Types of AI Models

Posted by bsstahl on 2025-11-06 and Filed Under: tools 

It is a common misconception that to have an Artificial Intelligence you must have some form of machine learning. This belief has become so pervasive in recent years that many developers and business leaders assume that AI and ML are synonymous terms, or worse, that LLMs are the definition of AI. However, this couldn't be further from the truth.

Artificial Intelligence is a broad field that encompasses a wide spectrum of computational approaches. While Machine Learning (ML) and Large Language Models (LLMs) are important subfields, AI also includes rule-based logic, search/optimization techniques, and Hybrid approaches. AI is not synonymous with ML or LLM.

Understanding the different types of AI models is crucial for several reasons:

• Choosing the Right Tool: Different problem domains require different approaches. A rules-based system might be more appropriate than a neural network for certain business logic scenarios.
• Explainability Requirements: Some applications demand clear explanations of how decisions are made, which varies across AI model types.
• Resource Constraints: Different AI approaches have vastly different requirements for data, computational power, and development expertise.
• Risk Management: Understanding the strengths and limitations of each approach helps in making informed decisions about where and how to deploy AI systems.

By exploring the full landscape of AI model types, we can make better architectural decisions and avoid the trap of applying machine learning solutions to problems that might be better solved with other AI approaches.

What is AI

An AI is a computational system that behaves rationally.

In the context of AI, rational behavior means making decisions that are optimal or near-optimal given the system's goals, available information, and understanding of the problem domain. This simple definition captures the essence of what distinguishes artificial intelligence from conventional software.

More comprehensively, an AI is a computational system that autonomously evaluates situations and makes decisions by attempting to optimize outcomes based on its model of the problem domain and available data, often while handling uncertainty and incomplete information.

At its core, an artificial intelligence system is designed to make decisions autonomously. Unlike traditional software that simply executes predetermined instructions, an AI system evaluates situations and attempts to make the best possible decision based on two critical components: its understanding of the problem domain (the model) and the available information about the current situation (the data).

This decision-making process is what distinguishes AI from simpler computational systems. The AI doesn't just process data--it interprets that data through the lens of its model to determine the most rational course of action. Furthermore, many AI systems go beyond just making decisions; they can also act on those decisions through automation, creating a complete cycle from data input to actionable output.

The key difference between an AI and a decision support system (DSS) is that the DSS aggregates and presents data such that the user can make the best decision whereas the AI attempts to make the decision itself. This autonomous decision-making capability is what transforms a helpful tool into an intelligent agent.

The Categories of AI Models

I find it useful to categorize AI models into four families: Logical Models; Probabilistic/Learning Models; Optimization/Search Models; Hybrid Models. Each category has distinct characteristics, typical use cases, and trade-offs in explainability and performance.

Logical Models

Logical AI models are perhaps the most familiar to traditional software developers because they operate using deterministic rules and conditional logic. These systems make decisions by following explicit, programmed instructions that can be reduced to if-then statements and boolean logic.

This category includes both object-oriented programming approaches (which encompass most traditional software development) and rules engines. While it might seem counterintuitive to classify conventional programming as AI, these systems qualify as artificial intelligence when they autonomously make decisions based on their programmed logic and available data, rather than simply executing predetermined workflows.

The key distinction is that logical AI systems evaluate conditions and make rational decisions within their domain, even if those decisions follow deterministic patterns. A sophisticated business rules engine that processes complex scenarios and determines appropriate actions is exhibiting rational behavior, even though its decision-making process is entirely transparent and predictable.

Features of Logical Models

• Results Explainable: Generally - Code is highly imperative
• Correctness Understood: Generally - Code is highly imperative
• Solution Discoverability: Low - Code is highly imperative

Probabilistic/Learning Models

Probabilistic and learning models represent the category most people think of when they hear "artificial intelligence" today. These stochastic systems operate by learning patterns from data and making predictions based on statistical relationships rather than explicit rules. Unlike logical models, they don't follow predetermined decision trees but instead develop their own understanding of how to map inputs to outputs.

What makes these models unique is their ability to handle uncertainty and incomplete information. They excel in domains where the relationships between variables are complex, non-linear, or not fully understood by human experts. Rather than requiring programmers to explicitly code every decision path, these systems discover patterns and relationships autonomously through exposure to training data.

These models are most appropriate when you have large amounts of historical data, when the problem domain is too complex for rule-based approaches, or when you need the system to adapt and improve over time. They're particularly powerful for tasks like image recognition, natural language processing, fraud detection, and recommendation systems where traditional programming approaches would be impractical.

However, this power comes with significant trade-offs. The decision-making process is often opaque—even to the system's creators—making it difficult to understand why a particular decision was made. Additionally, their correctness can only be evaluated statistically across many examples rather than being guaranteed for any individual case.

