Symbolic Artificial Intelligence

Neural Networks can be described as computational models that are based on the human brain’s neural structure. Each neuron receives inputs, applies weights to them, and passes the result through an activation function to produce an output. Through a process called training, neural networks adjust their weights to minimize the difference between predicted and actual outputs, enabling them to learn complex patterns and make predictions.

However, these algorithms tend to operate more slowly due to the intricate nature of human thought processes they aim to replicate. Despite this, symbolic AI is often integrated with other AI techniques, including neural networks and evolutionary algorithms, to enhance its capabilities and efficiency. Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go.

The Rise and Fall of Symbolic AI

You can foun additiona information about ai customer service and artificial intelligence and NLP. This resulted in AI systems that could help translate a particular symptom into a relevant diagnosis or identify fraud. We use symbols all the time to define things (cat, car, airplane, etc.) and people (teacher, police, salesperson). Symbols can represent abstract concepts (bank transaction) or things that don’t physically exist (web page, blog post, etc.). Symbols can be organized into hierarchies (a car is made of doors, windows, tires, seats, etc.). They can also be used to describe other symbols (a cat with fluffy ears, a red carpet, etc.).

The ontology also includes properties such as

“hasColor,” “hasWeight,” and “ownedBy,” which describe the

attributes and relationships of vehicles. One of the seminal moments in the history of Symbolic AI was the

Dartmouth Conference of 1956, organized by John McCarthy. This

conference brought together leading researchers from various disciplines

to discuss the possibility of creating intelligent machines. It was at

this conference that the term “Artificial Intelligence” was coined by

McCarthy, and the key ideas and goals of AI were articulated. RAAPID’s retrospective and prospective solution is powered by Neuro-symbolic AI to revolutionize chart coding, reviewing, auditing, and clinical decision support. Our Neuro-Symbolic AI solutions are meticulously curated from over 10 million charts, encompassing over 4 million clinical entities and over 50 million relationships.

As an AI expert with over two decades of experience, his research has helped numerous companies around the world successfully implement AI solutions. His work has been recognized globally, with international experts rating it as world-class. He is a recipient of multiple prestigious awards, including those from the European Space Agency, the World Intellectual Property Organization, and the United Nations, to name a few. With a rich collection of peer-reviewed publications to his name, he is also an esteemed member of the Malta.AI task force, which was established by the Maltese government to propel Malta to the forefront of the global AI landscape. In Layman’s terms, this implies that by employing semantically rich data, we can monitor and validate the predictions of large language models while ensuring consistency with our brand values. Google hasn’t stopped investing in its knowledge graph since it introduced Bard and its generative AI Search Experience, quite the opposite.

AI programming languages

Symbolic AI has been criticized as disembodied, liable to the qualification problem, and poor in handling the perceptual problems where deep learning excels. In turn, connectionist AI has been criticized as poorly suited for deliberative step-by-step problem solving, incorporating knowledge, and handling planning. Finally, Nouvelle AI excels in reactive and real-world https://chat.openai.com/ robotics domains but has been criticized for difficulties in incorporating learning and knowledge. Knowledge-based systems have an explicit knowledge base, typically of rules, to enhance reusability across domains by separating procedural code and domain knowledge. A separate inference engine processes rules and adds, deletes, or modifies a knowledge store.

The neural component of Neuro-Symbolic AI focuses on perception and intuition, using data-driven approaches to learn from vast amounts of unstructured data. Neural networks are

exceptional at tasks like image and speech recognition, where they can identify patterns and nuances that are not explicitly coded. On the other hand, the symbolic component is concerned with structured knowledge, logic, and rules.

Data from the Product Knowledge Graph is utilized to fine-tune dedicated models and assist us in validating the outcomes. Although we maintain a human-in-the-loop system to handle edge cases and continually refine the model, we’re paving the way for content teams worldwide, offering them an innovative tool to interact and connect symbolic ai example with their users. Unstructured data is any type of data that does not have a predefined structure, such as text, images, and videos. This data type can be difficult to understand and process using traditional methods. However, LLMs can be used to extract and organize knowledge from unstructured data in a number of ways.

