An Introduction to Numerical Analysis - Solutions by Suli E., Mayers D.

By Suli E., Mayers D.

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We believe that modeling an agent environment as a marketplace, where agents are motivated by economic incentives to provide goods and services to each other, is a practical and feasible approach. It is possible in the marketplace that some sellers may try to “cheat” by delivering some good with reasonable quality, followed by low quality goods. Buyers in our approach use reputation as a means to protect themselves from those dishonest sellers: They each dynamically maintain a set of reputable sellers and consider choosing suitable sellers from this set first.

As discussed in detail later, we believe that the proposed algorithm will result in improved performance for buyers, better satisfaction for both buyers and sellers, reduced communication load, and more robust systems. The paper is organized as follows: The next section, section 2, introduces our proposed learning algorithm for buyers and sellers, respectively. Section 3 discusses the possible advantages of the proposed algorithm. Section 4 remarks on related work. Section 5 provides some future research directions.

Figure 1 presents a diagram of the specialists (S) migrating to the remote host system from AERO as a result of the consultant (C) soliciting services from these specialists. Fig. 1. AERO agents working together 1 One way of dispatching a specialist is to clone the original specialist agent and then send the clone to the remote system. Another way may be to explicitly request the specialist to create a functionally-equivalent agent. 1 David Chen and Robin Cohen Host to AERO Processing Algorithm The steps below demonstrate a host system calling AERO and enlisting the exception handling service.

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