The Rise and Fall of Symbolic AI Philosophical presuppositions of AI by Ranjeet Singh

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. Symbolic AI and Expert Systems form the cornerstone of early AI research, shaping the development of artificial intelligence over the decades. These early concepts laid the foundation for logical reasoning and problem-solving, and while they faced limitations, they provided valuable insights that contributed to the evolution of modern AI technologies. Today, AI has moved beyond Symbolic AI, incorporating machine learning and deep learning techniques that can handle vast amounts of data and solve complex problems with unprecedented accuracy.

These rules were encoded in the form of “if-then” statements, representing the relationships between various symbols and the conclusions that could be drawn from them. By manipulating these symbols and rules, machines attempted to emulate human reasoning. The team solved the first problem by using a number of convolutional neural networks, a type of deep net that’s optimized for image recognition. In this case, each network is trained to examine an image and identify an object and its properties such as color, shape and type (metallic or rubber).

• Researchers aimed to create programs that could reason logically and manipulate symbols to solve complex problems.
• Nevertheless, symbolic AI has proven effective in various fields, including expert systems, natural language processing, and computer vision, showcasing its utility despite the aforementioned constraints.
• The symbolic representations are manipulated using rules to make inferences, solve problems, and understand complex concepts.
• One of their projects involves technology that could be used for self-driving cars.

Neural networks are almost as old as symbolic AI, but they were largely dismissed because they were inefficient and required compute resources that weren’t available at the time. In the past decade, thanks to the large availability of data and processing power, deep learning has gained popularity and has pushed past symbolic AI systems. In a nutshell, symbolic AI involves the explicit what is symbolic ai embedding of human knowledge and behavior rules into computer programs. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions.

“This grammar can generate all the questions people ask and also infinitely many other questions,” says Lake. “You could think of it as the space of possible questions that people can ask.” For a given state of the game board, the symbolic AI has to search this enormous space of possible questions to find a good question, which makes it extremely slow. Once trained, the deep nets far outperform the purely symbolic AI at generating questions. A hybrid approach, known as neurosymbolic AI, combines features of the two main AI strategies.

The key AI programming language in the US during the last symbolic AI boom period was LISP. LISP is the second oldest programming language after FORTRAN and was created in 1958 by John McCarthy. LISP provided the first read-eval-print loop to support rapid program development. Program tracing, stepping, and breakpoints were also provided, along with the ability to change values or functions and continue from breakpoints or errors.

In sections to follow we will elaborate on important sub-areas of Symbolic AI as well as difficulties encountered by this approach. 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. Similar to the problems in handling dynamic domains, common-sense reasoning is also difficult to capture in formal reasoning.

Statistical Mechanics of Deep Learning

The method involves training these weak learners sequentially, with each one focusing on the errors of the previous ones in an effort to correct them. Symbolic AI, a subfield of AI focused on symbol manipulation, has its limitations. Its primary challenge is handling complex real-world scenarios due to the finite number of symbols and their interrelations it can process. For instance, while it can solve straightforward mathematical problems, it struggles with more intricate issues like predicting stock market trends. In contrast, a multi-agent system consists of multiple agents that communicate amongst themselves with some inter-agent communication language such as Knowledge Query and Manipulation Language (KQML). Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost.

This is especially true of a branch of AI known as deep learning or deep neural networks, the technology powering the AI that defeated the world’s Go champion Lee Sedol in 2016. Such deep nets can struggle to figure out simple abstract relations between objects and reason about them unless they study tens or even hundreds of thousands of examples. And unlike symbolic AI, neural networks have no notion of symbols and hierarchical representation of knowledge.

At its core, Symbolic AI employs logical rules and symbolic representations to model human-like problem-solving and decision-making processes. Researchers aimed to create programs that could reason logically and manipulate symbols to solve complex problems. The second module uses something called a recurrent neural network, another type of deep net designed to uncover patterns in inputs that come sequentially. (Speech is sequential information, for example, and speech recognition programs like Apple’s Siri use a recurrent network.) In this case, the network takes a question and transforms it into a query in the form of a symbolic program.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Symbolic AI was the dominant paradigm from the mid-1950s until the mid-1990s, and it is characterized by the explicit embedding of human knowledge and behavior rules into computer programs. The symbolic representations are manipulated using rules to make inferences, solve problems, and understand complex concepts. For the first method, called supervised learning, the team showed the deep nets numerous examples of board positions and the corresponding “good” questions (collected from human players).

