List describe hardware software components computer

->>>> Click Here to Download <<<<<<<-

This converts the AC mains supply from the wall socket and supplies the correct DC voltages to all the components inside the computer. You get different wattage ratings for power supplies. The higher the wattage, the higher the electrical current that can be made available to parts that need it. A power supply also comes with its own cooling fan. This helps all the internal components to stay cool when the power supply is subjected to bigger loads.

If you would like to know more about a power supply and its lifespan , I have written an article discussing it more in-depth. There are various types of monitors on the market. There are also a variety of different sizes with different aspect ratios. The aspect ratio is simply the ratio between height and width. Monitors also have a fast response time to keep up with the high demands required to eliminate delays with user input for gaming. A keyboard is one of the ways to communicate with a computer.

Typing a key from the keyboard sends a small portion of data to tell the computer which key was pressed. The computer can use this information in many ways.

An example could be a command or a character that can be used in a document. There are two main different types of keyboards. Mechanical and membrane types. A mouse allows the user to move a pointer displayed on the monitor and experience a more intuitive interaction with the computer. However, the three main buttons allow the user to select, grab, scroll and access extra menus and options. It does this by using the data from the computer, and by either using toner or ink, it deposits one of these in a controlled and accurate manner to form the image.

That covers the basic components of a computer. All of these parts play a vital function for a computer to work. From here, I recommend that you go and read about knowing which computer parts are compatible with each other. Skip to content Going over the parts of a computer and their functions will help you understand all the vital components that make up a computer. Parts of a computer with their functions Here is a complete list of all the common computer hardware components and common peripherals used with them.

The computer case. Toggle Menu Close. The keyboard can be wired or wireless. The keyboard contains, alphabets, numbers, special characters and other buttons to give input to the computer. It is the input device that takes input to the user and processes the commands. The printer is a type of hardware that is used to print something which is seen on the computer and then transfer that displayed information to paper. The printers can be differentiated based on size, processing speed, and other factors.

There are many types of hardware devices present in the market. Choosing the right hardware device with the correct specification gives the best performance result.

The hardware devices vary in size, specification and it should be chosen as per the compatibility of the computer system. Different type of hardware device has a different role. And a complete set of hardware devices makes an effective computer system. This is a guide to Types of Computer Hardware. Here we discuss the basic concept with 7 different types of computer hardware along with respective advantages. You can also go through our other suggested articles to learn more—.

Submit Next Question. By signing up, you agree to our Terms of Use and Privacy Policy. Forgot Password? This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy.

By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy. Popular Course in this category. The next layer of navigation uses the metaphor of a virtual vehicle, which allows movement over distances in the VE greater than those distances allowed by the head-tracker alone. The position and orientation of the virtual vehicle can be controlled in a variety of ways. In simulation applications, the vehicle is controlled in the same way that an actual simulated vehicle would be controlled.

Examples that have been implemented are treadmills and bicycles and joysticks for flight or vehicle simulators. For more abstract applications, there have been several experimental approaches to controlling the vehicle. The most common is the point and fly technique, wherein the vehicle is controlled via a direct manipulation interface. The user points a three-dimensional position and orientation tracker in the desired direction of flight and commands the environment to fly the user vehicle in that direction.

Other methods of controlling the vehicle are based on the observation that in VE one need not get from here to there through the intervening space. Teleoperation is one obvious example, which often has the user specify a desired destination and then "teleports" the user there.

Solutions have included portals that have fixed entry and exit locations, explicit specification of destination through numerical or label input, and the use of small three-dimensional maps of the environment to point at the desired destination. Another method of controlling the vehicle is dynamic scaling, wherein the entire environment is scaled down so that the user can reach the desired destination, and then scaled up again around the destination indicated by the user.

All of these methods have disadvantages, including difficulty of control and orientation problems. There is a hierarchy of objects in the VE that may behave differently during navigation. Some objects are fixed in the environment and are acted on by both the user and the vehicle. Other objects, usually virtual. Still other objects, such as data displays, are always desired within the user's field of view and are not acted on by either the user or the vehicle.

These objects have been called variously world stable, vehicle stable , and head stable Fisher et al. Although most of the fundamental mathematics of navigation software are known, experimentation remains to be done.

Hierarchical data structures for the minimization of polygon flow to the graphics pipeline are the back end of visual scene navigation. When we have generated a matrix representing the chosen view, we then need to send the scene description transformed by that matrix to the visual display. One key method to get the visual scene updated in real time at interactive update rates is to minimize the total number of polygons sent to the graphics pipeline. Hierarchical data structures for polygon flow minimization are probably the least well understood aspect of graphics development.

