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called
contexts
. A context is defined as a set of information that can be used to character-
ize a situation. This set of information includes percepts and messages that are obtained
by the agent and its own state. Thanks to the context computation, the agent decides to
execute the suitable action. The relation between the environment and the agents based
on this perception - decision - action cycle does not exploit the potential of the envi-
ronment that remains a static entity. This relation can be enhanced by delegating the
perception process to the environment [1,8,13,17]. The objective is to support advanced
features that are difficult to obtain without the environment.
We have proposed an environment modeling and an environment framework that are
based on the delegation of the perception process to the environment. Our proposition
supports two advanced features: 1) Multi-Party Communications (MPC) [3] takes into
account dyadic interaction (one to one), group interaction (one to many) and overhear-
ing (many to one/many) within the same interaction process; 2) Contextual activation
[9] applied to the simulation process consists in activating the agents directly according
to their context. In our proposition, despite the delegation process to the environment,
the agents keep their autonomy because the decision process and the action process re-
main in the agents. The perception phase of the cycle is modified to take into account
the relation with the environment. That means that the environment computes the con-
texts for the agents, and that the agents can modify this computation process to suit their
needs: the agents decide which contexts are important for them and act by modifying in
consequence the computation process that is executed by the environment. In the MPC
case, the environment mediates the communication: the context is the information about
the agent receiver, the message and any other environment information which is rele-
vant to this communication. if the context is validated, the environment addresses the
messages to the related agents. In the simulation case, the environment is the scheduler
that manages the agent activation: the context is the subset of information accessible to
the agents. The environment activates an agent in a specific context and according to
this context, the agent performs the suitable action.
To adapt the perception process to each agent, the environment needs 'tools' to com-
pute their contexts. In our proposition, these tools are
filters
that reify the relation be-
tween an agent (described in the environment) and its contexts. A filter contains the
constraints on the context related to an action of an agent. This action is the reception
of a message for communication filters and a specific agent action to perform after val-
idation for activation filters. The agents modify their relation to the environment thanks
to the addition and removal of their filters in the environment. This process is dynamic
and the environment activates only its current filters. These choices (addition/removal)
belong to the decision process that is managed by each agent.
The externalization of the computation of the perception process improves qualita-
tively the design of a MAS. The dynamic filter management by the agents gives more
flexibility to the MAS design. More details on qualitative improvement are given in
[13] for communication and in [1] for simulation. The counterpart is a centralization of
a part of the MAS processes inside the environment. In this paper, we evaluate the cost
of this centralization for computing agent contexts, in comparison to the execution of
equivalent solutions for communication and simulation.
