Question-a-Star

Ali Arsanjani
5 min readNov 27, 2023

I have been asked about this topic so many times during the past week that I decided to write it up here. Okay, last week there was some questionable conjecture about a hypothetical AI that supports an algorithm that is supposedly called “Q-*” or “Q-Star”.

What could this mean? I have been asked. Well, it could be bogus or it could have nothing to do with the concepts below. Or it could be related or even true. In any case, I’m also a teacher and we teachers are happy to review some computer science concepts and them speculate on interconnections. Here is my hypothetical rendition.

TL;DR

Q-Star =<could be>= A* + Q-Learning [+/ {in context of } GenAI]

If you combine A* and Q-Learning in an advanced AI system like the purported Q-Star, A* could be utilized for structured problem-solving or navigation tasks, and Q-Learning could manage decision-making processes in dynamic or uncertain environments. This combo would enable a hypothetical Q-Star-based algorithm system to exhibit both efficient problem-solving capabilities and adaptive, intelligent decision-making in various scenarios.

Some Details

[OK, the next section is not just conjecture; just a description of the possible uses of the underlying concepts and algorithms.]

First off, A* (A-Star) and Q-Learning are two distinct algorithms in computer science, particularly in the fields of pathfinding and reinforcement learning (RL) respectively. They could conceivably play a role in the development of an advanced AI systems.

A* (A-Star) Algorithm

The purpose of A* is primarily for use as a pathfinding and graph traversal algorithm. It’s used to find the most efficient path between two points or nodes. How does it work? A* combines features of Dijkstra’s algorithm (which is good at “guaranteeing” the shortest path) and Greedy Best-First-Search (which is typically faster but less accurate). A* uses heuristics to estimate the best path more efficiently. If we were to apply this to an advanced AI system, A* could be used for spatial reasoning, navigation tasks, or any scenario where finding the most efficient route or solution is crucial.

Q-Learning

Q-Learning is a form of model-free RL. That means it doesn’t require a model of the environment and can thus handle problems with stochastic transitions and reward functions. It operates by learning a function (called, you guessed it, the Q-function) that predicts the utility of taking a given action in a given state and following a fixed policy from there onwards. The learning process updates this Q-function based on a reward feedback mechanism. If we were to apply this in an advanced AI system, Q-Learning would be used for implementing decision-making processes, especially where there is high uncertainty or complexity of the environment. For example if the AI system would need to learn optimal actions through a trial and error approach.

Image Generated by Author

Integration

Now if we were to hypothetically integrate these concepts, namely, A-Star and Q-Learning, providing reasoning, and optimization tasks for a Large Language Model (LLM) this would enable a highly sophisticated AI system that would render it capable of complex decision-making, problem-solving, plus efficient navigation through various scenarios.

Use-case: Supply Chain Management

Assume the AI system is tasked with optimizing a delivery route for a logistics company and needs to take into account traffic conditions, delivery time windows, and vehicle constraints.

What would be going on in the head of the AI system (Think CoT)?
Perhaps something like this.

Understand the problem’s scope, objectives, and constraints. To do so, if would need to define the goal/objective (optimize delivery routes), consider constraints (traffic, time windows, vehicle capacity), and success metrics (shortest time, lowest cost) for the optimization problem.

Gather Data and Analyze it. IT would need to identify and analyze relevant data sources. So it would try and collect data on traffic patterns, delivery addresses, time windows, and vehicle details.

Plan the Initial Route Using A-star. Find the most efficient initial path for deliveries. Use the A* algorithm to calculate the shortest possible route between delivery points, taking into consideration road networks and distance.

Make Dynamic Adjustments using Q-Learning. Adapt to real-time changes and optimize routes by using Q-Learning to make real-time decisions based on traffic updates, and go ahead and update and adjust routes dynamically so as to meet the optimization on efficiency.

Use the LLM for Reasoning and Further Optimization. Apply human-like reasoning for handling complex decision-making. This would be implemented most likely by leveraging the LLM’s various reasoning capabilities that would be used to evaluate decisions that the A* and Q-Learning algorithms have made, for example, further optimizations such as re-routing due to unexpected road closures, or weather conditions and delays as well as considering the prioritization of certain deliveries over others.

Implement a Learning Feedback Loop. Capture and record outcomes, to be used as learning input to the model(s) to learn nuances for existing domains (e.g., industry subsectors) and improve future performance. This would be accomplished by analyzing the actually completed routes from the ones not completed, feedback, and all delivery outcomes. This data could then be used to refine the LLM’s reasoning and the parameters of the A* and Q-Learning algorithms.

Reporting and Human Interaction. Communicate the results through report generation, dashboards, and insights on the route optimization taken and the rationale for human operators, and providing explanations and recommendations.

Theoretically, the combination of A* for efficient initial planning, Q-Learning for dynamic adaptation, combined with the LLM’s reasoning for complex decision-making and optimization can be used to create an extraordinarily robust system. Since we are conjecturing; such as system would likely be able to handle both structured and unpredictable scenarios, but also continually learning and improving over time through the feedback loop. This integrated approach of the advanced AI system would hypothetically not only solve complex logistical problems but also be capable of adapting to real-time changes and provide insights that are interpretable by and explainable for human operators, which would pave the way for a more efficient, adaptable and intelligent decision-making process.

[End of weekend hypothetical rumination!]

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Ali Arsanjani

Director Google, AI/ML & GenAI| EX: WW Tech Leader, Chief Principal AI/ML Solution Architect, AWS | IBM Distinguished Engineer and CTO Analytics & ML