Session No. |
Topic Title |
Topic and subtopic details |
Readings |
Slides |
Exercises |
1 |
Introduction |
What is AI?, Foundations of AI, Current state of AI |
1 |
|
|
2,3 |
Intelligent Agents |
Agents and Environment, Rationality, Nature of Environment, Structure of Agent |
2 |
|
|
4,5 |
Problem Solving Agents, Search |
Problem solving agents, Best-first Search, Uninformed Search: Breadth First Search, Uniform cost search, Depth First Search |
3.1-3.4 |
|
|
6,7 |
Informed Search |
Greedy Best First search, A* search, Designing heuristic functions |
3.5-3.6 |
|
|
8 |
Hands-on Activities |
Course Project Discussions |
|
|
|
9 |
Search in Complex Environments |
Local Search, Search with Non-deterministic Actions, Online Search |
4.1, 4.3-4.5 |
|
|
10 |
Adversarial Search |
Game Theory, Game trees, Stochastic games |
5.1-5.5 |
|
|
11 |
Constraint Satisfaction Problems |
CSP, Inference in CSPs, Backtracking and Local Search for CSPs |
6.1-6.5 |
|
|
12, 13 |
Logical Agents |
Logic, Propositional Logic, Propositional Theorem Proving, Propositional Model Checking, Propositional Logic Agents |
7.1-7.7 |
|
|
14 |
First Order Logic |
Introduction, Syntax and Semantics, Using FOL, Knowledge Engineering |
8.1-8.4 |
|
|
15 |
Automated Planning |
Algorithms and Heuristics for Classical Planning, Hierarchical Planning, Planning in Non-Determinstics Domains, Scheduling |
11.1-11.6 |
|
|
16 |
Probability |
Representing knowledge in uncertain domain, Semantics of Bayesian Network, conditional independence relations, case study |
12 |
|
|
17, 18 |
Probabilistic Reasoning |
Bayesian Networks, Representation, Bayesian Networks Inference |
13.1-13.5 |
|
|
19, 20 |
Probabilistic Reasoning over time |
Time and Uncertainty, inference in temporal models, Hidden Markov Model Kalman Filters |
14.1-14.3 |
|
|
21 |
Making Simple Decisions |
Utility Theory, Utility Functions, Decision Networks, Value of Perfect Information |
16.1-16.6 |
|
|
22 |
Making complex decisions |
Markov Decision Process |
17.1 |
|
|
23, 24 |
Reinforcement Learning |
Learning from Rewards, Passive and Active RL, Policy Search |
22 |
|
|
25 |
Project presentations |
|
|
|
|
26 |
Epilogue |
Summarisation, Quiz |
|
|
|
AIMA V3 Python libraries and notebooks can be downloaded
here or here
Installation instructions if downloaded from the second repository: Extract and navigate to the folder containing the file
setup.py. Then run:
pip install -r requirements.txt
pip install .The libraries can be used as:
from aima3.logic import * etc.
AIMA Version 4 download and installation instructions here.