An Introduction to MultiAgent Systems (2nd Edition) by Michael Wooldridge

By Michael Wooldridge

The eagerly expected up-to-date source on some of the most very important components of study and improvement: multi-agent systems
Multi-agent platforms enable many clever brokers to engage with one another, and this box of analysis has complex at a quick velocity because the ebook of the 1st variation of this publication, which was once approximately a decade in the past. With this fascinating new version, the insurance of multi-agents is totally up to date to incorporate a number of components that experience come to prominence within the final numerous years, together with auctions, computational social selection, and markov selection approaches. In flip, a number of subject matters that have been before everything thought of severe have diminished in value, so the assurance of that material is lowered with this new version. the results of this redefined stability of insurance is a well timed and crucial source on a well-liked subject.

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Extra info for An Introduction to MultiAgent Systems (2nd Edition)

Sample text

Intelligent agents are able to exhibit goal-directed behaviour by taking the initiative in order to satisfy their design objectives. Social ability. Intelligent agents are capable of interacting with other agents (and possibly humans) in order to satisfy their design objectives. These properties are more demanding than they might at first appear. To see why, let us consider them in turn. First, consider proactiveness: goal-directed behaviour. It is not hard to build a system that exhibits goal-directed behaviour we do it every time we write a procedure in Pascal, a function in C, or a method in Java.

Deterministic versus non-deterministic. A deterministic environment is one in which any action has a single guaranteed effect - there is no uncertainty about the state that will result from performing an action. Static versus dynamic. A static environment is one that can be assumed to remain unchanged except by the performance of actions by the agent. In contrast, a dynamic environment is one that has other processes operating on it, and which hence changes in ways beyond the agent's control. The physical world is a highly dynamic environment, as is the Internet.

There are several ways in which such a performance measure can be defined. The first is to associate utilities with states of the environment. Utiliw functions A utility is a numeric value representing how 'good' the state is: the higher the utility, the better. The task of the agent is then to bring about states that maximize utility - we do not specify to the agent how this is to be done. In this approach, a task specification would simply be a function which associates a real value with every environment state.

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