Symbolic artificial intelligence Wikipedia

It uses deep learning neural network topologies and blends them with symbolic reasoning techniques, making it a fancier kind of AI than its traditional version. We have been utilizing neural networks, for instance, to determine an item’s type of shape or color. However, it can be advanced further by using symbolic reasoning to reveal more fascinating aspects of the item, such as its area, volume, etc. This, in turn, enables AI to be trained using multiple techniques, including semantic inferencing and both supervised and unsupervised learning, which will ultimately create AI systems that can reason, learn, and engage in natural language question-and-answer interactions with humans. Already, this technology is finding its way into such complex tasks as fraud analysis, supply chain optimization, and sociological research. Like so many recent topic shifts in AI, it appears that the increased attention to NeSy AI is mostly due to the rise of deep learning, which has shown tremendous success even in areas which previously were mostly in the domain of symbolic approaches, such as Chess or Go game engines.

• This, in his words, is the “standard operating procedure” whenever inputs and outputs are symbolic.
• We began to add in their knowledge, inventing knowledge engineering as we were going along.
• An agent whose understanding of “dog” comes only from a limited set of logical sentences such as “Dog(x) ⇒ Mammal(x)” is at a disadvantage compared to an agent that has watched dogs run, has played fetch with them, and has been licked by one.
• Below, we identify what we believe are the main general research directions the field is currently pursuing.
• The workshop will include over 15 IBM talks, and 5 panels in various areas of theory and the application of neuro-symbolic AI.
• It aims for revolution rather than development and building new paradigms instead of a superficial synthesis of existing ones.

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. Literature references within this text are limited to general overview articles, but a supplementary online document referenced at the end contains references to concrete examples from the recent literature. Examples for historic overview works that provide a perspective on the field, including cognitive science aspects, prior to the recent acceleration in activity, are Refs [1,3]. First, symbolic AI algorithms are designed to deal with problems that require human-like reasoning. This means that they are able to understand and manipulate symbols in ways that other AI algorithms cannot. Second, symbolic AI algorithms are often much slower than other AI algorithms.

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Each method executes a series of rule-based instructions that might read and change the properties of the current and other objects. The Bosch code of ethics for AI emphasizes the development of safe, robust, and explainable AI products. By providing explicit symbolic representation, neuro-symbolic methods enable explainability of often opaque neural sub-symbolic models, which is well aligned with these esteemed values.

• When considering how people think and reason, it becomes clear that symbols are a crucial component of communication, which contributes to their intelligence.
• The topic has garnered much interest over the last several years, including at Bosch where researchers across the globe are focusing on these methods.
• Monotonic basically means one direction; i.e. when one thing goes up, another thing goes up.
• From the earliest writings of India and Greece, this has been a central problem in philosophy.
• Cory is a lead research scientist at Bosch Research and Technology Center with a focus on applying knowledge representation and semantic technology to enable autonomous driving.
• The general theme is also of importance in the context of the field of Cognitive Science [Besold17survey].

Arguably, this is also happening at the inflection point in time when we are slowly beginning to explore and understand the inherent limitations of pure deep learning approaches. The use of additional background knowledge is a natural path to attempt to further improve deep learning systems, and much of this line of work falls into the NeSy AI theme. Knowledge representation algorithms are used to store and retrieve information from a knowledge base. Knowledge representation is used in a variety of applications, including expert systems and decision support systems. An LNN consists of a neural network trained to perform symbolic reasoning tasks, such as logical inference, theorem proving, and planning, using a combination of differentiable logic gates and differentiable inference rules.

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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. OOP languages allow you to define classes, specify their properties, and organize them in hierarchies. You can create instances of these classes (called objects) and manipulate their properties. Class instances can also perform actions, also known as functions, methods, or procedures.

One difficult problem encountered by symbolic AI pioneers came to be known as the common sense knowledge problem. In addition, areas that rely on procedural or implicit knowledge such as sensory/motor processes, are much more difficult to handle within the Symbolic AI framework. In these fields, Symbolic AI has had limited success and by and large has left the field to neural network architectures (discussed in a later chapter) which are more suitable for such tasks. In sections to follow we will elaborate on important sub-areas of Symbolic AI as well as difficulties encountered by this approach. 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.

What Is Neuro-Symbolic Artificial Intelligence?

The development of neuro-symbolic AI is still in its early stages, and much work must be done to realize its potential fully. However, the progress made so far and the promising results of current research make it clear that neuro-symbolic AI has the potential to play a major role in shaping the future of AI. Symbolic AI and Neural Networks are distinct approaches to artificial intelligence, each with its strengths and weaknesses. Thus contrary to pre-existing cartesian philosophy he maintained that we are born without innate ideas and knowledge is instead determined only by experience derived by a sensed perception.