In the CLEVR challenge, artificial intelligences were faced with a world containing geometric objects of various sizes, shapes, colors and materials. The AIs were then given English-language questions (examples shown) about the objects in their world. Take, for example, a neural network tasked with telling apart images of cats from those of dogs. During training, the network adjusts the strengths of the connections between its nodes such that it makes fewer and fewer mistakes while classifying the images. Another area of innovation will be improving the interpretability and explainability of large language models common in generative AI.

We are already integrating data from the KG inside reporting platforms like Microsoft Power BI and Google Looker Studio. A user-friendly interface (Dashboard) ensures that SEO teams can navigate smoothly through its functionalities. Against the backdrop, the Security and Compliance Layer shall be added to keep your data safe and in line with upcoming AI regulations (are we watermarking the content? Are we fact-checking the information generated?). The platform also features a Neural Search Engine, serving as the website’s guide, helping users navigate and find content seamlessly.

The grandfather of AI, Thomas Hobbes said — Thinking is manipulation of symbols and Reasoning is computation. As such, Golem.ai applies linguistics and neurolinguistics to a given problem, rather than statistics. Their algorithm includes almost every known language, enabling the company to analyze large amounts of text. Notably because unlike GAI, which consumes considerable amounts of energy during its training stage, symbolic AI doesn’t need to be trained.

A newborn starts only with sensory abilities, the ability to see, smell, taste, touch, and hear. These sensory abilities are instrumental to the development of the child and brain function. They provide the child with the first source of independent explicit knowledge – the first set of structural rules.

It extends propositional logic by replacing propositional letters with a more complex notion of proposition involving predicates and quantifiers. These potential applications demonstrate the ongoing relevance and potential of Symbolic AI in the future of AI research and development. A similar problem, called the Qualification Problem, occurs in trying to enumerate the preconditions for an action to succeed.

RAAPID leverages Neuro-Symbolic AI to revolutionize clinical decision-making and risk adjustment processes. By seamlessly integrating a Clinical Knowledge Graph with Neuro-Symbolic AI capabilities, RAAPID ensures a comprehensive understanding of intricate clinical data, facilitating precise risk assessment and decision support. Our solution, meticulously crafted from extensive clinical records, embodies a groundbreaking advancement in healthcare analytics. However, traditional symbolic AI struggles when presented with uncertain or ambiguous information.

For example, we can use the symbol M to represent a movie and P to describe people. Relations allow us to formalize how the different symbols in our knowledge base interact and connect. The parser uses these symbolic rules to break down a sentence into its

constituent parts and create a parse tree representing its syntactic

structure. The historical context of Symbolic AI reveals a rich tapestry of ideas,

achievements, and challenges. From its early beginnings at the Dartmouth

Conference to its current state, Symbolic AI has played a crucial role

in shaping our understanding of intelligence and pushing the boundaries

of what machines can accomplish. As the field continues to evolve, the

lessons learned from its history will undoubtedly inform and guide

future research and development in AI.

A. Symbolic AI, also known as classical or rule-based AI, is an approach that represents knowledge using explicit symbols and rules. It emphasizes logical reasoning, manipulating symbols, and making inferences based on predefined rules. Symbolic AI is typically rule-driven and uses symbolic representations for problem-solving.Neural AI, on the other hand, refers to artificial intelligence models based on neural networks, which are computational models inspired by the human brain. Neural AI focuses on learning patterns from data and making predictions or decisions based on the learned knowledge. It excels at tasks such as image and speech recognition, natural language processing, and sequential data analysis. Neural AI is more data-driven and relies on statistical learning rather than explicit rules.

This section provides an overview of techniques and contributions in an overall context leading to many other, more detailed articles in Wikipedia. Sections on Machine Learning and Uncertain Reasoning are covered earlier in the history section. Time periods and titles are drawn from Henry Kautz’s 2020 AAAI Robert S. Engelmore Memorial Lecture[17] and the longer Wikipedia article on the History of AI, with dates and titles differing slightly for increased clarity.