Symbolic AI, a branch of artificial intelligence, focuses on the manipulation of symbols to emulate human-like reasoning for tasks such as planning, natural language processing, and knowledge representation. Unlike other AI methods, symbolic AI excels in understanding and manipulating symbols, which is essential for tasks that require complex reasoning. 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. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.

Since some of the weaknesses of neural nets are the strengths of symbolic AI and vice versa, neurosymbolic AI would seem to offer a powerful new way forward. Roughly speaking, the hybrid uses deep nets to replace humans in building the knowledge base and propositions that symbolic AI relies on. It harnesses the power of deep nets to learn about the world from raw data and then uses the symbolic components to reason about it.

Understanding these foundational ideas is crucial in comprehending the advancements that have led to the powerful AI technologies we have today. Knowable Magazine is from Annual Reviews,

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benefit of society. So not only has symbolic AI the most mature and frugal, it’s also the most transparent, and therefore accountable.

Somehow, the ducklings pick up and imprint on the idea of similarity, in this case the color of the objects. Like Inbenta’s, “our technology is frugal in energy and data, it learns autonomously, and can explain its decisions”, affirms AnotherBrain on its website. And given the startup’s founder, Bruno Maisonnier, previously founded Aldebaran Robotics (creators of the NAO and Pepper robots), AnotherBrain is unlikely to be a flash in the pan.

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. In contrast to the US, in Europe the key AI programming language during that same period was Prolog. Prolog provided a built-in store of facts and clauses that could be queried by a read-eval-print loop. The store could act as a knowledge base and the clauses could act as rules or a restricted form of logic.

Take, for example, a neural network tasked with telling apart images of cats from those of dogs. The image — or, more precisely, the values of each pixel in the image — are fed to the first layer of nodes, and the final layer of nodes produces as an output the label “cat” or “dog.” The network has to be trained using pre-labeled images of cats and 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. Also, some tasks can’t be translated to direct rules, including speech recognition and natural language processing. Some companies have chosen to ‘boost’ symbolic AI by combining it with other kinds of artificial intelligence.

In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. During the first AI summer, many people thought that machine intelligence could be achieved in just a few years. By the mid-1960s neither useful natural language translation systems nor autonomous tanks had been created, and a dramatic backlash set in. In the context of Neuro-Symbolic AI, AllegroGraph’s W3C standards based graph capabilities allow it to define relationships between entities in a way that can be logically reasoned about. The geospatial and temporal features enable the AI to understand and reason about the physical world and the passage of time, which are critical for real-world applications.

The Frame Problem: knowledge representation challenges for first-order logic

Marvin Minsky first proposed frames as a way of interpreting common visual situations, such as an office, and Roger Schank extended this idea to scripts for common routines, such as dining out. Cyc has attempted to capture useful common-sense knowledge and has «micro-theories» to handle particular kinds of domain-specific reasoning. Alain Colmerauer and Philippe Roussel are credited as the inventors of Prolog. Prolog is a form of logic programming, which was invented by Robert Kowalski. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods. For more detail see the section on the origins of Prolog in the PLANNER article.

The interplay between these two components is where Neuro-Symbolic AI shines. It can, for example, use neural networks to interpret a complex image and then apply symbolic reasoning to answer questions about the image’s content or to infer the relationships between objects within it. The researchers trained this neurosymbolic hybrid on a subset of question-answer pairs from the CLEVR dataset, so that the deep nets learned how to recognize the objects and their properties from the images and how to process the questions properly.

Neuro-Symbolic AI aims to create models that can understand and manipulate symbols, which represent entities, relationships, and abstractions, much like the human mind. These models are adept at tasks that require deep understanding and reasoning, such as natural language processing, complex decision-making, and problemsolving. New deep learning approaches based on Transformer models have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language processing. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the meaning of the vector components is opaque. Symbolic AI, also known as “good old-fashioned AI” (GOFAI), emerged in the 1960s and 1970s as a dominant approach to early AI research.

