Introduction to Artificial Intelligence - academic course
- lab_1.1 - Search Space vol. 1
- lab_1.2 - Search Space vol. 2
- lab_2 - Evolutionary and Genetic Algorithms
- lab_3 - Deterministic two-player games
- lab_4 - Regression and Classification
- lab_5 - Artificial neural networks
- lab_6 - Reinforcement learning (RL)
- lab_7 - Bayesian Models
Search Space - Knapsack problem
Task:
Find the optimal solution by exhaustive search. Solve the problem using a heuristic: pack items into a backpack in order of decreasing value-to-weight ratio.
Search Space - Stochastic Gradient Descent
Task:
Please implement the steepest descent/ascent method. We calculate the gradient numerically. Apply the method to find the minimum of the Booth function in 2 dimensions, then to find the minimum of functions 1 to 3 from CEC 2017 in 10 dimensions.
Evolutionary and Genetic Algorithms - evolutionary algorithm
Task:
Implement a classic evolutionary algorithm without crossover, using tournament selection and elitist succession. The available budget is 10000 evaluations of the objective function. We are optimizing functions number 4 and 5 from CEC 2017 in 10 dimensions. The bounds of the search space are -100 and 100.
Deterministic two-player games - MiniMax checkers
Task:
Implement the alpha-beta pruning min-max algorithm and apply it to the game of checkers/draughts. Let the evaluation function return the difference between the player's and the opponent's board state.
Regression and Classification - ID3 algorithm
Task:
Implement the ID3 classifier (decision tree) with nominal attributes and identity tests. Provide accuracy and confusion matrices for given datasets.
Artificial neural networks - two-layer perceptron
Task:
Implement a two-layer perceptron and train it to represent the function J: [-5,5] โ R
, given by the formula: J(x) = sin(xsqrt(5))+cos(xsqrt(3))
.
Reinforcement learning (RL) - Qlearning agent
Task: Implement the Q-Learning algorithm and use it to determine a decision policy for the FrozenLake8x8 problem.
In addition to investigating the default reward system (1 for reaching the goal, 0 otherwise), please propose your own system of rewards and penalties, and then compare the results achieved with the default system.
Bayesian Models - Random data generator using Bayesian network distribution
Task:
Implement a random data generator that follows the distribution represented by a given Bayesian network. The network describes the dependencies between (binary) random variables and is provided as a graph structure and conditional probability tables in a text file. Divide the generated set and use it to train and test the classifier created in previous exercises.
git clone https://github.com/tilleyd/cec2017-py
cp -R cec2017-py/cec2017 .