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Symbolic knowledge representation and reasoning and deep learning are fundamentally different approaches to artificial intelligence with complementary capabilities. The former are transparent and data-efficient, but they are sensitive to noise and cannot be applied to non-symbolic domains where the data is ambiguous. The latter can learn complex tasks from examples, are robust to noise, but are black boxes; require large amounts of --not necessarily easily obtained-- data, and are slow to learn and prone to adversarial examples. Either paradigm excels at certain types of problems where the other paradigm performs poorly. In order to develop stronger AI systems, integrated neuro-symbolic systems that combine artificial neural networks and symbolic reasoning are being sought. In this context, one of the fundamental open problems is how to perform logic-based deductive reasoning over knowledge bases by means of trainable artificial neural networks. Over the course of this dissertation, we provide a brief summary of our recent efforts to bridge the neural and symbolic divide in the context of deep deductive reasoners. More specifically, We designed a novel way of conducting neuro-symbolic through pointing to the input elements. More importantly we showed that the proposed approach is generalizable across new domain and vocabulary demonstrating symbol-invariant zero-shot reasoning capability. Furthermore, We have demonstrated that a deep learning architecture based on memory networks and pre-embedding normalization is capable of learning how to perform deductive reason over previously unseen RDF KGs with high accuracy. We are applying these models on Resource Description Framework (RDF), first-order logic, and the description logic EL+ respectively. Throughout this dissertation we will discuss strengths and limitations of these models particularly in term of accuracy, scalability, transferability, and generalizabiliy. Based on our experimental results, pointer networks perform remarkably well across multiple reasoning tasks while outperforming the previously reported state of the art by a significant margin. We observe that the Pointer Networks preserve their performance even when challenged with knowledge graphs of the domain/vocabulary it has never encountered before. To our knowledge, this work is the first attempt to reveal the impressive power of pointer networks for conducting deductive reasoning. Similarly, we show that memory networks can be trained to perform deductive RDFS reasoning with high precision and recall. The trained memory network's capabilities in fact transfer to previously unseen knowledge bases. Finally will talk about possible modifications to enhance desirable capabilities. Altogether, these research topics, resulted in a methodology for symbol-invariant neuro-symbolic reasoning.
Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be. Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of Artificial Intelligence.
This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.
Much has been achieved in AI but to realize its true potential, it is imperative that the AI system should be able to learn generalizable and actionable higher-level knowledge from lowest level percepts. Inspired by this goal, neuro-symbolic systems have been developed for the past four decades. These systems encompass the complementary strengths of fast adaptive learning of neural networks from low-level input signals and the deliberative, generalizable models of the symbolic systems. The advent of deep networks has accelerated the development of these neuro-symbolic systems. While successful, there are several open problems to be addressed in these systems, a few of which we tackle in this dissertation. These include: (i) several primitive neural network architectures have not been well studied in the symbolic context; (ii) lack of generic neuro-symbolic architectures that are do not make distributional assumptions; (iii) generalization abilities of many such systems are limited. The objective of this dissertation is to develop novel neuro-symbolic models that (i) induce symbolic reasoning capabilities to fundamental yet unexplored neural network architectures, and (ii) provide unique solutions to the generalization issues that occur during neuro-symbolic integration. More specifically, we consider one of the primitive models, Restricted Boltzmann Machines, that was originally employed for pre-training the deep neural networks and propose two unique solutions to lift them for relational model. For the first solution, we employ relational random walks to generate relational features for Boltzmann machines. We train the Boltzmann machines by passing these resulting features through a novel transformation layer. For the second solution, we employ the mechanism of functional gradient boosting to learn the structure and the parameters of the lifted Restricted Boltzmann Machines simultaneously. Next, most of the neuro-symbolic models designed till date have focused on incorporating neural capabilities in specific models, resulting in lack of a general relational neural network architecture. To overcome this, we develop a generic neuro-symbolic architecture that exploits the concept of relational parameter tying and combining rules to incorporate the first-order logic rules into the hidden layers of the proposed architecture. One of the prevalent neuro-symbolic models called knowledge graph embedding models encode the symbols as learnable vectors in Euclidean space and lose an important characteristic of generalizability to newer symbols while doing so. We propose two unique solutions to circumvent this problem by exploiting the text description of entities in addition to the knowledge graph triples in both the models. In our first model, we train both the text and knowledge graph data in generative setting, while in the second model, we posit the two data sources in adversarial setting. Our broad results across these several directions demonstrate the efficacy and efficiency of the proposed approaches on benchmarks and novel data sets. In summary, this dissertation takes one of the first steps towards realizing the grand vision of the neuro-symbolic integration by proposing novel models that allow for symbolic reasoning capabilities inside neural networks.
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.
Despite the recent remarkable advances in deep learning, we are still far from building machines with human-like general intelligence, for instance, understanding the world in a fast, structured, and generalizable way. The dominant stream in contemporary AI hopes to achieve human-level performance via purely data-driven methods, \ie, fitting deep neural networks on a massive amount of training data. However, these methods are often trapped in a dilemma of "big data, small tasks'', and are hard to interpret and generalize. In this dissertation, we seek a unified framework for general intelligence by integrating connectionism and symbolism in a neuro-symbolic system. We argue that (i) \textbf{Neural Network} is excellent at imitating human perception from raw signals, (ii) \textbf{Grammar} provides a universal approach to construct a holistic structured representation of the world, and (iii) \textbf{Symbolic Reasoning} forms a principled basis to incorporate commonsense knowledge and perform complex reasoning. Therefore, we propose a neural-symbolic framework by using grammar as the bridge to connect neural networks and symbolic reasoning. The learning of such a neural-symbolic framework mimics human's ability to learn from failures via abductive reasoning and requires very little supervision. We have developed benchmarks, algorithms, and practices, across vision and language, from synthetic environments to real-world scenarios, to realize such a unified framework. We hope such a unified framework can contribute to the long-term goal of building general artificial intelligence like humans
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
An edited collection focusing on the technology involved in enabling integration between lexical resources and semantic technologies.