The output of the recurrent network is also used to decide on which convolutional networks are tasked to look over the image and in what order. This entire process is akin to generating a knowledge base on demand, and having an inference engine run the query on the knowledge base to reason and answer the question. Neurosymbolic AI is also demonstrating the ability to ask questions, an important aspect of human learning. Crucially, these hybrids need far less training data then standard deep nets and use logic that’s easier to understand, making it possible for humans to track how the AI makes its decisions.

Symbolic AI, also known as Good Old-Fashioned Artificial Intelligence (GOFAI), is a paradigm in artificial intelligence research that relies on high-level symbolic representations of problems, logic, and search to solve complex tasks. Symbolic AI, a branch of artificial intelligence, specializes in symbol manipulation to perform tasks such as natural language processing (NLP), knowledge representation, and planning. These algorithms enable machines to parse and understand human language, manage complex data in knowledge bases, and devise strategies to achieve specific goals. 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.

But they require a huge amount of effort by domain experts and software engineers and only work in very narrow use cases. As soon as you generalize the problem, there will be an explosion of new rules to add (remember the cat detection problem?), which will require more human labor. For other AI programming languages see this list of programming languages for artificial intelligence. Currently, Python, a multi-paradigm programming language, is the most popular programming language, partly due to its extensive package library that supports data science, natural language processing, and deep learning.

Production rules connect symbols in a relationship similar to an If-Then statement. The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, OPS5, CLIPS and their successors Jess and Drools operate in this fashion.

Such causal and counterfactual reasoning about things that are changing with time is extremely difficult for today’s deep neural networks, which mainly excel at discovering static patterns in data, Kohli says. In 2019, Kohli and colleagues at MIT, Harvard and IBM designed a more sophisticated challenge in which the AI has to answer questions based not on images but on videos. The videos feature the types of objects that appeared in the CLEVR dataset, but these objects are moving and even colliding. Armed with its knowledge base and propositions, symbolic AI employs an inference engine, which uses rules of logic to answer queries. Asked if the sphere and cube are similar, it will answer “No” (because they are not of the same size or color).

These features enable scalable Knowledge Graphs, which are essential for building Neuro-Symbolic AI applications that require complex data analysis and integration. Ducklings exposed to two similar objects at birth will later prefer other similar pairs. If exposed to two dissimilar objects instead, the ducklings later prefer pairs that differ. Ducklings easily learn the concepts of “same” and “different” — something that artificial intelligence struggles to do. A new approach to artificial intelligence combines the strengths of two leading methods, lessening the need for people to train the systems. If I tell you that I saw a cat up in a tree, your mind will quickly conjure an image.

We began to add to their knowledge, inventing knowledge of engineering as we went along. Expert Systems found success in a variety of domains, including medicine, finance, engineering, and troubleshooting. One of the most famous Expert Systems was MYCIN, developed in the early 1970s, which provided medical advice for diagnosing bacterial infections and recommending suitable antibiotics. Artificial Intelligence (AI) has undergone a remarkable evolution, but its roots can be traced back to Symbolic AI and Expert Systems, which laid the groundwork for the field. In this article, we delve into the concepts of Symbolic AI and Expert Systems, exploring their significance and contributions to early AI research.

Cell meets robot in hybrid microbots

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. Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from funding agencies, especially during the two AI winters. This simple symbolic intervention drastically reduces the amount of data needed to train the AI by excluding certain choices from the get-go. “If the agent doesn’t need to encounter a bunch of bad states, then it needs less data,” says Fulton.

They can simplify sets of spatiotemporal constraints, such as those for RCC or Temporal Algebra, along with solving other kinds of puzzle problems, such as Wordle, Sudoku, cryptarithmetic problems, and so on. Constraint logic programming can be used to solve scheduling problems, for example with constraint handling rules (CHR). The automated theorem provers discussed below can prove theorems in first-order logic.

It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code. AllegroGraph is a horizontally distributed Knowledge Graph Platform that supports multi-modal Graph (RDF), Vector, and Document (JSON, JSON-LD) storage. It is equipped with capabilities such as SPARQL, Geospatial, Temporal, Social Networking, Text Analytics, and Large Language Model (LLM) functionalities.

