.
├── state-space-search/
│ └── solution.py # Search algorithms implementation
├── refutation-resolution/
│ └── solution.py # Logic reasoning system
├── decision-trees/
│ └── solution.py # ID3 algorithm implementation
├── genetic-neural-networks/
│ └── solution.py # Evolutionary neural network training
└── README.md
Implementation of search algorithms for pathfinding and puzzle-solving problems.
Algorithms: Breadth-First Search (BFS), Uniform Cost Search (UCS), A* Algorithm Features: Heuristic validation, support for 8-puzzle and map navigation
python solution.py --alg [bfs|ucs|astar] --ss <state_file> --h <heuristic_file>
python solution.py --ss <state_file> --h <heuristic_file> --check-optimisticAutomated reasoning system using propositional logic and resolution.
Features: Resolution algorithm, interactive cooking assistant with knowledge base operations
python solution.py resolution <clauses_file>
python solution.py cooking <clauses_file> <commands_file>ID3 algorithm implementation for classification tasks.
Features: Information gain calculation, tree depth limiting, accuracy evaluation
python solution.py <train_file> <test_file> [depth_limit]Neural network training using evolutionary algorithms instead of backpropagation.
Features: Feedforward networks, genetic optimization, function approximation
python solution.py --train <train_file> --test <test_file> --nn [5s|20s|5s5s] \
--popsize 10 --elitism 1 --p 0.1 --K 0.1 --iter 10000- Python 3.7.4+
- Standard library only (NumPy allowed for genetic neural networks)
- UTF-8 encoding
- State Space Search: Custom text format for states and transitions
- Refutation Resolution: CNF clauses with disjunction 'v' and negation '¬'
- Decision Trees & Genetic Neural Networks: CSV format with headers
Each solution handles the specific file formats and command-line arguments as required by the course autograder system.