Consider a scenario where a rabbit is trapped on cave. Given below is the picture of a cave as a grid. The task of the rescue robot is to help the rabbit to find the optimal path to reach the finish position. It can move to the north, south, west and east direction. While navigating through the environment it has obstacles like walls, fire and Bush plant. For each transition, a path cost of +3 is added in search. Assume that the robots vision sensors are sensitive to the exposure to the fire and whenever it tries to move towards the fire cell resulting in incurring an additional penalty of +5 cost. In addition, every time the rabbit attempts to move towards the bush plant cell, an extra transition cost of +1 is incurred. Use Manhattan distance as a heuristic wherever necessary. Use the following algorithms to find the optimal path. a. A* Algorithm b. Genetic Algorithm GIVE THE PYTHON CODE FOR BOTH THE ALGORITHMS. 1. Explain the PEAS (Performance measure, Environment, Actuator, Sensor.) for your agent. (20% marks)

2. Define the heuristic and or fitness function for the given algorithms and the given problem. [10% weightage]

3. Use appropriate data structures and implement search algorithms (informed and local search) to find and print the optimal path sequence with costs. (60% marks)

4. Include code in your implementation to calculate the space complexity and time complexity for the informed search and print the same. For local search interpret the significance of the hyperparameters if any applicable . (10% marks)