# What is a non-deterministic algorithm?

by Stephen M. Walker II, Co-Founder / CEO

## What is a non-deterministic algorithm?

A non-deterministic algorithm is an abstract computation model in which, at each step, there are multiple possible actions to choose from. This means that for any given input, there may be several different outputs depending on the choices made during execution of the algorithm. Unlike a deterministic algorithm, where only one output is produced, a non-deterministic algorithm produces all possible outputs simultaneously. In practice, a non-deterministic algorithm is often simulated using a randomized algorithm or a backtracking search.

## What are the benefits of using a non-deterministic algorithm?

Non-deterministic algorithms explore multiple paths simultaneously, leading to increased efficiency, improved accuracy, better scalability, flexibility, and easier implementation compared to deterministic algorithms.

## What are some of the challenges associated with non-deterministic algorithms?

Non-deterministic algorithms can be more difficult to implement and understand due to their complexity in managing multiple possible paths.

## How can non-deterministic algorithms be used in AI applications?

Non-deterministic algorithms can be used in AI applications by allowing the system to explore multiple possible paths simultaneously, leading to increased efficiency and improved accuracy. For example, a non-deterministic algorithm may be used in a reinforcement learning system to enable it to learn more quickly by exploring multiple actions at once. Additionally, non-deterministic algorithms can be useful in search or optimization problems where finding the optimal solution is important, such as in route planning or scheduling applications.

## What are some of the limitations of non-deterministic algorithms?

Non-deterministic algorithms may struggle with finding optimal solutions in situations where there is a large number of possible paths, as they can become overwhelmed by the need to explore multiple options. Additionally, non-deterministic algorithms can be difficult to implement and understand due to their complexity in managing multiple possible paths.

## More terms

### What is situation calculus?

Situation calculus is a logic formalism designed for representing and reasoning about dynamical domains. It was first introduced by John McCarthy in 1963. The main idea behind situation calculus is that reachable states, referred to as situations, can be defined in terms of actions that lead to them.

### RewardBench (Evaluating Reward Models for Language Modeling)

RewardBench is a comprehensive benchmarking framework designed to evaluate the effectiveness and safety of reward models used in language modeling, particularly for aligning language models with human preferences through Reinforcement Learning from Human Feedback (RLHF).