Information Technology Reference
In-Depth Information
6
Discussion and Conclusions
In the EASI/EASS models, the environment offers a technical support for the commu-
nication and the activation in the simulation. Its processing is the result of the filters
triggering according to the MAS descriptions. In that way, the agents add or remove
dynamically filters and update their description. Thanks to the filter triggering, an agent
receives a message or is activated in a specific context. The processing of the messages
and the action execution remain to the agents responsibility.
This paper is focused on the cost of the centralization, which is closely related to
the cost of the update process. Organizations, such as the agent-group-role model [5]
enable to decrease the cost but do not take into account ambient criteria like the location
of the agents (e.g. in the simulation). Let us note that our modeling enables to reproduce
a selection of percepts according to the organisation. Institutions (see e.g. [4]) gener-
ally do not share the same objective. The focus is on control and not on the filtering /
matching process. However, the technical solutions may be close to ours.
The use of a shared knowledge has already been done within agents (for example
Sycara's work, e.g. [14]) but in that case the update of the properties is not considered.
In this paper, we focus on the cost of the update process that could limit the interest to
centralize specific information. Furthermore, middle-agents are generally used only as
a first step to find contacts, and not to manage all the communications. In our view, the
environment is a facility, which can be used to facilitate the interaction, apply norms,
or verify some rules related to the application design. These roles do not belong to the
same design level as the agents.
Theoretically, we have shown that if the communication takes the context into ac-
count, there is no strictly dominant solution. It depends on the dynamicity of the multi-
agent system, the number of agents and the average percentage of agents interested in
each message. According to the number of tests criteria, we have shown that the envi-
ronment is always better than the local context computation. According to the number
of messages criteria, the result has to take into account the number of messages related
to the MAS activity and the number of messages related to the update process. We have
shown that the environment solution is generally better to mediate the communication
of the MAS activity and that few messages to mediate are needed to compensate the
cost of the update process.
To propose an empirical assessment of the cost of the environment, we have studied
the run-time criterion in the crisis simulation example. We compare the cost of the local
context analysis for each agent to a central and global control ensured by the environ-
ment, and the cost of communication. The main conclusion is that the environment cost
is significantly lower than the local agent calculation of the context perception, except
when there are very few agents.
In the future, we intend to investigate different ways to improve the environment
performance. An ongoing effort concerns the theoretical evaluation of a RETE-based
instantiation of the model. We also study how to take advantage of the filter and entity
structures to speed up the matching process.The clustering of the agents in several envi-
ronments is a perspective to improve the matching process. However, moving the agents
from one environment to another dynamically according to their interaction needs is
costly and therefore has to be taken into account.
 
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