Summarizing, neuro-symbolic artificial intelligence is an emerging subfield of AI that promises to favorably combine knowledge representation and deep learning in order to improve deep learning and to explain outputs of deep-learning-based systems. Neuro-symbolic approaches carry the promise that they will be useful for addressing complex AI problems that cannot be solved by purely symbolic or neural means. We have laid out some of the most important currently investigated research directions, and provided literature pointers suitable as entry points to an in-depth study of the current state of the art. Neuro-symbolic AI enhances the precision, explainability, and accuracy of artificial intelligence systems by combining neural networks and rules-based symbolic processing approaches. The neural element includes statistical deep learning approaches that are employed in a variety of machine learning applications.

Finally, we can define our world by its domain, composed of the individual symbols and relations we want to model. Domain2– The structured reasoning and interpretive capabilities characteristic of symbolic AI. The weakness of symbolic reasoning is that it does not tolerate ambiguity as seen in the real world. One false assumption can make everything true, effectively rendering the system meaningless. “Neuro-symbolic [AI] models will allow us to build AI systems that capture compositionality, causality, and complex correlations,” Lake said.

Expert

systems, which aimed to emulate the decision-making abilities of human

experts in specific domains, emerged as one of the most successful

applications of Symbolic AI during this period. Furthermore, the paper explores the applications of Symbolic AI in

various domains, such as expert systems, natural language processing,

and automated reasoning. We discuss real-world use cases and case

studies to demonstrate the practical impact of Symbolic AI. Neuro-symbolic models have showcased their ability to surpass current deep learning models in areas like image and video comprehension. Additionally, they’ve exhibited remarkable accuracy while utilizing notably less training data than conventional models.

What is Symbolic AI?

It demonstrated the potential of using symbolic logic and

heuristic search to solve complex problems. So, to verify Elvis Presley’s birthplace, specifically whether he was born in England refer the above  diagram , the system initially converts the question into a generic logical form by translating it into an Abstract Meaning Representation (AMR). Each AMR encapsulates the meaning of the question using terminology independent of the knowledge graph, a crucial feature enabling the technology’s application across various tasks and knowledge bases. Yet another instance of symbolic AI manifests in rule-based systems, such as those that solve queries.

Many leading scientists believe that symbolic reasoning will continue to remain a very important component of artificial intelligence. Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. As we look to the future, it’s clear that Neuro-Symbolic AI has the potential to significantly advance the field of AI. By bridging the gap between neural networks and symbolic AI, this approach could unlock new levels of capability and adaptability in AI systems. Moreover, Symbolic AI allows the intelligent assistant to make decisions regarding the speech duration and other features, such as intonation when reading the feedback to the user.

In machine learning, the algorithm learns rules as it establishes correlations between inputs and outputs. In symbolic reasoning, the rules are created through human intervention and then hard-coded into a static program. But the benefits of deep learning and neural networks are not without tradeoffs. Deep learning has several deep challenges and disadvantages in comparison to symbolic AI. Notably, deep learning algorithms are opaque, and figuring out how they work perplexes even their creators. Deep learning and neural networks excel at exactly the tasks that symbolic AI struggles with.

Symbols in Symbolic AI are more than just labels; they carry meaning and

enable the system to reason about the entities they represent. For

example, in a medical diagnosis expert system, symbols like “fever,”

“cough,” and “headache” represent specific symptoms, while symbols

like “influenza” and “pneumonia” represent diseases. These symbols

form the building blocks for expressing knowledge and performing logical

inference. Despite these challenges, Symbolic AI has continued to evolve and find

applications in various domains.

Symbolic AI, also known as rule-based AI or classical AI, uses a symbolic representation of knowledge, such as logic or ontologies, to perform reasoning tasks. Symbolic AI relies on explicit rules and algorithms to make decisions and solve problems, and humans can easily understand and explain their reasoning. It uses deep learning neural network topologies and blends them with symbolic reasoning techniques, making it a fancier kind of AI Models than its traditional version. We have been utilizing neural networks, for instance, to determine an item’s type of shape or color.

The integration of symbolic and

sub-symbolic approaches, as well as the emergence of neuro-symbolic AI,

has opened up new possibilities for leveraging the strengths of both

paradigms. Following the Dartmouth Conference, several influential projects and

programs were developed that shaped the course of Symbolic AI. One such

project was the Logic Theorist, developed by Allen Newell, Herbert A.