This is a very common misconception. Visual reality has been said to consist of 80 million polygons per picture Catmull et al. The alternatives are to live with worlds of reduced complexity or to off-load some of the graphics work done in the pipeline onto the multiple CPUs of workstations.

All polygon reduction must be accomplished in less time than it takes just to send the polygons through the pipeline. The difficulty of polygon flow minimization depends on the composition of the virtual world. This problem has historically been approached on an application-specific basis, and there is as yet no general solution. Current solutions usually involve partitioning the polygon-defined world into volumes that can readily be checked for visibility by the virtual world.

There are many partitioning schemes—some of which work only if the world description does not change dynamically Airey et al. A second component of the polygon flow minimization effort is the pixel coverage of the object modeled. Once an object has been determined to be in view, the secondary question is how many pixels that object will cover.

If the number of pixels covered by an object is small, then a reduced polygon count low-resolution version of that object can be rendered. This results in additional software complexity, again software that must run in real time. Because the level-of-detail models are precomputed, the issue is greater dataset size rather than level selection which is nearly trivial.

The current speed of z-buffers alone means we must carefully limit the polygons sent through the graphics pipeline. Other techniques that use the CPUs to minimize polygon flow to the pipeline are known for specific applications, but those techniques do not solve the problem in general. In a classic paper, Clark presents a general approach for solving the polygon flow minimization problem by stressing the construction of a hierarchical data structure for the virtual world Figure The approach is to envision a world database for which a bounding volume is known for each drawn object.

The bounding volumes are organized hierarchically, in a tree that is used to rapidly discard large numbers of polygons. This is accomplished by testing the bounding volumes to determine whether they are contained or partially contained in the current orientation of the view volume. The process continues recursively until a node is reached for which nothing underneath it is in the view volume. This part of the Clark paper provides a good start for anyone building a three-dimensional VE for which the total number of polygons is significantly larger than the hardware is capable of drawing.

The second part of Clark's paper deals with the actual display of the polygons in the leaf nodes of the tree. The idea is to send only minimal descriptions of objects through the graphics pipeline minimal based on the expected final pixel coverage of the object. In this approach, there will be multiple-resolution versions of each three-dimensional object and software for rapidly determining which resolution to draw. The assumption of multiple-resolution versions of each three-dimensional object being available is a large one, with automatic methods for their generation remaining an open issue.

Other discussions of this issue are found in DeHaemer and Zyda , Schroeder et al. Polygon flow minimization to the graphics pipeline is best understood by looking at specific solutions. Some of the more interesting work has been done by Brooks at the University of North Carolina at Chapel Hill with respect to architectural walkthrough Brooks, ; Airey et al.

The goal in those systems was to provide an interactive walkthrough capability for a planned new computer science building at the university that would offer visualization of the internal spaces of that building for the consideration of changes before construction. The walkthrough system had some basic tenets. The first was that the architectural model would be constructed by an architect and passed on to the walkthrough phase in a fixed form. A display compiler would then be run on that database and a set of hierarchical data structures would be output to a file.

The idea behind the display compiler was that the building model was fixed and that it was acceptable to spend some 45 minutes in computing a set of hierarchical data structures.

Once the data structures were computed, a display loop could then be entered, in which the viewpoint could be rapidly changed. The walkthrough system was rather successful, but it has the limitation that the world cannot be changed without rerunning the display compiler. Other walkthrough systems have similar limitations Teller and Sequin, ; Funkhouser et al. Real-time display of three-dimensional terrain is a well-researched area that originated in flight simulation.

Terrain displays are an interesting special case of the polygon flow minimization problem in that they are relatively well worked out and documented in the open literature Zyda et al. The basic idea is to take the terrain grid and generate a quadtree structure containing the terrain at various display resolutions. The notion of the grid cell is used for reducing polygon flow by drawing. This strategy works well for ground-based visual displays; a more comprehensive algorithm is required for air views of such displays.

Models that define the form, behavior, and appearance of objects are the core of any VE. A host of modeling problems are therefore central to the development of VE technology. An important technological challenge of multimodal VEs is to design and develop object representation, simulation, and rendering RSR techniques that support visual, haptic, and auditory interactions with the VE in real time.

