Market Statistics: The report provides market sizing and forecast across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2020 are considered as historical years, 2021 as the base year, 2022 as the estimated year, and years from 2023 to 2027 are considered as the forecast period. Market Segmentation & Coverage: This research report categorizes the Artificial Intelligence in Oil & Gas to forecast the revenues and analyze the trends in each of the following sub-markets: Based on Function, the market was studied across Field Services, Material Movement, Predictive Maintenance & Machine Inspection, Production Planning, Quality Control, and Reclamation. Based on Component, the market was studied across Hardware, Services, and Software.

Topology optimization has emerged as a popular approach to refine a component's design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite element analysis iterations required to evaluate the component's performance during the optimization process. Recently, machine learning-based topology optimization methods have been explored by researchers to alleviate this issue. However, previous approaches have mainly been demonstrated on simple two-dimensional applications with low-resolution geometry. Further, current approaches are based on a single machine learning model for end-to-end prediction, which requires a large dataset for training.

What are the powerful new ideas in knowledge based design? What important research issues require further investigation? Perhaps the key research problem in AIbased design for the 1980's is to develop better models of the design process. A comprehensive model of design should address the following aspects of the design process: the state of the design; the goal structure of the design process; design decisions; rationales for design decisions; control of the design process; and the role of learning in design This article presents some of the most important ideas emerging from current AI research on design, especially ideas for better models of design It is organized into sections dealing with each of the aspects of design listed above What is design? Why should we study it?

SINCE WE ARE FIRMLY CONVINCED that ideas like people have histories and can only be fully understood in the context of those histories, we will begin by discussing the underlying ideas that motivated us to initiate the Steamer effort. Without richer and more detailed understandings of the nature of these models, instructional applications will be severely limited. Graphical Interfaces for Interactave Inspectable Simulatzons - We believe that graphical interfaces to simulations of physical systems deserve extensive exploration. They make possible new types of instructional interactions by allowing one to control, manipulate, and monitor simulations of dynamic systems at many different hierarchical levels The key idea in Steamer is the conception of an znteractive inspectable simulation. We have consistently sought to make the system inspectable.

Department of Computer Science Carnegie-Mellon University Pittsburgh, Pennsylvania 15221 RI is a rule-based program that configures VAX-I 1 computer systems. Given a customer's purchase order, it determines what, if any, substitutions and additions have to be made to the order to make it consistent and complete and produces a numnber of diagrams showing the spatial and logical relationships among the 90 or so components that typically constitute a system. The program has been used on a regular basis by Digital Equipment Corporation's manufacturing organization since January of 1980. Rl has sufficient knowledge of the configuration domain and of the pecularities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do; thus it requires little search in order to configure a computer system. The approach RI takes to the configuration task and the way its knowledge is represented has been described elsewhere [McDermott 80a, MC Dermott 80b].

The automotive industry was the first to promote the development of applications of model-based systems technology on a broad scale and, as a result, has produced some of the most advanced prototypes and products. In this article, we illustrate the features and benefits of model-based systems and qualitative modeling by prototypes and application systems that were developed in the automotive industry to support on-board diagnosis, design for diagnosability, and failure modes and effects analysis. Car manufacturers and their suppliers face increasingly serious challenges particularly related to fault analysis and diagnosis during the life cycle of their products. On the one hand, the complexity and sophistication of vehicles is growing, so it is becoming harder to predict interactions between vehicle systems, especially when failures occur. On the other hand, legal regulations and the demand for safety impose strong requirements on the detection and identification of faults and the prevention of their effects on the environment or dangerous situations for passengers and other people.

This research was sponsored in part by the Office of Naval Research under contract NOOO14.80-C-0732 and the National Science Foundation under grant hZCS79-06543 IUNIX is trademark of Bell Laboratories These include the following: 1. A robust language analyzer, which almost never has a "hard" failure and which has the ability to handle most elliptical constructions in context 2 A context and memory mechanism that determines the focus of attention and helps with lexical and syntactic disambiguation, and with some aspects of pronominal reference. While some of the components of the system are experimental in nature: the basic features of UC provide a usable device to obtain information about UNIX. In addition, THE AI,MAGAZINE Spring 1984 29 it is straightforward to extend UC's knowledge base to cover UNIX with which UC is not currently familiar. How do I delete a file?

The system consists of an overall agent architecture and five components within the architecture. The five components are (1) goaldirected learning (GDL), a decisiontheoretic method for selecting learning goals; (2) probabilistic bias evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals; (3) uniquely predictive theories (UPTs) and probability computation using independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories; (4) a probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories; and (5) a decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA's initial learning goal is just An autonomous agent must be able to select biases (Mitchell 1980) for new learning tasks as they arise. PBE uses probabilistic background knowledge and a model of the system's expected learning performance to compute the expected value of learning biases for each learning goal. The resulting expected discounted future accuracy is used as the expected value of the bias.

A recent article by Ronald Brachman (Brachman, 1985) points out some philosophical or semantic problems in using the notion of a prototype, which is described by using default properties. The problem arises since default properties can be overridden or cancelled in representing particular instances, and therefore lack definitional power: i.e., they are not really essential to the concept being represented. As an example, Brachman presents an elephant joke: Q: What's big and gray, has a trunk, and lives in the trees? A: An elephant-I lied about the trees. Before discussing a solution to this dilemma, consider the following modified version of the elephant joke, perhaps not quite as funny: Q: What's big and gray, has a trunk, and lives in the trees?

We propose adding to the mix a novel architecture, the process-integrated mechanism (PIM), that enjoys the advantages of having a single controlling authority while avoiding the structural difficulties that have traditionally led to the rejection of centralized approaches in many complex settings. In many situations, PIMs improve on previous models with regard to coordination, security, ease of software development, robustness, and communication overhead. In the PIM architecture, the components are conceived as parts of a single mechanism, even when they are physically separated and operate asynchronously. The PIM model offers promise as an effective infrastructure for handling tasks that require a high degree of time-sensitive coordination between the components, as well as a clean mechanism for coordinating the high-level goals of loosely coupled systems. The PIM model enables coordination without the fragility and high communication overhead of centralized control, but also without the uncertainty associated with the system-level behavior of a multiagent system (MAS).