May 22 / Neeraj Kumar | Rahul Rai

What is the difference between symbolic systems and machine learning?

Introduction to Symbolic Systems and Machine Learning:

Why is it Important to Understand Different Machine Learning Algorithms img-1
Symbolic Artificial Intelligence (AI):
ymbolic AI is a unique approach in AI research characterized by its reliance on high-level symbolic representations of problems, logic, and search. Unlike other AI techniques, such as machine learning and deep learning, Symbolic AI does not require vast amounts of training data.
Machine Learning (ML):

ML is a data-driven approach that learns patterns from data without explicit programming. ML is the process of training a piece of software, called a model, to make valuable predictions or generate content from data.

Key Characteristics of Symbolic Machine Learning

Why is it Important to Understand Different Machine Learning Algorithms? img-2
Symbolic machine learning stands out due to three main features that enhance human decision-making:
1. Learning from Limited Data:
Unlike traditional machine learning methods that require vast amounts of data, symbolic machine learning can build accurate predictive models from minimal data.
2. Explainability:
The models generated by symbolic machine learning can be easily understood and articulated in human language, making them more accessible and trustworthy.
3. Incorporating Human Feedback: These systems are designed to learn and adapt based on feedback from human users, making them more personalized and effective over time.

Overcoming Data Limitations

Traditional deep learning and statistical machine learning thrive on large datasets to perform tasks such as classification and specific problem-solving. However, symbolic machine learning excels in scenarios where data is scarce. It can derive high-level decision-making models without the need for extensive data, making it a powerful tool in various applications.
Real-World Applications: Urban Mobility
An exemplary application of symbolic AI is in urban mobility. Imagine navigating from point A to point B in a city without knowing the best route. Symbolic AI systems can offer personalized suggestions based on general criteria like the shortest path, avoiding tolls, or minimizing payments. These systems can learn and adapt to an individual's preferences, making daily commuting decisions more tailored to personal needs.
Superior Personalization with Symbolic AI
Research has demonstrated that symbolic AI can outperform traditional machine learning by providing truly personalized recommendations. By learning from fewer examples, symbolic AI can extract generalizable models from individual behaviors, leading to more accurate and personalized outcomes.

The fundamental principles of Symbolic Systems and Machine Learning:

Symbolic Systems
Logic and Rule-Based Reasoning:
Symbolic systems are grounded in formal logic to represent knowledge and draw inferences. These systems use logic-based rules to articulate relationships, impose constraints, and execute reasoning steps. Imagine an expert system in the medical field: it employs logical rules to diagnose potential diseases based on observed symptoms.

Knowledge Representation: A cornerstone of Symbolic AI is its emphasis on explicit knowledge representation. This is achieved through:

Ontologies: These hierarchical structures organize knowledge into concepts and relationships, enabling machines to navigate complex domains.

Semantic Networks: These are graph-based representations where entities are interconnected by semantic links, facilitating deeper reasoning and understanding.

Expert Systems: Expert systems are specialized applications that emulate human expertise in specific areas. They utilize symbolic rules to solve problems, offer recommendations, and make decisions. Examples include systems for medical diagnosis, financial advice, and technical troubleshooting.

Machine Learning

Data-Driven Approaches: Machine Learning (ML) distinguishes itself by leveraging data to identify patterns and make predictions. Unlike symbolic systems that rely on explicit rules, ML models learn to generalize from examples. This data-driven approach enables ML to tackle intricate tasks challenging for rule-based systems.

Algorithms and Models: Machine Learning comprises various algorithms and models, each suited to different types of tasks:

Supervised Learning: In this approach, models learn from labeled data, which consists of input-output pairs. Examples include linear regression, support vector machines (SVM), and neural networks.

Unsupervised Learning: Here, models discover patterns in unlabeled data with clustering and dimensionality reduction standard techniques.

Reinforcement Learning: Agents learn by interacting with an environment, aiming to optimize rewards. This is exemplified by game-playing AI.