Examples of Probabilistic/Learning Models

• Neural/Bayesian Networks
• Genetic Algorithms

Features of Probabilistic/Learning Models

• Results Explainable: Rarely
• Correctness Understood: Somewhat - Unknown at design time, potentially known at runtime
• Solution Discoverability: High - Solutions may surprise the implementers

Optimization/Search Models

Optimization and search models represent a mathematical approach to artificial intelligence that focuses on finding the best possible solution within a defined solution space. These systems work by systematically exploring possible solutions and applying mathematical techniques to converge on optimal or near-optimal answers to well-defined problems.

What makes these models unique is their foundation in mathematical optimization theory and their ability to guarantee certain properties about their solutions. Unlike probabilistic models that learn from data, optimization models work with explicit mathematical formulations of problems and constraints. They excel at finding provably optimal solutions when the problem can be properly formulated and the solution space is well-defined.

These models are most appropriate for problems with clear objectives, well-understood constraints, and quantifiable outcomes. They shine in scenarios like resource allocation, scheduling, route planning, portfolio optimization, and supply chain management where you need to maximize or minimize specific metrics subject to known limitations. They're particularly valuable when you need to justify decisions with mathematical rigor or when regulatory requirements demand explainable optimization processes.

The trade-off with optimization models is that they require problems to be formulated in specific mathematical ways, which can be limiting for complex real-world scenarios. Their solution discoverability is constrained by how well the problem is modeled and the algorithms chosen for implementation. However, when applicable, they often provide the most reliable and defensible solutions.

Examples

• Dynamic Programming
• Linear Programming

Features

• Results Explainable: Sometimes - dependent on implementation
• Correctness Understood: Somewhat - dependent on implementation
• Solution Discoverability: Limited - solutions will likely be limited by the implementations

Hybrid Models

Hybrid AI models combine multiple AI approaches to leverage the strengths of different model types while mitigating their individual weaknesses. Rather than relying on a single technique, hybrid systems strategically integrate logical, probabilistic, and optimization approaches to solve complex problems that no single model type could handle effectively.

What makes hybrid models particularly powerful is their ability to provide both optimal solutions and explainable reasoning. This addresses one of the key limitations identified by IBM Fellow Grady Booch regarding systems like AlphaGo: while they can make optimal decisions, they cannot explain why those decisions were made.

Hybrid approaches can iteratively combine optimization engines with logical reasoning to create systems that not only find the best solutions but can also explain their decision-making process. For detailed examples of how this works in practice, see my previous articles on AI That Can Explain Why and An Example of a Hybrid AI Implementation, which demonstrate hybrid systems for employee scheduling and conference planning that provide both optimal solutions and clear explanations for why certain constraints couldn't be satisfied.

This approach is most appropriate when you need both optimal solutions and the ability to explain decisions to stakeholders. It's particularly valuable in scenarios like resource allocation, scheduling, and assignment problems where users need to understand not just what the solution is, but why certain trade-offs were necessary.

Features of Hybrid Models

• Results Explainable: Often - Depends on the combination of techniques used
• Correctness Understood: Often - Combines the characteristics of constituent models
• Solution Discoverability: Moderate to High - Can surprise implementers while providing reasoning

Conclusion

Understanding the different types of AI models is essential for making informed architectural decisions and choosing the right approach for your specific problem domain. Each model type offers distinct advantages and trade-offs that make them suitable for different scenarios.

Logical Models are ideal when you need transparent, explainable decision-making processes and have well-defined business rules. They're perfect for regulatory environments, business process automation, and scenarios where every decision must be auditable and justifiable.

Probabilistic/Learning Models excel when dealing with complex patterns, large datasets, and problems where traditional programming approaches would be impractical. They're the go-to choice for image recognition, natural language processing, and scenarios where the system needs to adapt and improve over time.

Optimization/Search Models are most valuable when you have clearly defined objectives, constraints, and need mathematically optimal solutions. They shine in resource allocation, scheduling, and planning problems where efficiency and optimality are paramount.

Hybrid Models combine the best of multiple approaches, providing both optimal solutions and explainable reasoning. They're particularly valuable in complex business scenarios where stakeholders need to understand not just what the solution is, but why certain trade-offs were necessary.

Feature Comparison

It is important to remember that artificial intelligence is not synonymous with machine learning. By understanding the full spectrum of AI approaches available, you can select the most appropriate technique for your specific requirements, constraints, and stakeholder needs. Sometimes the best solution isn't the most sophisticated one—it's the one that best fits your problem domain and organizational context.

Glossary

• AI: Artificial Intelligence, a broad family of computational techniques for solving problems and making decisions.
• ML: Machine Learning, a subset of AI focused on learning from data to improve performance over time.
• LLM: Large Language Model, a class of ML models specialized for natural language understanding and generation.
• DSS: Decision Support System, a traditional software system that supports decision making, distinct from autonomous AI.
• Explainability: The degree to which a system's decisions can be understood by humans.

Tags: development ai