Expressiveness Of First-order Logic Research Articles

Learning differentiable logic programs for abstract visual reasoning

AbstractVisual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. However, due to the memory intensity, most existing approaches do not bring the best of the expressivity of first-order logic, excluding a crucial ability to solve abstract visual reasoning, where agents need to perform reasoning by using analogies on abstract concepts in different scenarios. To overcome this problem, we propose NEUro-symbolic Message-pAssiNg reasoNer (NEUMANN), which is a graph-based differentiable forward reasoner, passing messages in a memory-efficient manner and handling structured programs with functors. Moreover, we propose a computationally-efficient structure learning algorithm to perform explanatory program induction on complex visual scenes. To evaluate, in addition to conventional visual reasoning tasks, we propose a new task, visual reasoning behind-the-scenes, where agents need to learn abstract programs and then answer queries by imagining scenes that are not observed. We empirically demonstrate that NEUMANN solves visual reasoning tasks efficiently, outperforming neural, symbolic, and neuro-symbolic baselines.

Constraint-Based Schedulability Analysis in Multiprocessor Real-Time Systems

As the demand for more functions and capabilities in the system increases, the application of multiprocessors has brought advantages in many ways. Many systems now have multiprocessors, and safety-critical systems with real-time properties are no exception. In these systems where the satisfaction of real-time properties is directly linked to the safety of life, the predictability of the behavior is very important, and the behavior of the system can be predicted using the schedulability analysis. In this paper, we propose the schedulability analysis of a real-time system in a homogeneous multiprocessor environment through constraint solving approach. First, the constraints that must be satisfied in order for the system to function properly were derived. These include the constraints of the task behavior, the scheduling behavior, and the operating conditions of a homogeneous multiprocessor environment. Once all the constraints were identified, they were encoded in the form of first-order logic expressions. The encoded constraints are then entered into a constraint solver along with a set of tasks. Finally, the solver provides a schedulable answer if the set of tasks satisfies all the constraints.

Ensemble method to joint inference for knowledge extraction

Traditionally, the probably approximately correct (PAC) learning refers the single concept class. We discuss the PAC framework of the multiple tasks in the joint inference model. And we extend PAC learning to multi-concept classes.We present an ensemble learning approach to joint inference on the three NLP sub-tasks. We explain how to combine those weak learners to a strong ability and present the dynamic weighted combination method in the ensemble joint inference model.Our Ensemble Markov Logic Networks (EMLNs) address the problem of the Markov Logic Networks intractable dealing with the large scale data. Experiments show that this approach leads to a higher precision and recall than that of pipeline approaches. Joint inference is a fundamental issue in the field of artificial intelligence. The greatest advantage of the joint inference is demonstrated by its capability of avoiding errors from cascading and accumulating on a pipeline of multiple chained sub-tasks. Markov Logic Network(MLN) is the most common joint inference model that provides a flexible representation and handles uncertainty. It has been applied successfully to joint inference on many natural language processing tasks to avoid error propagation. However, due to the great expressiveness of first-order logic, the representation for it in MLN generates rather complicated graph structures, which makes the learning and inference on large scale data intractably. In this paper, we present an ensemble learning approach to deal with the challenges in MLNs. Firstly, we give a proof within the probably approximately correct (PAC) framework. The proof points out what conditions are necessary for successful applying the ensemble learning approach to MLN. Secondly, the paper explains how to combine the learners. Finally, in order to illustrate the working mechanism of the ensemble joint inference model, we present an Ensemble Markov Logic Networks (EMLNs) method and use it to extract knowledge from a large scale corpus published by Google.11code.google.com/p/relation-extraction-corpus/. Experiments suggest that significant speedup can be gained by the EMLNs. Meanwhile, it show that this approach leads to a higher precision and recall than that of those pipeline approaches.

Dynamic Context-Aware Event Recognition Based on Markov Logic Networks.

Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data.

News events prediction using Markov logic networks

Predicting future events from text data has been a controversial and much disputed topic in the field of text analytics. However, far too little attention has been paid to efficient prediction in textual environments. This study has aimed to develop a novel and efficient method for news event prediction. The proposed method is based on Markov logic networks (MLNs) framework, which enables us to concisely represent complex events by full expressivity of first-order logic (FOL), as well as to reason uncertain event with probabilities. In our framework, we first extract text news events via an event representation model at a semantic level and then transform them into web ontology language (OWL) as a posteriori knowledge. A set of domain-specific causal rules in FOL associated with weights were also fed into the system as a priori (common-sense) knowledge. Additionally, several large-scale ontologies including DBpedia, VerbNet and WordNet were used to model common-sense logic rules as contextual knowledge. Finally, all types of such knowledge were integrated into OWL for performing causal inference. The resulted OWL knowledge base is augmented by MLN, which uses weighted first-order formulas to represent probabilistic knowledge. Empirical evaluation of real news showed that our method of news event prediction was better than the baselines in terms of precision, coverage and diversity.