Neural Networks: These deep learning models consist of interconnected layers. Specific types include convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for handling sequences.


Decision Trees: These are hierarchical structures used for classification or regression tasks.

Support Vector Machines (SVM): Effective for binary classification tasks.

Neural Networks: This category includes multilayer perceptrons (MLPs), CNNs, and RNNs.

Ensemble Methods: These combine multiple models to improve performance, such as Random Forests and Gradient Boosting.

The critical differences between Symbolic Systems and Machine Learning:

Knowledge Representation
Symbolic Systems:

Knowledge is explicitly encoded in symbolic systems using predefined rules, logic, and formal structures. Human experts meticulously craft and encode this knowledge, resulting in structured representations such as ontologies, semantic networks, and expert systems. These systems rely heavily on human input to define the relationships and constraints that guide reasoning.

Machine Learning:

Contrastingly, machine learning models infer knowledge from data without relying on explicit rules. Knowledge in ML emerges from the statistical relationships found within the training data. Examples of this approach include neural networks, decision trees, and clustering algorithms. The data-driven learning process allows these models to uncover patterns and insights autonomously.

Learning Process

Symbolic Systems:

The learning process in symbolic systems involves human experts manually encoding rules. These rules form the backbone of reasoning and decision-making within the system. The creation and maintenance of the knowledge base are labor-intensive, requiring domain-specific expertise to ensure accuracy and relevance.

Machine Learning:

Machine learning employs automatic pattern recognition. ML models learn from data by independently discovering patterns, correlations, and representations. During training, these algorithms adjust their parameters to fit the data distribution, enabling them to learn from experience. This automated learning process enhances the system's ability to adapt and improve over time.

Flexibility and Adaptability

Symbolic Systems:

Symbolic systems are typically rigid and deterministic, strictly adhering to predefined rules. This rigidity can be a limitation when the system encounters novel or unforeseen situations. Any changes or updates to the system's knowledge base require manual intervention to revise the rules and representations.

Machine Learning:

In contrast, machine learning models are adaptive and probabilistic. They can adjust to new data, handle uncertainty, and generalize well from their training experience. ML systems provide probabilistic predictions, offering uncertainty estimates and enhancing their robustness in dynamic environments.

Integration of Both Approaches:

In recent years, the integration of symbolic reasoning and machine learning has gained prominence, leading to the development of more powerful and flexible AI systems. This fascinating combination harnesses the strengths of both approaches to create hybrid systems capable of tackling a wide range of challenges.

Hybrid Systems

Hybrid AI systems merge symbolic reasoning's explicit knowledge representation and logical inference capabilities with machine learning's data-driven adaptability and generalization strengths. By leveraging these complementary features, hybrid systems aim to overcome each approach's inherent limitations.

Examples of Integration:-

IBM Watson: IBM Watson is a prime example of a hybrid AI system, seamlessly combining symbolic reasoning with machine learning techniques.

Question Answering: Watson's victory on "Jeopardy!" showcased its ability to understand natural language questions, retrieve relevant information, and apply symbolic reasoning to provide accurate answers. This achievement highlighted the power of combining ML for natural language processing with symbolic reasoning for logical inference.

Healthcare: In the medical domain, Watson for Oncology exemplifies the hybrid approach by using machine learning to analyze extensive medical literature and patient data. Simultaneously, it incorporates expert guidelines and symbolic rules to offer precise treatment recommendations, demonstrating its ability to blend data-driven insights with established medical knowledge.

Chatbots: Watson Assistant represents another successful integration, combining rule-based dialog management with machine learning-based intent recognition. This allows the system to manage conversations efficiently while accurately understanding and responding to user intents.

