CSE 571 Spring 2001
Semester Syllabus

PROFESSOR: Dr. Chitta Baral


OFFICE HOURS: Tue: 4:40-5:40pm Thu: 4:40-5:40pm

A student should make every effort to utilize the scheduled office hours. However, if you are unable to attend these
office hours, please schedule an appointment at least 24 hours in advance.

PHONE: 480 727-6047

EMAIL: chitta@asu.edu

CLASS SCHEDULE: TuTh 10:40 --11:55 SCOB 302

PREREQUISITE: CSE 471 (UG AI) recommended.


          S0. Introductory slides.

B1. Knowledge representation, reasoning and declarative problem solving using answer sets. Draft.

S1. Slides on search,  heuristics, MDPs, and POMDPs. (application to planning)

S2. Slides on probability, Bayes nets, and causality. (predictions, abductions and counterfactuals)

B2. Causality. Judea Pearl. (Chapter 1 and 2)

ADDITIONAL REFERENCES: Artificial Intelligence: a modern approach.    Russell and Norvig.

Computational Intelligence: a logical approach.  Mackworth, Goebel and Poole.

OBJECTIVES OF THE COURSE: This course is intended to give students deeper understanding of certain crucial AI topics. COURSE OUTLINE: Knowledge representation, reasoning and declarative problem solving. (about 8 weeks)

    Introduction to AnsProlog. B1: Ch1.1, 1.2, 1.3, 2, 3.2, 3.3.1,3.3.4

     Smodels system for AnsProlog.  B1: Ch 8.1.1-8.1.5

     Application of AnsProlog in reasoning and knowledge representation. B1 Ch2.2, 4.11
     (common-sense reasoning, reasoning with defaults, reasoning with incomplete information,
       reasoning with priorities and preferences, inheritance hierarchies.)

     Declarative problem solving with AnsProlog and Smodels. B1 Ch4.1, 4.2, 4.3, 4.7-4.10,4.12, 8.1.6-8.1.9
     (reasoning about actions, temporal reasoning, planning;
      tournament scheduling, job shop scheduling, supply chain planning;
      abductive reasoning, explanation generation, diagnosis;
      constraint satisfaction problems, DCSP;
      combinatorial auctions;
      product configuration.)

Search, heursistics, HSP and HSP-R planning, MDPs and POMDPs. (S1,  B1 Ch 7.3)
    (about 3 weeks)

    Basic search, heuristics serach (A*, IDA*,RTS,LRTA*);
     HSP and HSP-R planning;
     MDP, POMDP and their solution using Real time dynamic programming;
    Computing answer sets of AnsProlog programs. (the Smodels algorithm.)

Probability, Bayes nets and Causality. (S2, B2: Ch1 and 2)
    (about 3 weeks)

    Basic probability notions, Bayes nets, Inference with Bayes nets;
    Causal Bayes nets, Functional causal models (prediction, intervention and counterfactuals);
    Learning Bayes nets and causal models.


          Home works and programming assignments: 25%

           Project and project report: 30%
           (Examples of acceptable projects are: Implement a substantial knowledge representation, reasoning or
            declarative problem solving problem -- from the literature -- in AnsProlog.)

           Tests: 40%

           Class participation: 5%



Students are responsible for making every effort to take exams at the scheduled class time and day.