# Artificial Intelligence

2010 - 2020

## Overview

In this module, problem-solving methods from the perspective of the rational agent (one of the four definitions of AI) are investigated. Problems are defined assuming a world for the agent (environment). This world includes a set of states. An agent has a set of actions with which it moves between states. The difference in the structure of the states as well as the agent's level of knowledge from the next state creates a category in artificial intelligence.

what about the next state? | ||||
---|---|---|---|---|

determined | stochastic | unknown | ||

how are states in this environment | Atomic (simple) |
SEARCH | Stochastic Games Markov Decision Process |
Reinforcement Learning |

Factored (Variables) |
CSP LOGIC |
Bayes Net | ||

Structured | First Order Logic |

## Syllabus

\[\color{blue}{Part 1: Introduction to AI}\]- Introduction
- History of AI, Definition of AI, Agents and Environments, Rationality, Utility
- Types of Agents, Search Problem

- Uninformed Search
- BFS, DFS, DLS, IDS, BS, UCS

- Informed Search
- Heuristic Functions
- Greedy Search, A*, IDA*, RBFS, MA*, SMA*

- Constraint Satisfaction Problem
- Constraint Graphs, Backtracking Algorithm
- Improving Backtracking
- Filtering, Forward Checking, Arc Consistency
- Ordering, MRV, LCV,
- Structure, tree-structured CSP

- Local Search
- Optimization Problems
- Iterative improvement, min-conflict heuristic
- Hill Climbing, Random Walk
- Simulated Annealing, Beam Search
- Genetic Algorithm

- Adversarial Search and Games
- Forms of Games, Zero-sum game
- Minimax Theorem, Alpha-Beta Search, Pruning.
- Stochastic Games, Expectation Minimax

- Logical Agents
- Propositional Logic, First Order Logic
- Satisfiability, Planning
- Logic Representation(Syntax and Semantic), Logic Inference(Forward Chaining, Backward Chaining, Resolution), Unification
- ProLog
- Knowledge Representation, Ontology, Objects

- Markov Decision Process
- Definition of Markov Decision Process(MDP), State, Action, Transition Functions, Reward
- Policy, Optimal Policy, State-value, Q-value, Bellman Equation
- value iteration, policy iteration
- Convergence, exploration, exploitation, regret

- Reinforcement Learning
- Model-based RL, Model-free RL
- Passive RL, Active RL
- Temporal-Difference Value Learning,
- Q-Learning
- đťś–-Greedy
- Approximate Q-learning

- Uncertainty and Probability
- Random variables, Probability, PDF, Joint Probability, Conditional Probability
- Product Rule, Bayesâ€™ theorem, Chain Rule
- Independence, conditional Independence

- Bayes Net
- Bayesian Networks
- Representation
- Conditional Independence Semantics
- Causal Chains, Common Cause, Common Effect

- Active / Inactive Paths

- Conditional Independence Semantics
- Inference
- Exact inference
- Enumeration
- Variable Elimination

- Approximate Inference (Sampling)
- Prior Sampling
- Rejection Sampling
- Likelihood Weighting
- Gibbs Sampling

- Exact inference

- Hidden Markov Model
- Markov chain, Markov property, Markov Model, Hidden Markov Model
- Evaluation
- Forward Algorithm
- Backward Algorithm

- Most likely sequence of states(Decoding)
- Viterbi algorithm

- Training
- Viterbi training
- Baum Welch Algorithm

- Particle Filtering, Kalman filter