Attention Assist: A High-Level Information Fusion Framework for Situation and Threat Assessment in Vehicular Ad Hoc Networks

Driver inattentiveness constitutes the main cause of road accidents, which makes it a major factor in road safety. In this paper, we propose a comprehensive framework to address the road safety problem by tackling it from a high-level information fusion standpoint, considering vehicular ad hoc networks (VANETs) as the deployment platform. The proposed framework relies on the multientity Bayesian networks (MEBNs), which exploit the expressiveness of first-order logic for semantic relations, and the strength of the Bayesian networks in handling uncertainty. First, the entities that influence the inattention phenomenon, as well as both their causal and semantic relationships, are identified. Next, an MEBN-based high-level information fusion framework is proposed through which entities, situations, and their relationships in specific contexts are modeled using MEBN fragments. Furthermore, MEBN inference is used to assess the situations of interest by estimating their states. To demonstrate the capabilities of the proposed framework, a collision warning system simulator has been developed, which evaluates the likelihood of a vehicle being in a near-collision situation using a wide variety of local and global information sources available in various VANET environments. If the threat of being in a near-collision situation is determined to be high, then the driver is warned accordingly. Our experimental results for two distinct single-vehicle and multivehicle categories of driving scenarios, as well as a novel hybrid MEBN inference, demonstrate the capability of the proposed framework to efficiently achieve situation and threat assessment on the road.

Algorithm of computing verbal relationships for generating Vietnamese paragraph of summarization from the logical expression of discourse representation structure

This paper presents an important phase of our new approach for summarizing the given Vietnamese paragraph. The central of this phase is an algorithm for computing verbal relationships in the process of generating the Vietnamese paragraph from the logical expression of discourse representation structure (DRS), which is the first-order logic expressions without explicit quantifiers, and represents the meaning as well as reflects the potential contexts of a given discourse or a sequence of sentences. By defining elements to describe the appropriate information in each predicate of the logical expression (or can be called “DRS-conditions”), the algorithm is based on in turn considering three consecutive predicates in a logical expression for determining: the relationship between the first and second sentence, the relationship between the second and third sentence, and the priority when comparing these two relationships. The evaluation achieves two given criteria: the semantic completeness of summarization, and the natural quality of new reduced paragraph.

Risk Prediction for Production of an Enterprise

Despite all preventive measures, there is so much possibility of risks in any project development as well as in enterprise ma nagement. There is no any standard mechanism or methodology available to assess the risks in any project or production management. Using some precautionary steps, the manager can only avoid the risks as much as he can. To address this issue, this paper presents a probabilistic risk assessment model for the production of an enterprise. For this, Multi-Entity Bayesian Network (MEBN) has been used to represent the requirements for production management as well as to assess the risks adherence in production management, where MEBN combines expressivity of first-order logic and probabilistic feature of Bayesian network. Bayesian network provides the feature to represent the probabilistic uncertainty and reasoning about probabilistic knowledge base, which is used here to represent the probable risks behind each causes of a risk. The proposed probabilistic model is discussed with the help of a case study, which is used to predict risks inherent in the production of an enterprise, which depends upon various measures like labour availability, power backup, transport availability etc.

Hierarchical semantic-based index for ad hoc image retrieval

Ad hoc networks have received considerable research attention by provisions of wireless communications without location limitations and pre-built fixed infrastructure. Because of the absence of any static support infrastructure, ad hoc networks are prone to several limitations such as bandwidth, connectivity, and power. The traditional content-based image retrieval approaches employed in ad hoc networks may result in either high search cost or low fault tolerance. In this paper, we propose and analyze a decentralized nonflooding image retrieval scheme in multi-hop mobile ad hoc networks - Semantic Ad hoc Image Retrieval (SAIR). The novelty of SAIR stems from several factors including: (1) representation of image contents using first-order logic expressions; (2) clustering mobile nodes based on their data contents; (3) organizing image data with a hierarchical semantic-based indexing infrastructure; (4) performing content-based image retrieval within a reduced scope of mobile nodes. Through extensive simulations, we show that relative to the flooding strategy, SAIR can retrieve the semantically most similar image objects by accessing only a small portion of the mobile nodes with much lower search cost. Moreover, it is scalable to large network sizes and large number of data objects.

Pushing the envelope: challenges in a frame-based representation of human anatomy

One of the main threads in the history of knowledge-representation formalisms is the trade-off between the expressiveness of first-order logic on the one hand and the tractability and ease-of-use of frame-based systems on the other hand. Frame-based systems provide intuitive, cognitively easy-to-understand, and scalable means for modeling a domain. However, when a domain model is particularly complex, frame-based representation may lead to complicated and sometimes awkward solutions. We have encountered such problems when developing the Digital Anatomist Foundational Model, an ontology aimed at representing comprehensively the physical organization of the human body. We show that traditional frame-based techniques such as is-a hierarchies, slots (roles) and role restrictions are not sufficient for a comprehensive model of this domain. The diverse modeling challenges and problems in this project required us to use such knowledge-representation techniques as reified relations, metaclasses and a metaclass hierarchy, different propagation patterns for template and own slots, and so on. We posit that even though the modeling structure imposed by frame-based systems may sometimes lead to complicated solutions, it is still worthwhile to use frame-based representation for very large-scale projects such as this one.