Expert System for UNIX System Reliability and Availability Enhancement

Highly reliable and available systems are critical to the airline industry. However, most off-the-shelf computer operating systems and hardware do not have built-in fault tolerant mechanisms, the UNIX workstation is one example. In this research effort, we have developed a rule-based Expert System (ES) to monitor, command, and control a UNIX workstation system with hot-standby redundancy. The ES on each workstation acts as an on-line system administrator to diagnose, report, correct, and prevent certain types of hardware and software failures. If a primary station is approaching failure, the ES coordinates the switch-over to a hot-standby secondary workstation. The goal is to discover and solve certain fatal problems early enough to prevent complete system failure from occurring and therefore to enhance system reliability and availability. Test results show that the ES can diagnose all targeted faulty scenarios and take desired actions in a consistent manner regardless of the sequence of the faults. The ES can perform designated system administration tasks about ten times faster than an experienced human operator. Compared with a single workstation system, our hot-standby redundancy system downtime is predicted to be reduced by more than 50 percent by using the ES to command and control the system

Decision-making and problem-solving methods in automation technology

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming

Hierarchy in Knowledge Representations

This research was conducted at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract number N00014-75-C-0643.This paper discusses a number of problems faced in communicating expertise and common sense to a computer, and the approaches taken by several current knowledge representation languages towards solving these problems. The main topic discussed is hierarchy. The importance of hierarchy is almost universally recognized. Hierarchy forms the backbone of many existing representation languages. We discuss several technical problems raised in constructing hierarchical and almost hierarchical systems as criteria and open problems.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

ExpertiSZe, a tool for determining the effects of social security legislation

Social security legislation plays an important role in the Dutch society. In view of this, the effects of social security legislation have to be analysed carefully before new legislation can be made. Due to the growing complexity of legislation on the social security domain, this analysis has become a demanding task. ExpertiSZe is a knowledge-based system developed to support the process of analysing juridical and socio-economic effects of social security legislation. The ExpertiSZe system consists of three modules: a consultation module, a consistency module and a simulation module. These modules, which all work on the basis of the same rule-based model, provide the legislator with more insight into the impact of legislation. This article describes the potential of ExpertiSZe to support the analysis of effects of legislation

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research. We propose a full-view categorization and new taxonomies on these topics. Knowledge graph embedding is organized from four aspects of representation space, scoring function, encoding models, and auxiliary information. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We further explore several emerging topics, including meta relational learning, commonsense reasoning, and temporal knowledge graphs. To facilitate future research on knowledge graphs, we also provide a curated collection of datasets and open-source libraries on different tasks. In the end, we have a thorough outlook on several promising research directions

Physical Object Representation and Generalization: A Survey of Natural Language Processing Programs

This paper surveys a portion of the field of natural language processing. The main areas considered are those dealing with representation schemes, particularly work on physical object representation, and generalization processes driven by natural language understanding. Five programs serve as case studies for guiding the course of the paper. Within the framework of describing each of these programs, seven other programs, ideas and theories that are relevant to the program in focus are presented. Our current work which integrates representation and generalization is also discussed

Unlocking the Power of Large Language Models for Entity Alignment

Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs' capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA's superior performance, highlighting LLMs' potential in facilitating EA tasks

A Semantic Information Management Approach for Improving Bridge Maintenance based on Advanced Constraint Management

Bridge rehabilitation projects are important for transportation infrastructures. This research proposes a novel information management approach based on state-of-the-art deep learning models and ontologies. The approach can automatically extract, integrate, complete, and search for project knowledge buried in unstructured text documents. The approach on the one hand facilitates implementation of modern management approaches, i.e., advanced working packaging to delivery success bridge rehabilitation projects, on the other hand improves information management practices in the construction industry