2 edition of Towards the application of artificial intelligence techniques for discrete event simulation found in the catalog.
Towards the application of artificial intelligence techniques for discrete event simulation
Thesis (Ph.D.) - University of Warwick, 1986.
|Statement||by Andrew Flitman.|
Occupancy Visualization towards Activity Recognition Workshop ACM Workshop on Device-Free Human Sensing Novem A Symmetric Formalism for Discrete Event Simulation with Agents WSC Conference proceedings: Winter Simulation Conference 12 pages A Cross-Application Bridge from Minecraft to 3D Modeling. APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN UNINHABITED AERIAL VEHICLE FLIGHT Warren R. Duji-ene, Jr., Graduate School of Computer and Information Sciences, Nova Southeastern Universig, Ft. Lauderdale, FL Abstract This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control.
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White paper: Artificial Intelligence and Simulation in Business. Simulation is important for artificial intelligence because it provides solutions to some of the main problems faced by AI developers today. These solutions have immediate potential and are producing results already. Read on and get the whitepaper with example model Author: Gavin Wilkinson. Artificial Intelligence for Fashion Industry in the Big Data Era. Editors This book provides an overview of current issues and challenges in the fashion industry and an update on data-driven artificial intelligence (AI) techniques and their potential implementation in response to those challenges. A Discrete Event Simulation Model with.
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The possibility of incorporating Artificial Intelligence (A.I) techniques into Visual Interactive Discrete Event Simulation was examined.
After a study the current state of the art, work was undertaken to investigate the usefulness of PROLOG as a simulation language.
The possibility of incorporating Artificial Intelligence (A.I) techniques into Visual Interactive Discrete Event Simulation was examined. After a study the current state of the art, work was undertaken to investigate the usefulness of PROLOG as a simulation language.
This led to the development of a working. The possibility of incorporating Artificial Intelligence (A.I) techniques into Visual Interactive Discrete Event Simulation was examined. After a study the current state of the art, work was undertaken to investigate the usefulness of PROLOG as a simulation by: 2.
Artificial intelligence techniques promise significant advantages in discrete simulation modelling. In particular object-oriented programming offers the possibility of mapping simulated entities onto programme objects, so that class inheritance simplifies definition of similar, non-identical : Keith Popplewell.
Although the fields of artificial intelligence (AI) and simulation are both concerned with the development and use of models, these fields evolved almost independently until relatively recently. Now, the potential contributions of AI to the development and application of simulation models are being considered more by: Based on a discrete-event simulation system in an object-oriented programming environment, this simulator aims to offer a general and user-extensible framework for the simulation of spatially.
This article discusses the role of Artificial Intelligence (AI) in Modeling and Simulation (M&S). AI is the field of computer science that attempts to construct computer systems that emulate human problem solving behavior with the goal of understanding human intelligence.
M&S is a multidisciplinary field of systems engineering, softwareFile Size: 1MB. Simulation modelling is one of the areas in operational research that have generated considerable interest in artificial intelligence and expert systems.
This paper reviews some of the uses that AI techniques have been put to in simulation, as well as mentioning some of the uses that AI has made of by: 8. ABS into simulation courses.
Modelers often come to agent-based simulation by way of self-study or at-tendance at tutorials and short courses. Although there is substantial overlap, there are many aspects of ABS that differ from discrete-event simulation (DES) and System Dynamics (SD), including applicableFile Size: KB.
This paper describes a range of AI techniques: case-based reasoning, rule-based systems, artificial neural networks, genetic algorithms, cellular automata, fuzzy models, multi-agent systems, swarm intelligence, reinforcement learning and hybrid by: Intelligent systems, based on development of artificial intelligence, as well as simulation and specialized software will make students become familiar with models, structure of intelligent agents and machine learning, and they will gain necessary knowledge, skills for application of artificial neural networks (ANN) in advanced technologies.
In this paper, we describe an ontologically well-founded agent- based discrete event simulation framework with a high-level rule- based simulation language and an abstract simulator architecture Author: Gerd Wagner. Artificial intelligence concerned with making computers behave like humans more human like fashion and in much less time then a human takes.
Hence it is called as Artificial Intelligence. Artificial intelligence can be divided into parts according to philosophy of AI. Artificial Intelligence (abbreviated as AI) is the capability. Discrete-event simulation has become a successful computer science/industrial engineering method in semiconductor companies and in academic research for the semiconductor industry.
In this presentation, we will discuss discrete-event simulation applications in the semiconductor domain. A survey of some artiﬁcial intelligence ideas We will comment at the end of the chapter that more techniques could be tested; indeed one could almost systematically take an artiﬁcial intelligence text-book and choose some meta-heuristic method for solving hard problems, and then try to adapt it to the task of learning grammars or File Size: KB.
Artificial intelligence, simulation & modeling September September These advances have been complemented by computational tools for the automatic verification and simulation of hybrid systems.
This course will present the recent advances in modeling, analysis, control, and verification of hybrid systems. Topics will include: continuous-time and discrete-event models safety specifications and model checking.
artiﬁcial intelligence, such as expert systems and neural networks. Second, you will learn the requirements, purposes, advantages and limitations of models and simulations. The chapter concludes by discussing the use of modelling and simulation programs.
Artificial intelligence, simulation and modelling ISTech Chapt pp 23/9/04 Discrete event simulation environments have desirable features and components now driving researchers to develop and enhance existing environments.
The Handbook of Research on Discrete Event Simulation Environments: Technologies and Applications provides a comprehensive overview of theory and practice in simulation systems. A leading. White paper: Artificial Intelligence and Simulation in Business.
Simulation is important for artificial intelligence because it provides solutions to some of the main problems faced by AI developers today. These solutions have immediate potential and are producing results already. Read on and get the whitepaper with example model Author: Arash Mahdavi.
Simulation studies cycle through the phases of formulation, programming, verification and validation, experimental design and analysis, and implementation.
The work presented has been concerned with developing methods to enhance the practice and support for the experimental design and analysis phase of a study. The investigation focussed on the introduction of Artificial Intelligence (AI Cited by: 4.
Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers.
Events can be analyzed in a lot of ways, including sequence, location, numerical, categorical, image, text, and relationship data. These events can be google calendar events, a medical alarm, dates on a dating website, or in the case of GenRush, events can indicate new potential clients for a company based on events in the real world.
These event understanding problems are classic.