There are two major approaches to the RSR process. First, a unified central representation may be employed that captures all the geometric, surface, and physical properties needed for physical simulation and rendering purposes. In principle, methods such as finite element modeling could be used as the basis for representing these properties and for physical simulation and rendering purposes. At the other extreme, separate, spatially and temporally coupled representations could be maintained that represent only those properties of an object relevant for simulating and rendering interactions in a single modality e.

The former approach is architecturally the most elegant and avoids issues of maintaining proper spatial and temporal correlation between the RSR processes for each modality. Practically, however, the latter approach may allow better matching between modality-specific representation, simulation, and rendering streams. The abilities and limitations of the human user and the VE system for each of the modalities impose unique spatial e.

For example, it is likely that the level of detail and consequently the nature of approximations in the RSR process will be different for each of the modalities. It is unclear, therefore, whether these modality-specific constraints can be met by systems based on a single essential or core representation and still operate in real time.

The overwhelming majority of VE-relevant RSR research and development to date has been on systems that are visually rendered e. The theoretical and practical issues associated with either of the two RSR approaches or variants in a multimodal context, however, have received minimal attention but are likely to become a major concern for VE system researchers and developers.

For example, geometric modeling is relevant to the generation of acoustic environments i. Novel applications such as the use of auditory displays for the understanding of scientific data e. In this section, we concentrate on the visual domain and examine the problems of constructing geometric models, the prospects for vision-based acquisition of real-world models, dynamic model matching for augmented reality, the simulation of basic physical behavior, and simulation of autonomous agents.

Parallel issues are involved in the other modalities and are discussed in Chapters 3 and 4. The need to build detailed three-dimensional geometric models arises in computer-aided design CAD , in mainstream computer graphics, and in various other fields. Geometric modeling is an active area of academic and industrial research in its own right, and a wide range of commercial modeling systems is available.

Despite the wealth of available tools, modeling is generally regarded as an onerous task. Among the many factors contributing to this perception are sluggish performance, awkward user interfaces, inflexibility, and the low level at which models must typically be specified.

It is symptomatic of these difficulties that most leading academic labs, and many commercial animation houses such as Pixar and PDI , prefer to use in-house tools, or in some cases a patchwork of homegrown and commercial products.

From the standpoint of VE construction, geometric modeling is a vital enabling technology whose limitations may impede progress. As a practical matter, the VE research community will benefit from a shared open modeling environment, a modeling environment that includes physics.

In order to understand this, we need to look at how three-dimensional geometric models are currently acquired. We do this by looking at how several VE efforts have reported their model acquisition process. If one reads the work done for the walkthrough project at the University of North Carolina Airey et al.

In the presentation of that paper, one of the problems discussed was that of ''getting the required data out of a CAD program written for other purposes. In particular, data related to the actual physics of the building were not present, and partitioning information useful to the real-time. RB2 is a software development platform for designing and implementing real-time VEs.

Development under RB2 is rapid and interactive, with behavior constraints and interactions that can be edited in real time.

RB2 has a considerable following in organizations that do not have sufficient resources to develop their own in-house VE expertise. RB2 is a turnkey system, whose geometric and physics file formats are proprietary. As a result, project researchers have developed an open format for storing these three-dimensional models Zyda et al. Computer-aided design systems with retrofitted physics are beginning to be developed e. Many applications call for VEs that are replicas of real ones. Rather than building such models by hand, it is advantageous to use visual or other sensors to acquire them automatically.

Automatic acquisition of complex environment models such as factory environments is currently not practical but is a timely research issue. Meanwhile, automatic or nearly automatic acquisition of geometric models is practical now in some cases, and partially automated interactive acquisition should be feasible in the near term Ohya et al.

The most promising short-term approaches involve active sensing techniques. Scanning laser finders and light-stripe methods are both capable of producing range images that encode position and shape of surfaces that are visible from the point of measurement.

These active techniques offer the strong advantage that three-dimensional measurements may be made directly, without the indirect inferences that passively acquired images require. Active techniques do, however, suffer from some. Surfaces that are nonreflective or obliquely viewed shiny surfaces may not return enough light to allow range measurements to be made. Noise is enough of a problem that data must generally be cleaned up by hand.

A more basic problem is that a single range image contains information only about surfaces that were visible from a particular viewpoint. To build a complete map of an environment, many such views may be required, and the problem of combining them into a coherent whole is still unsolved.

Among passive techniques, stereoscopic and motion-based methods, relying on images taken from varying viewpoints, are currently most practical. However, unlike active sensing methods, these rely on point-to-point matching of images in order to recover distance by triangulation. Many stereo algorithms have been developed, but none is yet robust enough to compete with active methods.

