Artificial Intelligence | |||
Easy | Medium | Difficult | |
2019 DEC | 1. Fast Fourier transform time complexity 2. Classification algorithms 3. Min-Max algorithm | 1. Gradient descent method 2. Hill climbing algorithm 3. Dempster-Shafer theory for uncertainty 4. Additive rule model 5. Fuzzy Logic | 1. Parallelism time complexity 2. Genetic algorithm-Fitness function |
2016 JUNE | 1. STRIPS 2. Unsupervised Neural Network 3. Fuzzy Set Alpha cut 4. Genetic algorithms Properties | 1. Defuzzification 2. Conjunction/Disjunction 3. MIN MAX Algorithm 4. A*/Greedy best-first/Lowest cost-first 5. Fuzzy Set Alpha cut 6. Sigmoid function 7. Reinforcement learning | 1. Unsupervised algorithm(K-mean cluster) |
2018 DEC | 1.Propositional logic(How many models) 2.Agent 3.First order logic | 1.AND-OR Search algorithm 2.Semi-dynamic environment 3.Heuristic is admissible and heuristic is monotonous 4.Greedy BFS,A* search,Recursive BFS,Iterative-deepening A* Search) 5.Min Max game tree search 6.Propositional logic(Equivalence, Contrapositive, Converse,Implication) | — |
2018 JULY | 1.Environment is uncertain(deterministic/Observable) 2.Simple reflex agent 3.Propositional logic(How many models) | 1.Heuristic search algorithms 2.A* algorithm,informed search strategy 3.Propositional logic 4.First-Order Logic 5.Planning graph data structure 6.Knowledge base 7.Joint probability distribution | — |