Applications and Use Cases:

Symbolic Systems:-
Medical Diagnosis: Symbolic systems are extensively used in medical expert systems to diagnose diseases based on patient symptoms, medical history, and lab results. For instance, a symbolic system might analyze symptoms like fever, cough, and fatigue to suggest potential illnesses, aiding doctors in making informed decisions.
Legal Reasoning: In the legal field, symbolic AI assists professionals by analyzing legal texts, case law, and statutes. Symbolic systems can provide legal advice, help with contract analysis, and predict legal outcomes. These systems offer valuable support in navigating complex legal landscapes by encoding legal knowledge into logical rules.
Automated Theorem Proving: Symbolic reasoning is pivotal in formal logic and mathematics. Automated theorem provers leverage symbolic methods to prove mathematical theorems, significantly advancing research and education in mathematical sciences.
Machine Learning:-
Image Recognition: Machine learning models, particularly convolutional neural networks (CNNs), excel at recognizing objects, faces, and image patterns. Image recognition applications include self-driving cars identifying pedestrians and traffic signs, medical imaging detecting tumors, and content moderation flagging inappropriate images.
Natural Language Processing (NLP): Machine learning techniques are at the heart of NLP tasks such as sentiment analysis, machine translation, and chatbots. Recurrent neural networks (RNNs) and transformer models like BERT learn contextual representations for text, enabling machines to understand and generate human language with remarkable accuracy.
Recommendation Systems: ML algorithms power recommendation systems by analyzing user behavior, such as browsing history and purchase patterns, to suggest products, movies, or music. Collaborative filtering and matrix factorization are commonly employed to enhance user experience by providing personalized recommendations.

Advantages and Disadvantages of Symbolic Systems and Machine Learning

Symbolic Systems:-
Explainability: Symbolic systems offer clear, human-readable rules and logic, allowing experts to understand and interpret the reasoning behind decisions easily. This transparency fosters trust and accountability.
Transparency: The explicit representation of knowledge in symbolic systems clarifies how conclusions are reached, enhancing transparency.
Precise Reasoning: Symbolic AI follows deterministic rules, ensuring precise and reliable reasoning, which is particularly valuable in fields requiring high accuracy.
Scalability Issues: Symbolic systems often struggle with scalability. Managing and updating rules becomes increasingly complex and unwieldy as the knowledge base grows.
Brittleness: These systems can be sensitive to minor changes. A tiny rule alteration can lead to unexpected and undesirable behavior, making them less robust.
Manual Knowledge Engineering: Creating and maintaining the rules in symbolic systems requires significant human effort. This process is time-consuming and prone to errors, limiting the system's efficiency and adaptability.
Machine Learning
Scalability: Machine learning models excel at handling large datasets efficiently. They can process and learn from vast amounts of data, making them highly scalable.
Adaptability: ML models can adapt to new information and changing environments, continuously improving performance.
Handling Large Datasets: Machine learning techniques are particularly effective when dealing with massive data volumes, uncovering patterns and insights that human analysts might miss.
Black-Box Nature: Many machine learning models, especially neural networks, are often seen as black boxes due to their lack of interpretability. This makes it difficult to understand how decisions are made.
Need for Large Data: Effective ML model training requires substantial data. Insufficient data can lead to poor performance and unreliable predictions.
Less Interpretability: Unlike symbolic systems, machine learning models do not provide explicit rules for decision-making, making it challenging to explain the rationale behind their outputs.


The synergy between symbolic reasoning and machine learning holds immense potential for transforming the field of artificial intelligence. By combining the explicit, rule-based logic of symbolic systems with the adaptive, data-driven capabilities of machine learning, we can create hybrid models that surpass the limitations of each approach. This integration is not merely a theoretical advancement but a practical pathway to developing AI systems that are more versatile, intelligent, and robust.
Moreover, the integration of symbolic reasoning and machine learning can drive innovation in areas such as education, where AI can provide personalized learning experiences and instant feedback, and in environmental monitoring, where it can predict and mitigate the impacts of climate change. By harnessing the strengths of both approaches, AI systems can offer more reliable and context-aware solutions, enhancing their usefulness across diverse fields.

Follow Us on 


About Us

Contact Us

Hire Our Students

Blog Section 

Our Office

South Carolina, 29650,
United States
Waxhaw, 28173,
United States
Created with