Simon, and Cliff Shaw in 1956. The Logic Theorist was one of the first

programs designed to perform automated reasoning and prove mathematical

theorems.

“When you have neurosymbolic systems, you have these symbolic choke points,” says Cox. These choke points are places in the flow of information where the AI resorts to symbols that humans can understand, making the AI interpretable and explainable, while providing ways of creating complexity through composition. The researchers broke the problem into smaller chunks familiar from symbolic AI. In essence, they had to first look at an image and characterize the 3-D shapes and their properties, and generate a knowledge base. Then they had to turn an English-language question into a symbolic program that could operate on the knowledge base and produce an answer. It contained 100,000 computer-generated images of simple 3-D shapes (spheres, cubes, cylinders and so on).

Its overarching objective is to establish a synergistic connection between symbolic reasoning and statistical learning, harnessing the strengths of each approach. By adopting this hybrid methodology, machines can perform symbolic reasoning alongside exploiting the robust pattern recognition capabilities inherent in neural networks. The work in AI started by projects like the General Problem Solver and other rule-based reasoning systems like Logic Theorist became the foundation for almost 40 years of research. Symbolic AI (or Classical AI) is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. facts and rules). If such an approach is to be successful in producing human-like intelligence then it is necessary to translate often implicit or procedural knowledge possessed by humans into an explicit form using symbols and rules for their manipulation. Artificial systems mimicking human expertise such as Expert Systems are emerging in a variety of fields that constitute narrow but deep knowledge domains.

We investigate an unconventional direction of research that aims at converting neural networks, a class of distributed, connectionist, sub-symbolic models into a symbolic level with the ultimate goal of achieving AI interpretability and safety. It achieves a form of “symbolic disentanglement”, offering one solution to the important problem of disentangled representations and invariance. Basic computations of the network include predicting high-level objects and their properties from low-level objects and binding/aggregating relevant objects together. These computations operate at a more fundamental level than convolutions, capturing convolution as a special case while being significantly more general than it.

Despite these limitations, symbolic AI has been successful in a number of domains, such as expert systems, natural language processing, and computer vision. In ML, knowledge is often represented in a high-dimensional space, which requires a lot of computing power to process and manipulate. In contrast, symbolic AI uses more efficient algorithms and techniques, such as rule-based systems and logic programming, which require less computing power. First, a neural network learns to break up the video clip into a frame-by-frame representation of the objects. This is fed to another neural network, which learns to analyze the movements of these objects and how they interact with each other and can predict the motion of objects and collisions, if any. The other two modules process the question and apply it to the generated knowledge base.

Below is a quick overview of approaches to knowledge representation and automated reasoning. Expert systems can operate in either a forward chaining – from evidence to conclusions – or backward chaining – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as Soar can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies. While we cannot give the whole neuro-symbolic AI field due recognition in a brief overview, we have attempted to identify the major current research directions based on our survey of recent literature, and we present them below.

The Perceptron algorithm in 1958 could recognize simple patterns on the neural network side. However, neural networks fell out of favor in 1969 after AI pioneers Marvin Minsky and Seymour Papert published a paper criticizing their ability to learn and solve complex problems. Psychologist Daniel Kahneman suggested that neural networks and symbolic approaches correspond to System 1 and System 2 modes of thinking and reasoning. System 1 thinking, as exemplified in neural AI, is better suited for making quick judgments, such as identifying a cat in an image. System 2 analysis, exemplified in symbolic AI, involves slower reasoning processes, such as reasoning about what a cat might be doing and how it relates to other things in the scene. So to summarize, one of the main differences between machine learning and traditional symbolic reasoning is how the learning happens.

Because machine learning algorithms can be retrained on new data, and will revise their parameters based on that new data, they are better at encoding tentative knowledge that can be retracted later if necessary. The two biggest flaws of deep learning are its lack of model interpretability (i.e. why did my model make that prediction?) and the amount of data that deep neural networks require in order to learn. Symbolic AI algorithms are used in a variety of AI applications, including knowledge representation, planning, and natural language processing.

Symbolic AI assumes that the key to making machines intelligent is providing them with the rules and logic that make up our knowledge of the world. Once symbols are defined, they are organized into structured

representations that capture the relationships and properties of the

entities they represent. Common techniques for symbol structuring

include semantic networks, frames, and ontologies.

