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)

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

    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.