Methods that rely on information gleaned from static monocular views—edges, shading, texture, etc. For many purposes, far more is required of an environment model than just a map of objects' surface geometry.

If the user is to interact with the environment by picking things up and manipulating them, information about objects' structure, composition, attachment to other objects, and behavior is also needed. Unfortunately, current vision techniques do not even begin to address these deeper issues. The term augmented reality has come to refer to the use of transparent head-mounted displays that superimpose synthetic elements on a view of the real surroundings.

Unlike conventional heads-up displays in which the added elements bear no direct relation to the background, the synthetic objects in augmented reality are supposed to appear as part of the real environment.

That is, as nearly as possible, they should interact with the observer and with real objects, as if they too were real.

At one extreme, creating a full augmented-reality illusion requires a complete model of the real environment as well as the synthetic elements. For instance, to place a synthetic object on a real table and make it appear to stay on the table as the observer moves through the environment, we would need to know just where the table sits in space and how the observer is moving.

For full realism, enough information about scene illumination and surface properties to cast synthetic shadows onto real objects would be needed. Furthermore, we would need enough information about three-dimensional scene structure to allow real objects to hide or be hidden by synthetic ones, as appropriate. Naturally, all of this would. This sort of mix of the real and synthetic has already been achieved in motion picture special effects, most notably, Industrial Light and Magic's effects in films such as The Abyss and Terminator 2.

Some of these effects were produced by rendering three-dimensional models and creating a composite of the resulting images with live-action frames, as would be required in augmented reality. However, the process was extremely slow and laborious, requiring manual intervention at every step. After scenes were shot, models of camera and object motions were extracted manually, using frame-by-frame manual measurement along with considerable trial and error.

Even small geometric errors were prone to destroy the illusion, making the synthetic objects appear to float outside the live scene. Automatic generation of augmented-reality effects is still a research problem in all but the least demanding cases.

The two major issues are: 1 accurate measurement of observer motions and 2 acquisition and maintenance of scene models. The prospects for automatic solutions to the latter were discussed above. If the environment is to remain static, it would be feasible to build scene models off-line using interactive techniques. Although VE displays provide direct motion measurements of observer movement, these are unlikely to be accurate enough to support high-quality augmented reality, at least when real and synthetic objects are in close proximity, because even very small errors could induce perceptible relative motions, disrupting the illusion.

Perhaps the most promising course would use direct motion measurements for gross positioning, using local image-based matching methods to lock real and synthetic elements together.

In order to give solidity to VEs and situate the user firmly in them, virtual objects, including the user's image, need to behave like real ones. At a minimum, solid objects should not pass through each other, and things should move as expected when pushed, pulled, or grasped. Analysis of objects' behavior at the scale of everyday observation lies in the domain of classic mechanics, which is a mature discipline.

However, mechanics texts and courses are generally geared toward providing insight into objects' behavior, whereas to support VE the behavior itself is of paramount importance—insight strictly optional.

Thus classic treatments may provide the required mathematical underpinnings but do not directly address the problem at hand. Simulations of classic mechanics are extensively used as aids in engineering design and analysis. Although these traditional simulations do. In engineering practice, simulation is a long, drawn-out, and highly intellectualized activity.

The engineer typically spends much time with pencil and paper developing mathematical models for the system under study. These are then transferred to the simulation software, often with much tweaking, and parameter selection.

Only then can the simulation actually be run. As a design evolves, the initial equations must be modified and reentered and the simulation rerun.

In strong contrast, a mechanical simulation for VEs must run reliably, seamlessly, automatically, and in real time. Within the scope of the world being modeled, any situation that could possibly arise must be handled correctly, without missing a beat.

In the last few years, researchers in computer graphics have begun to address the unique challenges posed by this kind of simulation, under the heading of physically based modeling. Below we summarize the main existing technology and outstanding issues in this area.

Solid Object Modeling Solid objects' inability to pass through each other is an aspect of the physical world that we depend on constantly in everyday life: when we place a cup on a table, we expect it to rest stably on the table, not float above or pass through it.

In reaching and grasping, we rely on solid hand-object contact as an aid as do roboticists, who make extensive use of force control and compliant motion. Of course, we also rely on contact with the ground to stand and locomote. The problem of preventing interpenetration has three main parts. First, collisions must be detected. Second, objects' velocities must be adjusted in response to collisions.

