Technical approach and architecture--论文代写范文精选

这样的工具有时更适合建模文书形式处理,比分析设计和计划任务。一般来说,对于社会的概念在模拟组织,一个工具模型方面的社会行为，协调定义为合作但应用常规管理的角度。下面的essay代写范文进行详述。

Multiagent simulations of work
Several simulation tools enable modeling organizational command and coordination policies in a geographically distributed environment, in a way that broadly fits a model of practice. For example, Cohen, Greenberg, Hart, and Howe's (1989) Phoenix simulation models coordination of fire-fighting teams. Hayes-Roth, Brownston, and Sincoff's (1995) simulations allow for improvisation in games played by the agents. Tambe, Johnson, Jones, Laird, Rosenbloom, and Schwamb (1995) describe a tool that models social interactions such as briefing sessions before military missions.

As we have indicated, these tools provide an overall framework for developing a model of practice. In particular, models of business "enterprises" represent the complex coordination between agents playing different roles. For example, Levitt, et al.'s (1995) VDT models both inefficient behavior as well as idealized "intelligent" behavior. Burstein, Ferguson, and Abrett (1993) describe a tool for designing group coordination strategies for efficiency at peak loads, using "coordination structures" as templates for creating models, such as a template for an administrator who supervises agents who are clones. Dozens of programs focus specifically on modeling animal, computational, and even early human societies (e.g., see Gustavsson, 1993; Gilbert and Doran, 1993).

Such tools are sometimes more suitable for modeling clerical forms processing (in which kinds of information used and roles are relatively stable), than for analyzing design and planning tasks (in which the information and team are dynamically constructed).

In general, the notion of "social" in simulations of organizations is quite impoverished. For example, one tool models social behaviors in terms of "decreasing information-processing capability" (e.g., emotional responses) and deceit (Carley, Park, and Prietula, 1993, p. 3). Malone and Crowston (1991) define coordination as the "act of working together" but apply the conventional management perspective. Their models do not capture practice, but instead descriptively abstract coordination in terms of bidding and communicating interdependencies. Indeed, they view coordination as "the additional information processing performed when multiple, connected actors pursue goals that a single actor pursuing the same goals would not perform." That is, coordination is the overhead required when you can't do everything yourself!

Representation language details
The most central representational unit in Brahms is called a workframe (Figure 3), a situation-action rule consisting of preconditions (what the agent must believe to be true), actions, detectables (what facts in the world might be noticed, with what probability and when during the actions), and consequences (changes to the world or this agent's beliefs that result). The example shown is a workframe for Field Supervisors. It is at the "top level," which means that it is always potentially activated by members of this group. The workframe is activated after 8 AM; the effect is that the Field Supervisor will notice every service technician in the room and engage in "Morning Planning," an activity.

Workframes are organized hierarchically into activities (e.g., Morning Planning with STs is an activity with one workframe, shown in Figure 4). Actions in a workframe may be simple (just indicating a name, duration, and priority) or composite (another activity). Figure 4 indicates that during the Morning Planning activity, the Field Supervisor engages in face-to-face conversation with service technicians. For each service tech and order that needs to be discussed, the Field Supervisor will tell the service tech that he or she is assigned to an order.

Simple actions also include movement to another location. Consequences and actions are ordered and interleaved. Detectables may be indicated as "impasses" that interrupt the workframe or as "end conditions" that end the workframe or its encompassing activity. The detectable in Figure 3 simply observes facts in the environment.

Workframes are inherited by agents from all groups to which they belong; groups may belong to other groups (Figure 5). Priorities allow workframes to interrupt each other or carry out specific aspects of a more general protocol. For example, workframes at the "all groups" (top) level specify how to use a telephone and have face-to-face conversations; these have intermediate priority. Workframes that trigger conversations are most specific and have the lowest priority. Workframes that specify what to say during certain kinds of conversations have the highest priority. By this simple scheme, it is possible for one agent to initiate a conversation and for the responder to "remember" something he wanted to tell the first agent when he called; thus a give and take may ensue.

