Adding Purpose to Your AI UX Testing Agents

Now that your AI UX testing environment is visualized, it’s time to simulate actual user goals—like completing a form or reaching a specific button. This introduces the idea of rewards, the foundation of reinforcement learning.

Table of Contents

What Are UX Tasks in AI Testing?

A task might be something like:

Reaching a login button
Navigating to a settings page
Clicking through a signup wizard

In our grid, we’ll define a “goal cell” where the user is supposed to reach. When they do, they get a reward. We’ll use this feedback to evolve better behavior over time.

Step 1: Define a Goal Zone

Update model.py to add a target cell:

class UXModel(Model):
    def __init__(self, num_agents, width, height):
        ...
        self.goal_position = (width - 1, height - 1)  # bottom-right corner

Step 2: Add Reward Behavior in Agents

Update agent.py so agents move toward the goal and receive a “reward” when reaching it:

class UserAgent(Agent):
    def __init__(self, unique_id, model):
        super().__init__(unique_id, model)
        self.reached_goal = False

    def step(self):
        if self.pos == self.model.goal_position:
            self.reached_goal = True
            print(f"Agent {self.unique_id} reached the goal!")
            return  # No more moves

        # Move toward goal (simple greedy logic)
        x, y = self.pos
        goal_x, goal_y = self.model.goal_position

        new_x = x + (1 if x < goal_x else -1 if x > goal_x else 0)
        new_y = y + (1 if y < goal_y else -1 if y > goal_y else 0)
        new_pos = (new_x, new_y)

        if self.model.grid.is_cell_empty(new_pos):
            self.model.grid.move_agent(self, new_pos)

Step 3: Visualize Task Completion

Update the portrayal in server.py to show goal cells in green:

def agent_portrayal(agent):
    color = "green" if agent.reached_goal else "blue"
    return {
        "Shape": "circle",
        "Color": color,
        "Filled": "true",
        "r": 0.5
    }

Now You Have Purposeful UX Testing Agents

This simulation is now capable of mimicking real UX testing goals. Your AI UX testing agents can now complete tasks and learn from success/failure.

What’s Next?

In Day 6, we’ll look at how to track and visualize user pain points—where they get stuck, how often they fail, and how you can optimize your UX based on those insights.

Tag: #AIUXTesting #UXGoals #ReinforcementLearning

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