Finally, if the collision response does not cause the objects to separate immediately, contact forces must be calculated and applied until separation finally occurs. Collision detection is most frequently handled by checking for object overlaps each time position is updated. If overlap is found, a collision is signaled, the state of the system is backed up to the moment of collision, and a collision response is computed and applied.

The bulk of the work lies in the geometric problem of determining whether any pair of objects overlap. This problem has received attention in robotics, in mechanical CAD, and in computer graphics.

Brute force overlap detection for convex polyhedra is a straightforward matter of testing each vertex of every object against each face of every other object. More efficient schemes use bounding volumes or spatial subdivision to avoid as many tests as possible.

Good general methods for objects with curved surfaces do not yet exist. This is not merely an esoteric concern, because it means that rapidly moving objects, e.

Needless to say, large errors can result. Guaranteed methods have been described by Lin and Canny for the case of convex polyhedra with constant linear and angular velocity. Collision response involves the application of an impulse and producing an instantaneous change in velocity that prevents interpenetration. The basics of collision response are well treated in classic mechanics and do not pose any great difficulties for implementation.

Problems do arise in developing accurate collision models for particular materials, but many VE applications will not require this degree of realism. To handle continuous multibody contact, it is necessary to calculate the constraint forces that are exchanged at the points of contact and to identify the instants at which contacts are broken.

Determining which contacts are breaking is a particularly difficult problem, turning out, as shown by Baraff, to require combinatorial search Baraff and Witkin, ; Baraff, Fortunately, Baraff also developed reasonably efficient methods that work well in practice.

Many virtual world systems exhibit rigid body motion with collision detection and response Hahn, ; Moore and Wilhelms, ; Baraff, ; Baraff and Witkin, ; Zyda et al. Baraff's system also handles multibody continuous contact and frictional forces for curved surfaces. These systems provide many of the essential elements required to support VEs. Constraints and Articulated Objects In addition to simple objects such as rigid bodies, we should be able to handle objects with moving parts—doors that open and close, knobs and switches that turn, etc.

In principle, the ability to simulate simple objects such as rigid bodies, together with the ability to prevent interpenetration, could suffice to model most such compound objects. For instance, a working desk drawer could be constructed by modeling the geometry of a tongue sliding in a groove, or a door by modeling in detail the rigid parts of the hinge.

In practice, it is far more efficient to employ direct geometric constraints to summarize the effects of this kind of detailed interaction. For instance, a sliding tongue and groove would be idealized as a pair of coincident lines, one on each object, and a hinge would be represented as an ideal revolute joint. The simulation and analysis of articulated bodies—jointed assemblies of rigid parts—have been treated extensively, particularly in robotics.

Building on the work of Lathrop, Schroeder demonstrated that it is nevertheless feasible to build a "virtual erector set" based on recursive formulations Schroeder and Zeltzer, Another approach to simulating constrained systems of objects builds on the classic method of Lagrangian multipliers, in which a linear system is solved at each time step to yield a set of constraint forces.

This approach offers several advantages: first, it is general, allowing essentially arbitrary holonomic constraints to be applied to essentially arbitrary not necessarily rigid bodies. Second, it lends itself to on-the-fly construction and modification, an important consideration for VEs. Finally, the constraint matrices that form the linear system are typically sparse, reflecting the fact that everything is not usually connected directly to everything else.

Using numerical methods that exploit this sparsity can yield performance that competes with recursive methods. Witkin et al. Nonrigid Objects A vast body of work treats the use of finite element methods to simulate continuum dynamics. Most of this work is probably of limited relevance to the construction of conventional VEs, simply because such environments will not require fine-grained nonrigid modeling, with the possible exception of virtual surgery.

However, interactive continuum analysis for science and engineering may become an important specialized application of VEs once the computational horsepower is available to support it. Highly simplified models for flexible-body dynamics are presented by Witkin and Welch , by Pentland and Williams , and by Baraff and Witkin The general idea of these models is to use only a few global parameters to represent the shape of the whole object, formulating the dynamic equations in terms of these variables.

These simplified models capture only the gross deformations of the object but in return provide very high performance. They are probably the most appropriate choice for VEs that require simple nonrigid behavior. The general idea is to use simulated flexible materials as a sculpting medium. Flexible thin sheets are employed by Celniker and Gossard and by Welch and Witkin Szeliski and Tonnesen uses clouds of oriented particles to form smooth surfaces.

Motivated by the obvious need in both computer graphics and engineering for realism and physically based environments that support various levels of object detail and interaction depending on the application , Metaxas , ; Metaxas and Terzopoulos, a, b, ; Terzopoulos and Metaxas, developed a general framework for shape and nonrigid motion synthesis, which can also handle rigid bodies as a special case.

