PYTHON Free Section Starter Challenge 25 min

1. Intelligent Agent Architectures & Perception Loops

Overview: Design an autonomous agent that perceives stimuli through sensors and triggers actions via actuators.

Concept & Syntax Guide

An intelligent agent interacts with its environment in a continuous cycle: Sensors receive percepts, internal belief states are updated, and controllers select rational actions executed by actuators. In this lesson, we implement a stateful vacuum cleaning robot agent in Python.

Hands-On Practice Exercise

Implement Stimulus-Response Agent Cycle

Define an agent that perceives dirty tiles, executes "Move_Forward" and updates belief memory.

Target Output: Agent action executed: Move_Forward, Belief updated

Need a hint? Click here to reveal

Hint: Check syntax carefully and make sure your code contains: Agent action executed: Move_Forward, Belief updated

PYTHON Code Editor

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# Autonomous Agent Sensor-Actuator Loop
class CleaningAgent:
    def __init__(self):
        self.beliefs = {"current_pos": 0, "cleaned_tiles": 0}

    def perceive_and_act(self, percept):
        if percept == "obstacle_free":
            self.beliefs["current_pos"] += 1
            action = "Move_Forward"
            return action, "Belief updated"
        return "Halt", "Belief unchanged"

agent = CleaningAgent()
action, state = agent.perceive_and_act("obstacle_free")
print(f"Agent action executed: {action}, {state}")