AI Escalation Policy Learner: Contextual Bandit for Human Handoff

What is a Contextual Bandit?

A contextual bandit is a machine learning framework where an agent must make decisions based on context (or state) to maximize some form of cumulative reward. In the AI Escalation Policy Learner, the context is represented by different ticket types, and the decision is whether to auto-resolve or escalate each ticket.

The goal is to learn a policy that maximizes customer satisfaction while minimizing costs associated with human escalation.

How Does the Contextual Bandit Work?

In this simulation, tickets arrive at different rates and are categorized into various types. The AI agent must decide whether to auto-resolve each ticket or escalate it to a human based on the current state (ticket type) and historical data.

The agent uses an epsilon-greedy strategy: with probability ε, it explores by choosing a random action; otherwise, it exploits by selecting the action with the highest Q-value.

Key Concepts in the Simulation

The exploration rate (ε) controls how often the agent explores new actions. A higher ε means more exploration and potentially faster learning of better policies.

The learning rate (α) determines how much new information overrides old knowledge. A higher α leads to quicker adaptation but might also cause instability.

Why Does It Matter?

By simulating the decision-making process, this tool helps in understanding and optimizing AI systems for customer service applications.

It provides insights into balancing exploration and exploitation, which is crucial for developing efficient and effective AI policies.

Frequently asked questions

Try it live

Everything above runs in your browser — open AI Escalation Policy Learner (2D) — Contextual Bandit for Human Handoff and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.