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Found 39 papers, 12 papers with code
Simple and Effective Transfer Learning for Neuro-Symbolic Integration
Then, a NeSy model is trained on the same task via transfer learning, where the weights of the perceptual part are injected from the pretrained network.
Lifted Inference beyond First-Order Logic
We expand a vast array of previous results in discrete mathematics literature on directed acyclic graphs, phylogenetic networks, etc.
logLTN: Differentiable Fuzzy Logic in the Logarithm Space
A significant trend in the literature involves integrating axioms and facts in loss functions by grounding logical symbols with neural networks and operators with fuzzy semantics.
Weakly-Supervised Visual-Textual Grounding with Semantic Prior Refinement
The first untrained module aims to return a rough alignment between textual phrases and bounding boxes.
Interval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data.
Weighted First Order Model Counting with Directed Acyclic Graph Axioms
However, many properties of real-world data can not be modelled in $\mathrm{C^2}$.
Planning for Learning Object Properties
Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data.
Exploiting Proximity-Aware Tasks for Embodied Social Navigation
In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximity Compass) to inject into a reinforcement learning navigation policy the ability to infer common-sense social behaviors.
Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
In this paper, we propose Deep Symbolic Learning (DSL), a NeSy system that learns NeSy-functions, i. e., the composition of a (set of) perception functions which map continuous data to discrete symbols, and a symbolic function over the set of symbols.
Refining neural network predictions using background knowledge
Using a new algorithm called Iterative Local Refinement (ILR), we combine refinement functions to find refined predictions for logical formulas of any complexity.
Knowledge Enhanced Neural Networks for relational domains
In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i. e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds.
On Projectivity in Markov Logic Networks
We show that, in terms of data likelihood maximization, RBM is the best possible projective MLN in the two-variable fragment.
Online Learning of Reusable Abstract Models for Object Goal Navigation
In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task.
On some Foundational Aspects of Human-Centered Artificial Intelligence
The burgeoning of AI has prompted recommendations that AI techniques should be "human-centered".
Online Grounding of Symbolic Planning Domains in Unknown Environments
If a robotic agent wants to exploit symbolic planning techniques to achieve some goal, it must be able to properly ground an abstract planning domain in the environment in which it operates.
Weighted Model Counting in FO2 with Cardinality Constraints and Counting Quantifiers: A Closed Form Formula
Weighted First-Order Model Counting (WFOMC) computes the weighted sum of the models of a first-order logic theory on a given finite domain.
A formalisation of BPMN in Description Logics
In this paper we present a textual description, in terms of Description Logics, of the BPMN Ontology, which provides a clear semantic formalisation of the structural components of the Business Process Modelling Notation (BPMN), based on the latest stable BPMN specifications from OMG [BPMN Version 1. 1 -- January 2008].
A Better Loss for Visual-Textual Grounding
Given a textual phrase and an image, the visual grounding problem is the task of locating the content of the image referenced by the sentence.
Reasoning on $\textit{DL-Lite}_{\cal R}$ with Defeasibility in ASP
In this direction, in our previous works we presented a framework for representing (contextualized) OWL RL knowledge bases with a notion of justified exceptions on defeasible axioms: reasoning in such framework is realized by a translation into ASP programs.
Logic Tensor Networks
In this paper, we present Logic Tensor Networks (LTN), a neurosymbolic formalism and computational model that supports learning and reasoning through the introduction of a many-valued, end-to-end differentiable first-order logic called Real Logic as a representation language for deep learning.
Weighted Model Counting in the two variable fragment with Cardinality Constraints: A Closed Form Formula
We introduce the concept of lifted interpretations as a tool for formulating polynomials for WFOMC.
Neural Networks Enhancement with Logical Knowledge
In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i. e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds.
Exploiting Scene-specific Features for Object Goal Navigation
We study this question in the context of Object Navigation, a problem in which an agent has to reach an object of a specific class while moving in a complex domestic environment.
A Note on Reasoning on $\textit{DL-Lite}_{\cal R}$ with Defeasibility
Representation of defeasible information is of interest in description logics, as it is related to the need of accommodating exceptional instances in knowledge bases.
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
In spite of the recent impact of AI, several works have identified the need for principled knowledge representation and reasoning mechanisms integrated with deep learning-based systems to provide sound and explainable models for such systems.
Incremental Learning of Discrete Planning Domains from Continuous Perceptions
We propose a framework for learning discrete deterministic planning domains.
Incremental learning abstract discrete planning domains and mappings to continuous perceptions
Most of the works on planning and learning, e. g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain.
Reasoning with Justifiable Exceptions in Contextual Hierarchies (Appendix)
This paper is an appendix to the paper "Reasoning with Justifiable Exceptions in Contextual Hierarchies" by Bozzato, Serafini and Eiter, 2018.
Logic Tensor Networks for Semantic Image Interpretation
Logic Tensor Networks (LTNs) are an SRL framework which integrates neural networks with first-order fuzzy logic to allow (i) efficient learning from noisy data in the presence of logical constraints, and (ii) reasoning with logical formulas describing general properties of the data.
On Coreferring Text-extracted Event Descriptions with the aid of Ontological Reasoning
Systems for automatic extraction of semantic information about events from large textual resources are now available: these tools are capable to generate RDF datasets about text extracted events and this knowledge can be used to reason over the recognized events.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning.
Query Answering over Contextualized RDF/OWL Knowledge with Forall-Existential Bridge Rules: Decidable Finite Extension Classes (Post Print)
Sound, complete and terminating procedures, which are adaptations of the well known chase algorithm, are provided for these classes for deciding query entailment.
Semantic Enrichment of Mobile Phone Data Records Using Background Knowledge
This article addresses the issues in context awareness given heterogeneous and uncertain data of mobile network events missing reliable information on the context of this activity.
Knowledge Propagation in Contextualized Knowledge Repositories: an Experimental Evaluation
As the interest in the representation of context dependent knowledge in the Semantic Web has been recognized, a number of logic based solutions have been proposed in this regard.
Query Answering over Contextualized RDF/OWL Knowledge with Forall-Existential Bridge Rules: Attaining Decidability using Acyclicity (full version)
Quads, which extend a standard RDF triple, by adding a new parameter of the `context' of an RDF triple, thus informs a reasoner to distinguish between the knowledge in various contexts.
NewsReader: recording history from daily news streams
The European project NewsReader develops technology to process daily news streams in 4 languages, extracting what happened, when, where and who was involved.
The KnowledgeStore: an Entity-Based Storage System
The system allows (i) to import background knowledge about entities, in form of annotated RDF triples; (ii) to associate resources to entities by automatically recognizing, coreferring and linking mentions of named entities; and (iii) to derive new entities based on knowledge extracted from mentions.