RESULTS
Two detailed, connected models have been created: front-end order processing in the NYNEX BNA Center and the back-end circuit wiring and testing of the technicians, splicers, etc. The back-end ("turf coordinator") model was created originally as part of specifying requirements for Brahms. The problem was chosen because testing a circuit occurs when three agents are in a conference call at three locations--a form of synchronization that people find demanding and that we knew would challenge our modeling skills. Analyzing existing Sparks models enabled us over the course of several years to articulate the difference between task and activity models (Clancey, 1997b) and how a workflow diagrams could be generated automatically. Building this model also revealed how planning occurs on the job (the coffee meeting example). We deliberately modeled as much at the "all-group" level as possible (Figure 5), to make the model adaptable for other settings; components such as the phone and fax machine models are directly reusable.
The front-end model was begun in 1996 and was intended to help managers and software engineers understand why orders rejected by an on-line system were generated and resolved. Workers collaborating with us found the modeling process to be valuable. Specifically, the focus on what people actually did when they processed orders revealed that the program's rejects, called "errors" heretofore, were not necessarily human mistakes, but just orders that the software could not process. Our systemic approach led backwards from this downstream processing (in another part of Manhattan) back to the BNA Center and to its peer at World Trade Center. The actual causes of computer system problems were found to be not just typos, but primarily an inability to specify certain kinds of jobs using existing forms. Assumptions built into software also ignored pragmatic issues, such as the need to start order processing before getting customer credit approval. We also showed that the error rate dropped not because of "fewer mistakes," but because the work group shifted to a fully manual process that worked around the limitations of the order-processing that was supposed to partially automate circuit design. This analysis raised each group's awareness of the other's work and gave the software engineer responsible for the downstream system a better appreciation of the difficulties the BNA Center encountered and appropriately handled.

Finally, in the back-end turf coordinator model we had treated all members of a work group as being clones, such that all SETs behave identically. This choice followed from the social science preference not to view people as individuals, but to focus on trends and commonalities. In building this second model we questioned this simplification and asked how the individuals in a group differed from one another. Our analysis of BNA engineers revealed a kind of knowledge variability that was unexpected--people were of course not clones, but differences were not errors, either. We found alternative methods being used for the same task (such as verifying that a given circuit was available for assignment); we conjecture that such differences are a vital source for learning. Possibly the lack of consideration of individual differences heretofore by the social scientists led them to emphasize cross-functional learning (one group learning to do another's tasks), rather than learning among people with similar responsibilities. Furthermore, the idea of legitimate knowledge variability contrasts with the typical corporate view that all variability in job performance is non-optimal or based on misconceptions or lack of knowledge (hence, one goal of corporate training is standardization). Instead, knowledge variability may be a source of robustness, allowing adaptability when the environment changes (like variability in a biological population).

Given information about the location of different service technicians and knowing that the turf coordinator had last read the order database that morning and wouldn't review it again until the following morning, a program might offer advice such as, "TC Allen, ST Aronson just completed the job down on Wall Street and is now available; perhaps you want to have her go over to Broadway to handle the Teleport job?" More broadly, activity-based modeling provides a new way of modeling users (such as the turf coordinator), which includes not only what tasks they do and the information they use, plus some of the deductions they might make, but also where and when they do such reasoning, where they might be found at a particular moment, who might know where they are located, what interests them at a particular time of day, etc. 

Thus a Brahms user model would combine cognitive and social-interactional considerations. Similarly, such a model would be potentially more useful for instruction than a typical knowledge-based model because it would help a student understand the practices by which different tools are related, who is typically available for providing help, what kind of assistance may be sought, and so on. Finally, one could use Brahms for implementing a software agent itself, locating the agent in the modeled social-interactional context, making explicit what external resources are available to it, how it should behave when participating in different groups, what it should do at different times of the day, and so on. In summary, activity-based modeling provides a way to inform computer systems of everyday practice of the people they are serving, and thus, in a very limited way, integrate them into human communities.

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