When a deep net is being trained to solve a problem, it’s effectively searching through a vast space of potential solutions to find the correct one. Adding a symbolic component reduces the space of solutions to search, which speeds up learning. Symbolic artificial intelligence is very convenient for settings where the rules are very clear cut,  and you can easily obtain input and transform it into symbols. In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications. Symbolic AI is still relevant and beneficial for environments with explicit rules and for tasks that require human-like reasoning, such as planning, natural language processing, and knowledge representation. It is also being explored in combination with other AI techniques to address more challenging reasoning tasks and to create more sophisticated AI systems.

Japan championed Prolog for its Fifth Generation Project, intending to build special hardware for high performance. Similarly, LISP machines were built to run LISP, but as the second AI boom turned to bust these companies could not compete with new workstations that could now run LISP or Prolog natively at comparable speeds. Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. Our chemist was Carl Djerassi, inventor of the chemical behind the birth control pill, and also one of the world’s most respected mass spectrometrists.

While Symbolic AI showed promise in certain domains, it faced significant limitations. One major challenge was the “knowledge bottleneck,” where encoding human knowledge into explicit rules proved to be an arduous and time-consuming task. As the complexity of problems increased, the sheer volume of rules required became impractical to manage. One of their projects involves technology that could be used for self-driving cars.

There are now several efforts to combine neural networks and symbolic AI. One such project is the Neuro-Symbolic Concept Learner (NSCL), a hybrid AI system developed by the MIT-IBM Watson AI Lab. NSCL uses both https://chat.openai.com/ rule-based programs and neural networks to solve visual question-answering problems. As opposed to pure neural network–based models, the hybrid AI can learn new tasks with less data and is explainable.

Then, they tested it on the remaining part of the dataset, on images and questions it hadn’t seen before. Overall, the hybrid was 98.9 percent accurate — even beating humans, who answered the same questions correctly only about 92.6 percent of the time. Symbolic AI has been used in a wide range of applications, including expert systems, natural language processing, and game playing.

The second AI summer: knowledge is power, 1978–1987

The systems depend on accurate and comprehensive knowledge; any deficiencies in this data can lead to subpar AI performance. Despite its early successes, Symbolic AI has limitations, particularly when dealing with ambiguous, uncertain knowledge, or when it requires learning from data. It is often criticized for not being able to handle the messiness of the real world effectively, as it relies on pre-defined knowledge and hand-coded rules. Symbolic AI was the dominant approach in AI research from the 1950s to the 1980s, and it underlies many traditional AI systems, such as expert systems and logic-based AI.

Neuro-Symbolic AI represents a significant step forward in the quest to build AI systems that can think and learn like humans. By integrating neural learning’s adaptability with symbolic AI’s structured reasoning, we are moving towards AI that can understand the world and explain its understanding in a way that humans can comprehend and trust. Platforms like AllegroGraph play a pivotal role in this evolution, providing the tools needed to build the complex knowledge graphs at the heart of Neuro-Symbolic AI systems. As the field continues to grow, we can expect to see increasingly sophisticated AI applications that leverage the power of both neural networks and symbolic reasoning to tackle the world’s most complex problems. 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.

This limitation makes it very hard to apply neural networks to tasks that require logic and reasoning, such as science and high-school math. Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, Chat PG gained traction, symbolic AI has fallen by the wayside. Parsing, tokenizing, spelling correction, part-of-speech tagging, noun and verb phrase chunking are all aspects of natural language processing long handled by symbolic AI, but since improved by deep learning approaches. In symbolic AI, discourse representation theory and first-order logic have been used to represent sentence meanings.

As pressure mounts on GAI companies to explain where their apps’ answers come from, symbolic AI will never have that problem. Unlike ML, which requires energy-intensive GPUs, CPUs are enough for symbolic AI’s needs. Facial recognition, for example, is impossible, as is content generation. Generative AI (GAI) has been the talk of the town since ChatGPT exploded late 2022.

We hope that by now you’re convinced that symbolic AI is a must when it comes to NLP applied to chatbots. Machine learning can be applied to lots of disciplines, and one of those is Natural Language Processing, which is used in AI-powered conversational chatbots. To think that we can simply abandon symbol-manipulation is to suspend disbelief. Cognitive architectures such as ACT-R may have additional capabilities, such as the ability to compile frequently used knowledge into higher-level chunks. 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.

