Symbolic AI was the dominant approach to AI before it arrived at machine learning and deep learning. It is also known as Good Old-Fashioned Artificial Intelligence (GOFAI) which is based on the capability of behavior (like reasoning and knowledge representation) that can use explicit rules, logic, and symbols. Opposite to modern AI, which is trained using massive size data, Symbolic AI is based on a priori rules and automated inference.
Yet, what is Symbolic AI and why is it interesting now? Let’s discuss its main concepts, its use, and how it contrasts with current AI methods.
What is Symbolic AI?
Symbolic AI (i.e., rule-based AI) is an AI system keeping and manages a knowledge base store by the use of symbols, inference, and deterministic rules. It mimics human thinking by using logicalistic rules to structured information, which allows computers to perform tasks in a clear and clearly understandable way.
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Key Features of Symbolic AI:
- Rule-Based Logic – Performs the role of an identifier and decider of issues through “if-then” rules.
- Symbolic Representation- Data is expressed as symbols, as opposed to numerical data.
- Reasoning- decisions that are transparently analyzable can be reversed and justified through a transparent articulation of rules.
- Structured Knowledge – Best performance in systems with clearly defined structure and clearly defined rules.
How Does Symbolic AI Work?
Symbolic AI operates through two main components:
- Knowledge Base: A combination of organized knowledge and relationships (e.g., “All humans are mortal.
- Inference Engine: A logical reasoning system that uses rules to infer new findings (e.g., “If Socrates is human, then Socrates is a human, and if Socrates is a human, then Socrates is mortal”.
These systems are rooted in symbolic representation and built to represent and reason about knowledge. Compared with DL models, Symbolic AI does not require large training parameters in the form of data volume but instead depends on a priori rules systems.
In particular, an expert system on medical diagnosis could be based on Symbolic AI to deduce diseases from symptoms through some defined rules.
Applications of Symbolic AI
Currently, deep learning is in the spotlight, while Symbolic AI continues to be used when the application of logical reasoning is not forced.
Key Use Cases:
In medical diagnosis, legal decisions, and finance, there is an application of expert Systems (Rule-based reasoning and fraud detection).
- Natural Language Processing (NLP) – A grammar-based language analyzer and a rule-based chatbot engine.
- Robotics- Supports decision-making and planning in artificial settings.
- Mathematical Theorem Proving – Used in the context of automated reasoning for the solution of very hard logic-based problems.
- Cyber security- Characterization of an attack through the application of rule-based security policies.
However, although Symbolic AI is less modular than deep learning, Symbolic AI may be more advantageous in situations that require understandable reasoning, structured reasoning, and transparency.
Challenges of Symbolic AI
Even though in some contexts Symbolic AI has an edge, several difficulties exist:
- Rigidness: It is unable to generalize from past events but needs to have predetermined rules.
- High Maintenance: Continuous updates of the rules are desired to ensure that the system is always up to date.
- Scalability: It is inefficient in large, complex domains.
- Lack of Learning: Although machine learning builds a system that can autonomously learn from new data (unseen data), it is quite different for Symbolic AI.
However, because the generality is missing, that generality has been given up and replaced with newer learning devices that can learn from new data and generalize to new data inputs.
The Future of Symbolic AI
Unlike machine learning, which is the current “hot spot” of the AI ecosystem, Symbolic AI is being revived due to hybrid AI models which use the inspiration of Symbolic AI complemented by deep learning (DL).
Potential Future Trends:
- Hybrid Artificial Intelligence (AI) systems, defined as AI that integrate Symbolic AI with Machine Learning to realize more intelligent and interpretable reasoning.
- Explainable artificial intelligence (XAI) using symbolic AI to enhance transparency for DL.
- Automated rule generation AI models can automatically dig out logical rules from data.
- Integration in Autonomous Systems – Using symbolic AI for decision making process of autonomous vehicles and robots.
Actually, in a frank way, without the symbolic AI, this is not a complete story for the current AI, but the hybridized with deep learning, this can be a much more powerful and also understandable AI strategy.
Wrap-Up
- Symbolic Artificial Intelligence is an inference-driven system, where, learning and inferring are based on the design of formal representation of knowledge and inference.
- Data consumption is relatively low compared to machine learning, but it actually is based solely on the rule set created by humans.
- Symbolic AI concrete applications are expert systems, Natural language processing and the mathematical provability of theorems, and all three of them show potential for applications in the same.
- Symbolic AI has scalability and generalizability limitations, but is otherwise still of interest in a few applications.
- Hybrid systems leading to the future of AI are systems that bring together the benefits of Symbolic and Deep Learning in order to produce explainable, trustworthy AI models.
In spite (and maybe because) of Symbolic Artificial Intelligence (AI) no longer leads the pack, in current form of artificial intelligence’s development, its parameters still serve as a measure of the progression of the artificial intelligence field.