Implicit knowledge refers to information gained unintentionally and usually without being aware. Therefore, implicit knowledge tends to be more ambiguous to explain or formalize. Examples of implicit human knowledge include learning to ride a bike or to swim.

But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. Symbolic AI, a branch of artificial intelligence, excels at handling complex problems that are challenging for conventional AI methods. It operates by manipulating symbols to derive solutions, which can be more sophisticated and interpretable. This interpretability is particularly advantageous for tasks requiring human-like reasoning, such as planning and decision-making, where understanding the AI’s thought process is crucial.

Its applications range from expert systems and natural language processing to automated planning and knowledge representation. While symbolic AI has its limitations, ongoing research and hybrid approaches are paving the way for more advanced and intelligent systems. As the field progresses, we can expect to see further innovations and applications of symbolic AI in various domains, contributing to the development of smarter and more capable AI systems. Not everyone agrees that neurosymbolic AI is the best way to more powerful artificial intelligence. Serre, of Brown, thinks this hybrid approach will be hard pressed to come close to the sophistication of abstract human reasoning.

Some companies have chosen to ‘boost’ symbolic AI by combining it with other kinds of artificial intelligence. Inbenta works in the initially-symbolic field of Natural Language Processing, but adds a layer of ML to increase the efficiency of this processing. The ML layer processes hundreds of thousands of lexical functions, featured in dictionaries, that allow the system to better ‘understand’ relationships between words. The tremendous success of deep learning systems is forcing researchers to examine the theoretical principles that underlie how deep nets learn.

For example, ILP was previously used to aid in an automated recruitment task by evaluating candidates’ Curriculum Vitae (CV). Due to its expressive nature, Symbolic AI allowed the developers to trace back the result to ensure that the inferencing model was not influenced by sex, race, or other discriminatory properties. Thomas Hobbes, a British philosopher, famously said that thinking is nothing more than symbol manipulation, and our ability to reason is essentially our mind computing that symbol manipulation.

An infinite number of pathological conditions can be imagined, e.g., a banana in a tailpipe could prevent a car from operating correctly. Qualitative simulation, such as Benjamin Kuipers’s QSIM,[88] approximates human reasoning about naive physics, such as what happens when we heat a liquid in a pot on the stove. We expect it to heat and possibly boil over, even though we may not know its temperature, its boiling point, or other details, such as atmospheric pressure. Cognitive architectures such as ACT-R may have additional capabilities, such as the ability to compile frequently used knowledge into higher-level chunks.

Formal

logic allows for the precise specification of rules and relationships,

enabling Symbolic AI systems to perform deductive reasoning and draw

valid conclusions. Another significant development in the early days of Symbolic AI was the

General Problem Solver (GPS) program, created by Newell and Simon in

1957. GPS was designed as a universal problem-solving engine that could

tackle Chat GPT a wide range of problems by breaking them down into smaller

subproblems and applying general problem-solving strategies. Although

GPS had its limitations, it demonstrated the potential of using symbolic

representations and heuristic search to solve complex problems. Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together.

Other non-monotonic logics provided truth maintenance systems that revised beliefs leading to contradictions. Limitations were discovered in using simple first-order logic to reason about dynamic domains. Problems were discovered both with regards to enumerating the preconditions for an action to succeed and in providing axioms for what did not change after an action was performed. Similarly, Allen’s temporal interval algebra is a simplification of reasoning about time and Region Connection Calculus is a simplification of reasoning about spatial relationships. The logic clauses that describe programs are directly interpreted to run the programs specified. No explicit series of actions is required, as is the case with imperative programming languages.

These rules specify how symbols can be

combined, transformed, or inferred based on the relationships and

properties encoded in the structured representations. Neuro-symbolic AI emerges from continuous efforts to emulate human intelligence in machines. Conventional AI models usually align with either neural networks, adept at discerning patterns from data, or symbolic AI, reliant on predefined knowledge for decision-making. Hadayat Seddiqi, director of machine learning at InCloudCounsel, a legal technology company, said the time is right for developing a neuro-symbolic learning approach. “Deep learning in its present state cannot learn logical rules, since its strength comes from analyzing correlations in the data,” he said.

