Neuro-Symbolic Integration and Explainable Artificial Intelligence Data Semantics Lab

Called expert systems, these symbolic AI models use hardcoded knowledge and rules to tackle complicated tasks such as medical diagnosis. 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. OOP languages allow you to define classes, specify their properties, and organize them in hierarchies.

For this reason, Symbolic AI systems are limited in updating their knowledge and have trouble making sense of unstructured data. 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. René Descartes also compared our thought process to symbolic representations. Our thinking process essentially becomes a mathematical algebraic manipulation of symbols. For example, the term Symbolic AI uses a symbolic representation of a particular concept, allowing us to intuitively understand and communicate about it through the use of this symbol.

Neuro Symbolic Applications

Although Symbolic AI paradigms can learn new logical rules independently, providing an input knowledge base that comprehensively represents the problem is essential and challenging. The symbolic representations required for reasoning must be predefined and manually fed to the system. With such levels of abstraction in our physical world, some knowledge is bound to be left out of the knowledge base. The big difference is that they did away with backpropagation, which is a cornerstone of many AI processes.

Deductive reasoning is deducing new information from logically related known information. It is the form of valid reasoning, which means the argument’s conclusion must be true when the premises are true. These are just a few examples, and the potential applications of neuro-symbolic AI are constantly expanding as the field of AI continues to evolve. “There have been many attempts to extend logic to deal with this which have not been successful,” Chatterjee said. Alternatively, in complex perception problems, the set of rules needed may be too large for the AI system to handle.

Key Terminologies Used in Neuro Symbolic AI

Such reasoning is non-monotonic, precisely because the

set of accepted conclusions have become smaller when the set of premises is

expanded. Hobbes was influenced by Galileo, just as Galileo thought that geometry could represent motion, Furthermore, as per Descartes, geometry can be expressed as algebra, which is the study of mathematical symbols and the rules for manipulating these symbols. A different way to create AI was to build machines that have a mind of its own. René Descartes, a mathematician, and philosopher, regarded thoughts themselves as symbolic representations and Perception as an internal process. Neuro-symbolic AI represents the future, seamlessly merging past insights and modern techniques. It’s more than just advanced intelligence; it’s AI designed to mirror human understanding.

Advantages of multi-agent systems include the ability to divide work among the agents and to increase fault tolerance when agents are lost. Research problems include how agents reach consensus, distributed problem solving, multi-agent learning, multi-agent planning, and distributed constraint optimization. Programs were themselves data structures that other programs could operate on, allowing the easy definition of higher-level languages. 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.

Symbolic AI

We introduce the Deep Symbolic Network (DSN) model, which aims at becoming the white-box version of Deep Neural Networks (DNN). The DSN model provides a simple, universal yet powerful structure, similar to DNN, to represent any knowledge of the world, which is transparent to humans. The conjecture behind the DSN model is that any type of real world objects sharing enough common features are mapped into human brains as a symbol. Those symbols are connected by links, representing the composition, correlation, causality, or other relationships between them, forming a deep, hierarchical symbolic network structure.

The Cyc project is a multi-year attempt to encapsulate common-sense knowledge using a First Order Logic-like language, called CycL. It represents the most advanced attempt to make a knowledge base for artificial intelligence. In figure 1 is an example of relations from the concept “person.” The Cyc knowledge base has two major distinction related to sets. A thing that represents a subset of a set “generalizes” it and its relation predicate is genls. A number of researchers have been exploring the possibility of symbolic AI in law. One approach taken by some computer scientists is to represent a statute, such as an Act of Parliament, as a logic program, and convert the facts of a case into the same logic representation, and perform legal reasoning as a query in that logic language.

In a nutshell, symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. Multiple different approaches to represent knowledge and then reason with those representations have been investigated. 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.

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.). Planning is used in a variety of applications, including robotics and automated planning.

Non-monotonic Reasoning

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• For example, deep learning systems are trainable from raw data and are robust against outliers or errors in the base data, while symbolic systems are brittle with respect to outliers and data errors, and are far less trainable.
• “The surprising thing about this framework is that the neurons reason about ideas in the exact same way that philosophers have always described our reasoning process,” Blazek said.
• In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals.
• Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules.
• One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem.
• While efficient for tasks with clear rules, it often struggles in areas requiring adaptability and learning from vast data.