• Using symbolic knowledge bases and expressive metadata to improve deep learning systems.
• 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.
• This in general would include things like term rewriting, graph algorithms, and natural language question answering.
• This lead towards the connectionist paradigm of AI, also called non-symbolic AI which gave rise to learning and neural network-based approaches to solve AI.
• A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.[56]
  The simplest approach for an expert system knowledge base is simply a collection or network of production rules.
• The following resources provide a more in-depth understanding of neuro-symbolic AI and its application for use cases of interest to Bosch.

In addition, several artificial intelligence companies, such as Teknowledge and Inference Corporation, were selling expert system shells, training, and consulting to corporations. On the extraction vs. representation dimension, we notice that explicit work on extracting symbolic information from trained neural networks is less of an emphasis. This makes sense in the light of Kautz’ categories above, where the first three categories would usually not involve an extraction aspect, and the fourth also may not. In particular, in the diagram note that I/O is situated on the symbolic side only, while training is situated only on the neural side, whereas reasoning can happen in either part. Logical Neural Networks (LNNs) are neural networks that incorporate symbolic reasoning in their architecture.

Call for book chapter proposals for A Compendium of Neuro-Symbolic Artificial Intelligence

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. The automated theorem provers discussed below can prove theorems in first-order logic. Horn clause logic is more restricted than first-order logic and is used in logic programming languages such as Prolog.

When deep learning reemerged in 2012, it was with a kind of take-no-prisoners attitude that has characterized most of the last decade. He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes. 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. Constraint solvers perform a more limited kind of inference than first-order logic.

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You create a rule-based program that takes new images as inputs, compares the pixels to the original cat image, and responds by saying whether your cat is in those images. Symbolic artificial intelligence showed early progress at the dawn of AI and computing. You can easily visualize the logic of rule-based programs, communicate them, and troubleshoot them. 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 key AI programming language in the US during the last symbolic AI boom period was LISP.

In particular, we will highlight two applications of the technology for autonomous driving and traffic monitoring. Neuro-symbolic methods have the potential of benefiting from the advantages of both deep neural models (i.e., performance) and symbolic methods metadialog.com (i.e., transparency and mutability) – see also [9]. Such methods would focus on the development of methods that incorporate declarative knowledge into deep neural methods, including the use of knowledge representation logics, such as natural logic.

Combining Deep Neural Nets and Symbolic Reasoning

Researchers investigated a more data-driven strategy to address these problems, which gave rise to neural networks’ appeal. While symbolic AI requires constant information input, neural networks could train on their own given a large enough dataset. Although everything was functioning perfectly, as was already noted, a better system is required due to the difficulty in interpreting the model and the amount of data required to continue learning. Although deep learning has historical roots going back decades, neither the term “deep learning” nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton’s now classic (2012) deep network model of Imagenet.

Despite these limitations, symbolic AI has been successful in a number of domains, such as expert systems, natural language processing, and computer vision. In summary, symbolic AI excels at human-understandable reasoning, while Neural Networks are better suited for handling large and complex data sets. Integrating both approaches, known as neuro-symbolic AI, can provide the best of both worlds, combining the strengths of symbolic AI and Neural Networks to form a hybrid architecture capable of performing a wider range of tasks.

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In this paper, we propose an end-to-end reinforcement learning architecture comprising a neural back end and a symbolic front end with the potential to overcome each of these shortcomings. As proof-of-concept, we present a preliminary implementation of the architecture and apply it to several variants of a simple video game. We show that the resulting system – though just a prototype – learns effectively, and, by acquiring a set of symbolic rules that are easily comprehensible to humans, dramatically symbolic artificial intelligence outperforms a conventional, fully neural DRL system on a stochastic variant of the game. One of the fundamental differences between neural and symbolic AI approaches, that is relevant for our discussion, is that of representation of information within an AI system. For symbolic systems, representation is explicit and in such terms that are in principle understandable by a human. E.g., a rule such as square(x)→rectangle(x) is readily understood and manipulated by symbolic means.

Insofar as computers suffered from the same chokepoints, their builders relied on all-too-human hacks like symbols to sidestep the limits to processing, storage and I/O. As computational capacities grow, the way we digitize and process our analog reality can also expand, until we are juggling billion-parameter tensors instead of seven-character strings. Read more about our work in neuro-symbolic AI from the MIT-IBM Watson AI Lab.

In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model. The output of a classifier (let’s say we’re dealing with an image recognition algorithm that tells us whether we’re looking at a pedestrian, a stop sign, a traffic lane line or a moving semi-truck), can trigger business logic that reacts to each classification. 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. Latent semantic analysis (LSA) and explicit semantic analysis also provided vector representations of documents.