The first objective of this chapter is to discuss the concept of Symbolic AI and provide a brief overview of its features. Symbolic AI is heavily influenced by human interaction and knowledge representation. We will then examine the key features of Symbolic AI, which allowed it to dominate the field during its time. After that, we will cover various paradigms of Symbolic AI and discuss some real-life use cases based on Symbolic AI. We will finally discuss the main challenges when developing Symbolic AI systems and understand their significant pitfalls.

Although these advancements represent notable strides in emulating human reasoning abilities, existing versions of Neuro-symbolic AI systems remain insufficient for tackling complex and abstract mathematical problems. Nevertheless, the outlook for AI with Neuro-Symbolic AI appears promising as researchers persist in their exploration and innovation within this domain. The potential for Neuro-Symbolic AI to enhance AI capabilities and adaptability is vast, and further breakthroughs are anticipated in the foreseeable future.

As such, this chapter also examined the idea of intelligence and how one might represent knowledge through explicit symbols to enable intelligent systems. Symbolic AI is more concerned with representing the problem in symbols and logical rules (our knowledge base) and then searching for potential solutions using logic. In Symbolic AI, we can think of logic as our problem-solving technique and symbols and rules as the means to represent our problem, the input to our problem-solving method. The natural question that arises now would be how one can get to logical computation from symbolism.

• We are already integrating data from the KG inside reporting platforms like Microsoft Power BI and Google Looker Studio.
• Another example of symbolic AI can be seen in rule-based system like a chess game.
• Modern generative search engines are becoming a reality as Google is rolling out a richer user experience that supercharges search by introducing a dialogic experience providing additional context and sophisticated semantic personalization.
• The inclusion of LLMs allows for the processing and understanding of natural language, turning unstructured text into structured knowledge that can be added to the graph and reasoned about.

Through Symbolic AI, we can translate some form of implicit human knowledge into a more formalized and declarative form based on rules and logic. AI research firms view Neuro-symbolic AI as a route towards attaining artificial general intelligence. By enhancing and merging the strengths of statistical AI, such as machine learning, with human-like symbolic knowledge capabilities and reasoning, they aim to spark a revolution in the field of AI. Upon delving into human cognition and reasoning, it’s evident that symbols play a pivotal role in concept understanding and decision-making, thereby enhancing intelligence. Researchers endeavored to emulate this symbol-centric aspect in robots to align their operations closely with human capabilities.

However, there is a growing interest in neuro-symbolic AI, which aims to combine the strengths of symbolic AI and neural networks to create systems that can both reason with symbols and learn from data. We might teach the program rules that might eventually become irrelevant or even invalid, especially in highly volatile applications such as human behavior, where past behavior is not necessarily guaranteed. Even if the AI can learn these new logical rules, the new rules would sit on top of the older (potentially invalid) rules due to their monotonic nature. As a result, most Symbolic AI paradigms would require completely remodeling their knowledge base to eliminate outdated knowledge. For this reason, Symbolic AI systems are limited in updating their knowledge and have trouble making sense of unstructured data. As AI evolves, the integration of Symbolic AI with other paradigms, like

machine learning and neural networks, holds immense promise.

In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications. The deep learning hope—seemingly grounded not so much in science, but in a sort of historical grudge—is that intelligent behavior will emerge purely from the confluence of massive data and deep learning. Since the program has logical rules, we can easily trace the conclusion to the root node, precisely understanding the AI’s path. For this reason, Symbolic AI has also been explored multiple times in the exciting field of Explainable Artificial Intelligence (XAI). A paradigm of Symbolic AI, Inductive Logic Programming (ILP), is commonly used to build and generate declarative explanations of a model. This process is also widely used to discover and eliminate physical bias in a machine learning model.

In short, we extract the different symbols and declare their relationships. With our knowledge base ready, determining whether the object is an orange becomes as simple as comparing it with our existing knowledge of an orange. An orange should have a diameter of around 2.5 inches and fit into the palm of our hands. We learn these rules and symbolic representations through our sensory capabilities and use them to understand and formalize the world around us. This paper provides a comprehensive introduction to Symbolic AI,

covering its theoretical foundations, key methodologies, and

applications.