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Modelling human behaviours and reactions under dangerous environment
This paper describes the framework of a real-time simulation system to model human behavior and reactions in dangerous environments. The system utilizes the latest 3D computer animation techniques, combined with artificial intelligence, robotics and psychology, to model human behavior, reactions and decision making under expected/unexpected dangers in real-time in virtual environments. The development of the system includes: classification on the conscious/subconscious behaviors and reactions of different people; capturing different motion postures by the Eagle Digital System; establishing 3D character animation models; establishing 3D models for the scene; planning the scenario and the contents; and programming within Virtools (TM) Dev. Programming within Virtools (TM) Dev is subdivided into modeling dangerous events, modeling character's perceptions, modeling character's decision making, modeling character's movements, modeling character's interaction with environment and setting up the virtual cameras. The real-time simulation of human reactions in hazardous environments is invaluable in military defense, fire escape, rescue operation planning, traffic safety studies, and safety planning in chemical factories, the design of buildings, airplanes, ships and trains. Currently, human motion modeling can be realized through established technology, whereas to integrate perception and intelligence into virtual human's motion is still a huge undertaking. The challenges here are the synchronization of motion and intelligence, the accurate modeling of human's vision, smell, touch and hearing, the diversity and effects of emotion and personality in decision making. There are three types of software platforms which could be employed to realize the motion and intelligence within one system, and their advantages and disadvantages are discussed
Algorithmic Approaches to Game-theoretical Modeling and Simulation
This paper deals with a methodology of computer modeling and simulation of market competitive situations using game theory. The situations are thematically focused mostly to models of commodity markets but the applications of the methodology can be wider. This methodology covers the whole modeling work, including a primary specification of a problem, making an abstract model, making a simulation model, design of a state space of the problem and the simulator itself. As a whole, the methodology represents a complete framework for implementation of computer models of commodity markets suitable for their further analysis and prediction of their future evolution. The main contribution of the paper consists in the algorithmic implementation of computer processing of large strategic game.Market models, non-cooperative game theory, modeling and simulation, artificial intelligence
Robot graphic simulation testbed
The objective of this research was twofold. First, the basic capabilities of ROBOSIM (graphical simulation system) were improved and extended by taking advantage of advanced graphic workstation technology and artificial intelligence programming techniques. Second, the scope of the graphic simulation testbed was extended to include general problems of Space Station automation. Hardware support for 3-D graphics and high processing performance make high resolution solid modeling, collision detection, and simulation of structural dynamics computationally feasible. The Space Station is a complex system with many interacting subsystems. Design and testing of automation concepts demand modeling of the affected processes, their interactions, and that of the proposed control systems. The automation testbed was designed to facilitate studies in Space Station automation concepts
An AI approach for scheduling space-station payloads at Kennedy Space Center
The Payload Processing for Space-Station Operations (PHITS) is a prototype modeling tool capable of addressing many Space Station related concerns. The system's object oriented design approach coupled with a powerful user interface provide the user with capabilities to easily define and model many applications. PHITS differs from many artificial intelligence based systems in that it couples scheduling and goal-directed simulation to ensure that on-orbit requirement dates are satisfied
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
Sparse Nested Markov models with Log-linear Parameters
Hidden variables are ubiquitous in practical data analysis, and therefore
modeling marginal densities and doing inference with the resulting models is an
important problem in statistics, machine learning, and causal inference.
Recently, a new type of graphical model, called the nested Markov model, was
developed which captures equality constraints found in marginals of directed
acyclic graph (DAG) models. Some of these constraints, such as the so called
`Verma constraint', strictly generalize conditional independence. To make
modeling and inference with nested Markov models practical, it is necessary to
limit the number of parameters in the model, while still correctly capturing
the constraints in the marginal of a DAG model. Placing such limits is similar
in spirit to sparsity methods for undirected graphical models, and regression
models. In this paper, we give a log-linear parameterization which allows
sparse modeling with nested Markov models. We illustrate the advantages of this
parameterization with a simulation study.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation
This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation– optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computa- tions, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems
Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation
This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation– optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computa- tions, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems
The 1988 Goddard Conference on Space Applications of Artificial Intelligence
This publication comprises the papers presented at the 1988 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland on May 24, 1988. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in these proceedings fall into the following areas: mission operations support, planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; modeling and simulation; and development tools/methodologies
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