Forest Grid: The game is played on a grid representing the forest terrain. Each cell

may contain obstacles, artifacts, the player character, or the forest guardian.

Player Movement: Players can move their character in cardinal directions $($up$,$ down,

left, right$).$ Players cannot move through obstacles or the guardian.

Guardian Patrolling: The forest guardian patrols the forest grid, following a

predetermined path or behaviour tree. It moves one cell at a time, searching for the

player.

Artifact Collection: Artifact are scattered throughout the forest grid. Players can

collect treasures by moving onto the cell containing them.

Guardian Detection: If the guardian and player characters collide, the player is

caught, and the game ends.

Win Condition: Players win by collecting all artifacts without being caught by the

guardian. The game also ends if the player is caught by the guardianI want to build an intelligent agent for a game. Below are the agent PEAS analysis:

$$ Partially Observable: The Agent percepts are limited to its immediate surroundings. It can only detect objects that are in its line of sight, or within a certain range. Objects outside its field of view are not directly observable.

$$ Dynamic: Environment has objects interacting over time, like the player collecting artifacts. The agents perception of the environment may change rapidly as a result of these interactions

$$ Sequential: The agent behaviour depends on sequential interactions with the player and the game environment. It considers the sequence of actions and states over time, adapting its behaviour based on past interactions.

$$ Deterministic: The agent given the same input will do the same action. If an agent detects a player, it will pursuit the player. Its movement when patrolling is random, however, given certain stimuli it will have finite option of decisions to make.

$$ Continuous: The agent speed and actions can change based on changes in the environment

Along with the PEAS analysis:

And from that, i would like to use behavioral trees written in python. How can i go about writing the intelligent agent for the simulation?