AD8413-Artificial-Intelligence-I-Laboratory-

COURSE OBJECTIVES

• To design and implement different techniques to develop simple autonomous agents that make effective decisions in fully informed, and partially observable, settings.
• To apply appropriate algorithms for solving given AI problems.
• To Design and implement logical reasoning agents.
• To Design and implement agents that can reason under uncertainty.
• To understand the Implementation of these reasoning systems using either backward or forward inference mechanisms

LIST OF EXPERIMENTS:

1. Develop PEAS descriptions for given AI tasks
2. Implement basic search strategies for selected AI applications
3. Implement A* and memory bounded A* algorithms
4. Implement genetic algorithms for AI tasks
5. Implement simulated annealing algorithms for AI tasks
6. Implement alpha-beta tree search
7. Implement backtracking algorithms for CSP
8. Implement local search algorithms for CSP
9. Implement propositional logic inferences for AI tasks
10. Implement resolution based first order logic inferences for AI tasks
11. Implement classical planning algorithms
12. Mini-Project

COURSE OUTCOMES

• Implement simple PEAS descriptions for given AI tasks
• Develop programs to implement simulated annealing and genetic algorithms
• Demonstrate the ability to solve problems using searching and backtracking
• Ability to Implement simple reasoning systems using either backward or forward inference mechanisms
• Will be able to choose and implement a suitable technics for a given AI task

SOFTWARE:

• C++ or Java Software