The framework features a new class of dynamic deformable part models. These models have both global deformation parameters that represent the gross shape of an object in terms of a few parameters and local deformation parameters that represent an object's details through the use of sophisticated finite element techniques.

Global deformations are defined by fully nonlinear parametric equations. Hence the models are more general than the linearly deformable ones included in Witkin and Welch and quadratically deformable ones included in Pentland and Williams By augmenting the underlying Lagrangian equations' motion with very fast dynamic constraint techniques based on Baumgarte , he adds the capability to compose articulated models Metaxas, , ; Metaxas and Terzopoulos, b from deformable parts, whose special case for rigid objects is the technique used by Barzel and Barr Moreover, Metaxas , also develops fast algorithms for the computation of impact forces that occur during collisions of complex flexible multibody objects with the simulated physical environment.

Issues to be Addressed Most of the essential pieces that are required to imbue VEs with physical behavior have already been demonstrated. Some—notably snap-together constraints and interactive surface modeling—have been demonstrated in fully interactive systems, and others—notably the handling of collision and contact—are only now beginning to appear in interactive systems recent work by David Baraff at Carnegie Mellon University involves an interactive 2.

The most immediate challenge at hand is one of integrating the existing technology into a working system, along with other elements of VE construction software. Many performance-related issues are still to be addressed, for example, doing efficient collision detection in a large-scale environment systems with from to , players or parts and further accelerating constrained dynamics solutions.

In addition, many of the standard. For example, the ratio of compute time to real time can vary by orders of magnitude in the simulation of noninterpenetrating bodies, slowing even further when complex contact situations arise. Maintaining a constant frame rate will require the development of new methods that degrade gracefully in such situations. The need for simulated autonomous agents arises in many VE application areas, such as training, education, and entertainment, in which such agents could play the role of adversaries, trainers, or partners or simply supernumeraries to add richness and believability.

Although fully credible simulated humans are the stuff of science fiction, simple agents will often suffice. The construction of simulated autonomous agents draws on a number of technologies, including robotics, computer animation, artificial intelligence, and optimization. Motion Control Placing an autonomous agent in a virtual physical environment is essentially like placing a robot in a real environment: the agent's body is a physical object that must be controlled to achieve coordinated motion.

Fortunately, controlling a virtual agent is much easier than controlling a real one, since many simplifications and idealizations can be made. For example, the agent can be given access to full and perfect information about the state of the world, and many troubling mechanical effects need not arise.

Closed-loop controllers were used to animate virtual agents by McKenna and Zeltzer and by Miller More recently, Raibert and Hodgkins adapted their controller for a real legged robot to the creation of animation. Rather than hand-crafting controllers, Witkin and Kass solve numerically for optimal goal-directed motion, in an approach that has since been elaborated by Van de Panne et al. Human Figure Simulation In many applications, a VE system must be able to display accurate models of human figures, possibly including a model of the user.

Consider training systems, for example. Out-the-window views generated by high-end flight simulators hardly ever need to include images of human figures.

But there are many situations in which personnel must cooperate and interact with other crew members. Carrier flight deck operations, small squad training or antiterrorist tactics, for example, require precise coordination of the actions of many individuals for safe and successful execution. VE systems to support training,.

We call a computer model of a human figure that can move and function in a VE a virtual actor. If the movement of a virtual actor is slaved to the motions of a human using cameras, instrumented clothing, or some other means of body tracking, we call that a guided virtual actor , or simply, a guided actor.

Autonomous actors operate under program control and are capable of independent and adaptive behavior, such that they are capable of interacting with human participants in the VE, as well as with simulated objects and events. In addition to responding to the typed or spoken utterances of human participants, a virtual actor should be capable of interpreting simple task protocols that describe, for example, maintenance and repair operations.

Given a set of one or more motor goals—e. Beyond the added realism that the presence of virtual actors can provide in those situations in which the participants would normally expect to see other human figures, autonomous actors can perform two important functions in VE applications.

First, autonomous actors can augment or replace human participants. This will allow individuals to work or train in group settings without requiring additional personnel. Second, autonomous actors can serve as surrogate instructors. VE systems for training, education, and operations rehearsal will incorporate various instructional features, including knowledge-based systems for intelligent computer-aided instruction ICAI Ford, The required degree of autonomy and realism of simulated human figures will vary, of course, from application to application.