Over Time 16 Making Simple Decisions17 Making Complex DecisionsPart VI Learning18 Learning from Observations19 Knowledge in Learning 20 Statistical Learning Methods21 Reinforcement LearningPart 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
We will skip “beyond classical search” and
start with planning
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..