It can be difficult to represent complex, ambiguous, or uncertain knowledge with symbolic AI. Furthermore, symbolic AI systems are typically hand-coded and do not learn from data, which can make them brittle and inflexible. Henry Kautz,[17] Francesca Rossi,[79] and Bart Selman[80] have also argued for a synthesis. Their arguments are based on a need to address the two kinds of thinking discussed in Daniel Kahneman’s book, Thinking, Fast and Slow. Kahneman describes human thinking as having two components, System 1 and System 2.

While the project still isn’t ready for use outside the lab, Cox envisions a future in which cars with neurosymbolic AI could learn out in the real world, with the symbolic component acting as a bulwark against bad driving. “You can check which module didn’t work properly and needs to be corrected,” says team member Pushmeet Kohli of Google DeepMind in London. For example, debuggers can inspect the knowledge base or processed question and see what the AI is doing.

In finance, it can analyze transactions within the context of evolving regulations to detect fraud and ensure compliance. This will only work as you provide an exact copy of the original image to your program. For instance, if you take a picture of your cat from a somewhat different angle, the program will fail. As such, Golem.ai applies linguistics and neurolinguistics to a given problem, rather than statistics.

And unlike symbolic-only models, NSCL doesn’t struggle to analyze the content of images. According to Wikipedia, machine learning is an application of artificial intelligence where “algorithms and statistical models are used by computer systems to perform a specific task without using explicit instructions, relying on patterns and inference instead. (…) Machine learning algorithms build a mathematical model based on sample data, known as ‘training data’, in order to make predictions or decisions without being explicitly programmed to perform the task”. Better yet, the hybrid needed only about 10 percent of the training data required by solutions based purely on deep neural networks.

Research problems include how agents reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and distributed constraint optimization. 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. 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.).

Semantic networks, conceptual graphs, frames, and logic are all approaches to modeling knowledge such as domain knowledge, problem-solving knowledge, and the semantic meaning of language. DOLCE is an example of an upper ontology that can be used for any domain while WordNet is a lexical resource that can also be viewed as an ontology. YAGO incorporates WordNet as part of its ontology, to align facts extracted from Wikipedia with WordNet synsets. The Disease Ontology is an example of a medical ontology currently being used.

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.). Equally cutting-edge, France’s AnotherBrain is a fast-growing symbolic AI startup whose vision is to perfect “Industry 4.0” by using their own image recognition technology for quality control in factories.

Nevertheless, understanding the origins of Symbolic AI and Expert Systems remains essential to appreciate the strides made in the world of AI and to inspire future innovations that will further transform our lives. 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. 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. It leverages databases of knowledge (Knowledge Graphs) and rule-based systems to perform reasoning and generate explanations for its decisions.

The knowledge base would also have a general rule that says that two objects are similar if they are of the same size or color or shape. In addition, the AI needs to know about propositions, which are statements that assert something is true or false, to tell the AI that, in some limited world, there’s a big, red cylinder, a big, blue cube and a small, red sphere. All of this is encoded as a symbolic program in a programming language a computer can understand.

Below is a quick overview of approaches to knowledge representation and automated reasoning. Early work covered both applications of formal reasoning emphasizing first-order logic, along with attempts to handle common-sense reasoning in a less formal manner. One solution is to take pictures of your cat from different angles and create new rules for your application to compare each input against all those images. Even if you take a million pictures of your cat, you still won’t account for every possible case. A change in the lighting conditions or the background of the image will change the pixel value and cause the program to fail.

But adding a small amount of white noise to the image (indiscernible to humans) causes the deep net to confidently misidentify it as a gibbon. René Descartes, a mathematician, and philosopher, regarded thoughts themselves as symbolic representations and Perception as an internal process. McCarthy’s approach to fix the frame problem was circumscription, a kind of non-monotonic logic where deductions could be made from actions that need only specify what would change while not having to explicitly specify everything that would not change.

Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents. In the latter case, vector components are interpretable as concepts named by Wikipedia articles. A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.[51]

The simplest approach for an expert system knowledge base is simply a collection or network of production rules.