What is reasoning in AI?

Recent releases of reasoning models, including DeepSeek-R1, Google’s Gemini 2.0 Flash Thinking, IBM’s Granite 3.2 and OpenAI’s o1 series and o3-mini, have put AI reasoning in the spotlight. Advancements in AI have allowed its capabilities to evolve from following predefined rules to integrating some form of reasoning. And with AI adoption increasing, the role of the technology is shifting.

Rather than just generating answers, today’s reasoning models can reflect on and break down their analysis step-by-step. This allows AI to tackle ever more complex problems, guiding users to take meaningful action.

However, AI reasoning is not a recent capability and has been programmed into AI since its earliest days, according to IBM Research Fellow Francesca Rossi. Preprogrammed reasoning skills gave AI models’ predictions a degree of certainty that might be trusted and relied upon. But newer AI models might lack that certainty and reliability due to their more dynamic reasoning capabilities, Rossi said.

And while AI reasoning is designed to mimic human reasoning, Rossi noted that AI still needs much work to truly reason like humans do.

Reasoning in AI is depicted as a system typically made up of two core components:

    ● Knowledge base

    ● Inference engine

The knowledge base is the backbone of an AI reasoning system. It contains knowledge graphs, ontologies, semantic networks and other models of knowledge representation. These structured forms map real-world entities—such as concepts, domain-specific information, events, facts, objects, relationships, rules and situations—into a structure that AI models can process and understand.

Acting as the brain of an AI reasoning system is the inference engine. It’s powered by trained machine learning models. The inference engine implements the needed logic and reasoning methods to analyze data from the knowledge base and reach a decision.

To illustrate how an AI reasoning system works, let’s take an autonomous robotic floor cleaner as an example. Its knowledge base can contain information about different kinds of floors and what type of cleaning they require. The robot’s machine learning algorithms have also been trained to recognize and classify each floor type based on this knowledge base.

When deployed for cleaning, the robot receives and processes input data, including images and sensor data. Then, it draws upon its knowledge base and training and applies the appropriate reasoning technique to make a real-time decision on its cleaning action, such as vacuuming and mopping hardwood, tile and vinyl floors but only vacuuming carpeted floors.

AI systems implement different reasoning strategies depending on their datasets and the target application. They usually employ a combination of these approaches:

    ● Abductive reasoning

    ● Agentic reasoning

    ● Analogical reasoning

    ● Commonsense reasoning

    ● Deductive reasoning

    ● Fuzzy reasoning

    ● Inductive reasoning

    ● Neuro-symbolic reasoning

    ● Probabilistic reasoning

    ● Spatial reasoning

    ● Temporal reasoning

Abductive reasoning aims to formulate the most likely conclusion based on current available observations. In healthcare, for instance, diagnostic algorithms use abductive reasoning to identify the best possible disease that corresponds to a set of symptoms according to predefined criteria in a knowledge base.

Agentic reasoning allows AI agents to autonomously carry out tasks. Simple agents rely on preset rules, while model-based agents use their current perception and memory in addition to a set of rules to operate in environments. Goal-based agents plan and choose actions that help them reach a goal. Utility-based agents also have an objective to achieve but consider how optimal the outcome will be as well.

Two common reasoning paradigms for agentic AI include ReAct (Reasoning and Action) and ReWOO (Reasoning WithOut Observation). ReAct employs a think-act-observe strategy to solve problems step-by-step and improve upon responses iteratively. ReWOO plans ahead before formulating a response.

Analogical reasoning transfers knowledge from one situation to another. This reasoning methodology draws on analogies to find parallels or similarities between past scenarios and new ones. Research shows that AI models, particularly generative pretrained transformers (GPTs), still struggle with analogical reasoning.¹

Commonsense reasoning uses general knowledge about the world and practical knowledge about everyday life to make decisions. Large language models (LLMs), for example, can deduce patterns from natural language that mirror commonsense reasoning.

Deductive reasoning draws specific conclusions from general facts or wider hypotheses. This means that if the assumption is true, then the conclusion must also be true.

Expert systems are an example of AI systems that depend on deductive reasoning. They’re designed to emulate the reasoning capabilities of human experts. These systems are equipped with a knowledge base that contains information and rules relevant to a particular domain.

