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in environments in which they interact, and maybe cooperate with other agents (includ-
ing both people and software) that have possibly conflicting aims. Such environments
are known as multi-agent systems. Agents can be distinguished from objects (in the
sense of object-oriented software) in that they are autonomous entities capable of exer-
cising choice over their actions and interactions.
As presented in [17], Multi-agent systems (MAS) are based on the idea that a cooper-
ative distributed working environment comprising synergistic software components can
cope with problems which are hard to solve using the traditional centralized approach
to computation. Smaller software entities - software agents - with special capabili-
ties are used instead to interact in a flexible and dynamic way to solve problems more
efficiently.
Agents are considered to be autonomous ( i.e. , independent, not-controllable), reac-
tive ( i.e. , responding to events), pro-active ( i.e. , initiating actions of their own volition),
and social ( i.e. , communicative). Agents vary in their abilities; e.g. they can be static or
mobile, or may or may not be intelligent. Each agent may have its own task and/or role.
Agents and multi-agent systems are used as a metaphor to model complex distributed
processes.
On one hand we considered the SHARE-it problem settings, an elder trying to live
autonomously at his prefered environment/home burdened with a profile of disability
be it cognitive, physical or a combination of both. On the other hand we have the i-
Wa l k e r and other robotic platforms developed to assist these elders, and a specially
designed intelligent environment equiped with sensors and ubiquitous communications
tools to distribute the information gathered from them. In order to combine this set of
sensing tools, process the information that they provide and the functionalities of the
different platforms and actuators, we needed an intelligent and distributed approach.
Thus, developing a multi-agent system to manage all this information and provide a
sort of assistive services to the users, it is the decision we took considering the agent
technology capabilities and background.
Rational agents have an explicit representation of their environment (sometimes
called world model) and of the objectives they are trying to achieve. Rationality means
that the agent will always perform the most promising actions (based on the knowledge
about itself and the world) to achieve its objectives. As it usually does not know all
of the effects of an action in advance, it has to deliberate about the available options.
Regarding the theoretical foundation and the number of implemented and successfully
applied systems, the most interesting and widespread agent architecture is the Belief-
Desire-Intention (BDI) architecture, introduced by Bratman as a philosophical model
for describing rational agents [2]. It consists of the concepts of belief, desire and in-
tention as mental attitudes that generate human action. Beliefs capture informational
attitudes, desires motivational attitudes, and intentions deliberative attitudes of agents.
[23] have adopted this model and transformed it into a formal theory and an execution
model for software agents, based on the notion of beliefs, goals, and plans. The agent
development framework JADEX [21] has been our BDI model implementation choice
to build the SHARE-it multi-agent system.
The SHARE-it agent layer architecture (see Fig. 3) focuses on delivering three main
kind of services: monitorization, navigation support and cognitive support. This agent
 
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