Using symbolic AI for knowledge-based question answering

Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing. 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. 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.

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. Advanced Micro Devices (AMD 4.21%) and Symbotic (SYM 7.75%) have both posted explosive stock performance over the past year. Their share prices are up 130% and 180%, respectively, as of this writing, and both look poised to see long-term benefits from the rise of artificial intelligence (AI). Perhaps surprisingly, the correspondence between the neural and logical calculus has been well established throughout history, due to the discussed dominance of symbolic AI in the early days. As ‘common sense’ AI matures, it will be possible to use it for better customer support, business intelligence, medical informatics, advanced discovery, and much more.

Symbolic Artificial Intelligence

LNNs are a modification of today’s neural networks so that they become equivalent to a set of logic statements — yet they also retain the original learning capability of a neural network. Standard neurons are modified so that they precisely model operations in With real-valued logic, variables can take on values in a continuous range between 0 and 1, rather than just binary values of ‘true’ or ‘false.’real-valued logic. LNNs are able to model formal logical reasoning by applying a recursive neural computation of truth values that moves both forward and backward (whereas a standard neural network only moves forward). As a result, LNNs are capable of greater understandability, tolerance to incomplete knowledge, and full logical expressivity. Figure 1 illustrates the difference between typical neurons and logical neurons. Building on the foundations of deep learning and symbolic AI, we have developed software that can answer complex questions with minimal domain-specific training.

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. The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has symbolic ai yielded remarkable progress on individual tasks. 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. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data.

Truly, neurally, deeply

Most recently, an extension to arbitrary (irregular) graphs then became extremely popular as Graph Neural Networks (GNNs). The tremendous success of deep learning systems is forcing researchers to examine the theoretical principles that underlie how deep nets learn. Researchers are uncovering the connections between deep nets and principles in physics and mathematics. 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). On the other hand, learning from raw data is what the other parent does particularly well.

Error from approximate probabilistic inference is tolerable in many AI applications. But it is undesirable to have inference errors corrupting results in socially impactful applications of AI, such as automated decision-making, and especially in fairness analysis. The universe is written in the language of mathematics and its characters are triangles, circles, and other geometric objects. 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.

Neuro-symbolic AI aims to give machines true common sense

Other ways of handling more open-ended domains included probabilistic reasoning systems and machine learning to learn new concepts and rules. McCarthy’s Advice Taker can be viewed as an inspiration here, as it could incorporate new knowledge provided by a human in the form of assertions or rules. For example, experimental symbolic machine learning systems explored the ability to take high-level natural language advice and to interpret it into domain-specific actionable rules. 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.

The ultimate goal, though, is to create intelligent machines able to solve a wide range of problems by reusing knowledge and being able to generalize in predictable and systematic ways. Such machine intelligence would be far superior to the current machine learning algorithms, typically aimed at specific narrow domains. One promising approach towards this more general AI is in combining neural networks with symbolic AI. In our paper “Robust High-dimensional Memory-augmented Neural Networks” published in Nature Communications,1 we present a new idea linked to neuro-symbolic AI, based on vector-symbolic architectures. But neither the original, symbolic AI that dominated machine learning research until the late 1980s nor its younger cousin, deep learning, have been able to fully simulate the intelligence it’s capable of. Samuel’s Checker Program[1952] — Arthur Samuel’s goal was to explore to make a computer learn.

Symbolic AI

A deep net, modeled after the networks of neurons in our brains, is made of layers of artificial neurons, or nodes, with each layer receiving inputs from the previous layer and sending outputs to the next one. Information about the world is encoded in the strength of the connections between nodes, not as symbols that humans can understand. LNNs’ form of real-valued logic also enables representation of the strengths of relationships between logical clauses via neural weights, further improving its predictive accuracy.3 Another advantage of LNNs is that they are tolerant to incomplete knowledge. Most AI approaches make a closed-world assumption that if a statement doesn’t appear in the knowledge base, it is false. LNNs, on the other hand, maintain upper and lower bounds for each variable, allowing the more realistic open-world assumption and a robust way to accommodate incomplete knowledge.