Rule-based systems, which are a subset of expert systems, rely on if-then rules to guide their reasoning process. They can be implemented in finance, for instance, to assist with fraud detection.

Fuzzy reasoning caters to degrees of truth instead of the absolute binaries of true or false. It helps deal with vagueness.

For instance, in sentiment analysis, fuzzy reasoning can help evaluate text and determine whether it expresses a positive, negative or neutral sentiment.

Compared to deductive reasoning, inductive reasoning uses specific observations to derive a broader generalization. This type of reasoning is typically implemented in machine learning techniques such as supervised learning, which trains AI models to predict outputs based on labeled training data. Neural networks also harness inductive reasoning to identify the underlying patterns and relationships within datasets.

Symbolic reasoning represents concepts or objects as symbols instead of numbers and manipulates them according to logical rules. Neuro-symbolic AI combines the deep learning capabilities of neural networks with symbolic reasoning for more robust decision-making. This is a fairly recent advancement and is still an emerging area of research.

This reasoning method gauges the statistical likelihood of different outcomes. It helps with decision-making in ambiguous or uncertain conditions, such as when data is limited or if varied results are possible and need to be assessed.

Naïve Bayes classifiers, for example, employ principles of probability for classification tasks. Probabilistic reasoning is also used for natural language processing (NLP) tasks and generative AI applications.

Spatial reasoning allows intelligent systems such as autonomous vehicles and robots to tackle three-dimensional spaces. This type of reasoning can incorporate geometric modeling to understand shapes and surfaces and pathfinding algorithms that help determine the shortest or most optimal route to efficiently navigate dynamic environments.

Spatial reasoning can also integrate convolutional neural networks (CNNs), which use three-dimensional data for image classification and object recognition tasks.

Through temporal reasoning, AI systems learn to process time-specific data and understand sequences of events, allowing them to formulate plans, schedule tasks or build forecasts.

Recurrent neural networks (RNNs), for instance, are trained on sequential or time series data to infer logical conclusions about future outcomes. An RNN might be used to project future sales, predict stock market performance or generate weather forecasts.

Reasoning can lead to more powerful AI applications, but it also has its limits. Here are some challenges associated with AI reasoning systems:

    ● Bias

    ● Computational costs

    ● Interpretability

Biases that might be present in training data can trickle down to AI reasoning systems. Diversifying data sources can help mitigate bias. Moreover, incorporating human oversight, integrating AI ethics within algorithmic development and establishing AI governance are crucial to make sure these reasoning systems arrive at decisions ethically and fairly.

Complex reasoning tasks require significant computing power, making it difficult to scale these systems. Enterprises must optimize AI models for efficiency while preserving accuracy. They must also be prepared to invest in the necessary resources for developing, training and deploying these reasoning systems.

AI reasoning systems, especially the more complex ones, are often black box models. They lack transparency on their reasoning techniques and decision-making processes. Several methods can help establish interpretability in AI models, and creating interpretable systems can help build trust with users.

Reasoning in AI can be valuable in enterprise contexts, aiding in problem-solving and automation of complex tasks. Here are some industries that can benefit from AI reasoning systems:

    ● Customer service

    ● Cybersecurity

    ● Healthcare

    ● Manufacturing

    ● Robotics

Conversational AI, such as chatbots or virtual agents, can use AI reasoning for more accurate responses to customer queries. Retailers can also harness reasoning for their recommendation engine, suggesting relevant items for a more personalized and enhanced user experience.

AI reasoning systems can support cybersecurity technologies in monitoring and detecting threats. They can also swiftly recommend an appropriate course of action, helping improve response times.

AI reasoning models can aid medical diagnostics and suggest treatment plans. They can also help accelerate drug discovery, finding the best molecules to test for drug development.

AI reasoning systems can assist with demand forecasting for improved inventory control. Predictive maintenance systems can also rely on AI reasoning to identify equipment issues in real time and recommend timely fixes.

When equipped with reasoning abilities, robots can operate more effectively in real-world spaces and better interact with humans and other machines. They can make logical inferences autonomously, helping enhance their adaptability, environment mapping, navigation and object manipulation skills.