July 16, 2017

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By J.C. Catford

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They are only the first stage of a complete research process [9]. Furthermore, decentralised agents can exhibit other phenomena such as hyperactivity [10] and diversity [11] and their designers must address the real-world costs of messaging [12]. Experiments with MAS implementations of a genetic algorithm have yielded unexpected results that were partly due to the cost of messaging [13]. Equally unexpected results may be uncovered with asynchronous and parallel implementations of other nature-inspired algorithms.

This positive reinforcement builds efficient trails to food sources in a decentralised fashion. The Ant Colony System algorithm adapts this process to a graph structure. Figure 1 gives the pseudocode for the algorithm. Initially, every edge in the problem is given the same pheromone level τ0 . A number of ants m are randomly assigned to their starting cities such that no more than one ant occupies any city. The following main loop then proceeds for a maximum of tmax iterations where t is the count of the current iteration.

Directory Facilitators as Place Agents The FIPA Agent Management Specification [22] provides for so-called Directory Facilitator (DF) agents. These offer a ‘yellow pages’ service by allowing other agents to advertise their particular service(s) with the DF. Other agents can then query a DF about its advertised services and subscribe for notification about the appearance of new services. We can use a collection of DF agents to build a decentralised pheromone infrastructure of Place agents. Each Place (DF) agent offers its own service description as a Place agent.

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