Knowledge representation & Planning

$($a$)$ Design an expert system for autonomous embodied robots in a warehouse environment that utilizes traditional knowledge representation techniques, including semantic networks, frames, and production rules. Draw the System Architecture: Clearly indicate how each knowledge representation method is integrated into the expert system for the robot. Include components such as environment representation, robot state, decision$-$making, and action planning. Handling Challenges of Ambiguous or Incomplete Information: Explain how your system handles the challenges of ambiguous or incomplete information within the warehouse environment, such as unknown obstacles or dynamically changing item locations. Provide examples to illustrate your approach.

$($b$)$ Consider the same autonomous embodied robot in the large warehouse designed to fetch items for shipping. The warehouse is organized into a grid where each cell can either be empty, contain an item, or be an obstacle. The robot can move up$,$ down, left, or right and can pick up items when it is in the same cell as an item. The robot has a battery limit$,$ which only allows it to make a certain number of moves before needing to return to a charging station. Design a planning algorithm that allows the robot to optimize its path to collect a maximum number of items and return to the charging station before the battery runs out. Discuss the following aspects: The representation of the environment and the robot's state. The planning strategy you would use $($e$.$g$.,$ heuristic search, greedy algorithms, A$*$ algorithm$).$ How the robot can handle dynamic changes in the environment, such as newly placed obstacles or items. Evaluate the efficiency of your proposed solution in terms of computational complexity and the quality of the path generated. Implement same autonomous embodied robot in python code.