1,710 research outputs found
A Semantic Framework for Neural-Symbolic Computing
Two approaches to AI, neural networks and symbolic systems, have been proven
very successful for an array of AI problems. However, neither has been able to
achieve the general reasoning ability required for human-like intelligence. It
has been argued that this is due to inherent weaknesses in each approach.
Luckily, these weaknesses appear to be complementary, with symbolic systems
being adept at the kinds of things neural networks have trouble with and
vice-versa. The field of neural-symbolic AI attempts to exploit this asymmetry
by combining neural networks and symbolic AI into integrated systems. Often
this has been done by encoding symbolic knowledge into neural networks.
Unfortunately, although many different methods for this have been proposed,
there is no common definition of an encoding to compare them. We seek to
rectify this problem by introducing a semantic framework for neural-symbolic
AI, which is then shown to be general enough to account for a large family of
neural-symbolic systems. We provide a number of examples and proofs of the
application of the framework to the neural encoding of various forms of
knowledge representation and neural network. These, at first sight disparate
approaches, are all shown to fall within the framework's formal definition of
what we call semantic encoding for neural-symbolic AI
Tutorial: Neuro-symbolic AI for Mental Healthcare
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, to predict MH conditions. Subsequently, researchers created classification schemes to measure the severity of each MH condition. Such ad-hoc schemes, engineered features, and models not only require a large amount of data but fail to allow clinically acceptable and explainable reasoning over the outcomes. To improve Neural-AI for MHCare, infusion of clinical symbolic knowledge that clinicans use in decision making is required. An impactful use case of Neural-AI systems in MH is conversational systems. These systems require coordination between classification and generation to facilitate humanistic conversation in conversational agents (CA). Current CAs with deep language models lack factual correctness, medical relevance, and safety in their generations, which intertwine with unexplainable statistical classification techniques. This lecture-style tutorial will demonstrate our investigations into Neuro-symbolic methods of infusing clinical knowledge to improve the outcomes of Neural-AI systems to improve interventions for MHCare:(a) We will discuss the use of diverse clinical knowledge in creating specialized datasets to train Neural-AI systems effectively. (b) Patients with cardiovascular disease express MH symptoms differently based on gender differences. We will show that knowledge-infused Neural-AI systems can identify gender-specific MH symptoms in such patients. (c) We will describe strategies for infusing clinical process knowledge as heuristics and constraints to improve language models in generating relevant questions and responses
Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits. On
MNIST and Fashion datasets, our learning algorithm outperforms neural networks
that have an order of magnitude more parameters. Yet, logistic circuits have a
distinct origin in symbolic AI, forming a discriminative counterpart to
probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that
parameter learning for logistic circuits is convex optimization, and that a
simple local search algorithm can induce strong model structures from data.Comment: Published in the Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI19
A hybrid job-shop scheduling system
The intention of the scheduling system developed at the Fraunhofer-Institute for Material Flow and Logistics is the support of a scheduler working in a job-shop. Due to the existing requirements for a job-shop scheduling system the usage of flexible knowledge representation and processing techniques is necessary. Within this system the attempt was made to combine the advantages of symbolic AI-techniques with those of neural networks
Not just for humans: Explanation for agent-to-agent communication
Once precisely defined so as to include just the explanation\u2019s act, the notion of explanation should be regarded as a central notion in the engineering of intelligent system\u2014not just as an add-on to make them understandable to humans. Based on symbolic AI techniques to match intuitive and rational cognition, explanation should be exploited as a fundamental tool for inter-agent communication among heterogeneous agents in open multi-agent systems. More generally, explanation-ready agents should work as the basic components in the engineering of intelligent systems integrating both symbolic and sub-/non-symbolic AI techniques
Object Action Complexes as an Interface for Planning and Robot Control
Abstract — Much prior work in integrating high-level artificial intelligence planning technology with low-level robotic control has foundered on the significant representational differences between these two areas of research. We discuss a proposed solution to this representational discontinuity in the form of object-action complexes (OACs). The pairing of actions and objects in a single interface representation captures the needs of both reasoning levels, and will enable machine learning of high-level action representations from low-level control representations. I. Introduction and Background The different representations that are effective for continuous control of robotic systems and the discrete symbolic AI presents a significant challenge for integrating AI planning research and robotics. These areas of research should be abl
V-LoL: A Diagnostic Dataset for Visual Logical Learning
Despite the successes of recent developments in visual AI, different
shortcomings still exist; from missing exact logical reasoning, to abstract
generalization abilities, to understanding complex and noisy scenes.
Unfortunately, existing benchmarks, were not designed to capture more than a
few of these aspects. Whereas deep learning datasets focus on visually complex
data but simple visual reasoning tasks, inductive logic datasets involve
complex logical learning tasks, however, lack the visual component. To address
this, we propose the visual logical learning dataset, V-LoL, that seamlessly
combines visual and logical challenges. Notably, we introduce the first
instantiation of V-LoL, V-LoL-Trains, -- a visual rendition of a classic
benchmark in symbolic AI, the Michalski train problem. By incorporating
intricate visual scenes and flexible logical reasoning tasks within a versatile
framework, V-LoL-Trains provides a platform for investigating a wide range of
visual logical learning challenges. We evaluate a variety of AI systems
including traditional symbolic AI, neural AI, as well as neuro-symbolic AI. Our
evaluations demonstrate that even state-of-the-art AI faces difficulties in
dealing with visual logical learning challenges, highlighting unique advantages
and limitations specific to each methodology. Overall, V-LoL opens up new
avenues for understanding and enhancing current abilities in visual logical
learning for AI systems
From Neural Activations to Concepts: A Survey on Explaining Concepts in Neural Networks
In this paper, we review recent approaches for explaining concepts in neural
networks. Concepts can act as a natural link between learning and reasoning:
once the concepts are identified that a neural learning system uses, one can
integrate those concepts with a reasoning system for inference or use a
reasoning system to act upon them to improve or enhance the learning system. On
the other hand, knowledge can not only be extracted from neural networks but
concept knowledge can also be inserted into neural network architectures. Since
integrating learning and reasoning is at the core of neuro-symbolic AI, the
insights gained from this survey can serve as an important step towards
realizing neuro-symbolic AI based on explainable concepts.Comment: Submitted to Neurosymbolic Artificial Intelligence
(https://neurosymbolic-ai-journal.com/paper/neural-activations-concepts-survey-explaining-concepts-neural-networks
Formalizing consistency and coherence of representation learning
In the study of reasoning in neural networks, recent efforts have sought to improve consistency and coherence of sequence models, leading to important developments in the area of neuro-symbolic AI. In symbolic AI, the concepts of consistency and coherence can be defined and verified formally, but for neural networks these definitions are lacking. The provision of such formal definitions is crucial to offer a common basis for the quantitative evaluation and systematic comparison of connectionist, neuro-symbolic and transfer learning approaches. In this paper, we introduce formal definitions of consistency and coherence for neural systems. To illustrate the usefulness of our definitions, we propose a new dynamic relation-decoder model built around the principles of consistency and coherence. We compare our results with several existing relation-decoders using a partial transfer learning task based on a novel data set introduced in this paper. Our experiments show that relation-decoders that maintain consistency over unobserved regions of representation space retain coherence across domains, whilst achieving better transfer learning performance
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