A Social Semantic Infrastructure for Ambient
Ambient intelligence scenarios
envisage devices and software agents, running
in devices, that organise themselves for the wellness of their
users: software agents interoperate and share knowledge or experiences,
gather information (e.g., road traffic), they automatically pay amount
money from e-purses, they customise rooms lights and temperature,
for references, or build user profiles.
These applications are supported by
an unobtrusive and invisible technology,
which is able to take decisions, and initiatives, make proposals to the
and negotiate. In addition, in order to fully support human beings
overloading them with requests and information, the underlying
(devices, and agents) needs advanced means of communication for:
each other, gather and share knowledge, information and experience
other, ensure their own security (data integrity, confidentiality,
access control), and resources management. In distributed and
environments, as those in which ambient intelligence systems will
interoperable policies are closely linked with authorisation policies,
resource management. Therefore, such a technology needs a social
enabling agents mutual understanding, and knowledge sharing for
security support, and resource management in an intertwined way.
This project builds on the previous Engineering
Applications project experience
on self-organising mechanisms, and intends
to extend, and integrate them in order to define an infrastructure for
interoperable computing entities following a human-like communication.
In addition to checking each other interoperability capabilities,
can appreciate and diffuse information about the capacities of a peer
the issue of an interaction, and the availability of some resource
in its environment through trust, recommendations, and evidence
and gathering. Such an infrastructure integrates, in an interweaved
of semantical content, support for knowledge sharing and diffusion, and
handling additional issues, such as security or environment management.
Our approach consists in proposing a
meta-ontology framework coupled with
a dynamic trust-based management system, in order to produce a
middleware for representing, and diffusing semantic information among
software. This project will provide a social interaction model based
on a homogeneous framework which serves for expressing and checking
information of different kinds: functional behaviour, non-functional
observations, recommendations, users profiles; and serve different
interoperability, users customisation, resources management,
or access control. This information is shared among entities. On the
of this information, autonomous entities decide to interact, to
or deny access, to ask for more information, or to find other partners.
model we intend to build is based on three principles: semantical information framework based on a higher-order logic
and proof checking, i.e., an entity brings the proof of its claimed
functionality, or identity (expressed using specification); information and knowledge exchange; and processing
of information giving rise to
actual interactions, recommendations, or
requests (trust-based management systems). In addition to the social
model, we will investigate the definition of a formal logical language
incorporating the social
interaction model, and defining operators
for: ontology definition (vocabulary, relations, axioms,
observation, recommendation, information request, and processing
information. Finally, for validation purposes, we foresee an
of the formal language operational semantics. It will provide an
distributed middleware supporting the social interaction model, the
information exchange, and proof-checking. In addition, it must allow
integration of new sites, of new computing entities participating in
of interacting devices.
The goal of this project is to
provide a middleware infrastructure for programming
components taking part in an ambient intelligence applications in such
that those components will interact with each other as would do human
Human beings exchange different kinds of semantical information for
types of purposes: to understand each other, to share knowledge about
or something else, to take decisions, to learn more, etc.
and partial knowledge are a key characteristic of the natural world.
this uncertainty human beings make choices, take decisions, learn by
and adapt their behaviour.
Similarly, software entities that
will take part in ambient intelligence
systems will be part of decentralised systems, and embedded in highly
environments where peer entities appear and disappear permanently, and
information dynamically changes and is not permanently valid.
with peers can occur only locally, there is only partial knowledge
entities and about the environment.
The following small example shows
how a group of computers can share
a pool of printers through our envisioned infrastructure. Before
with each other computers and printers exchange their respective
as well as non-functional capabilities, e.g. a printer claims
is a postscript double-sided printer, and a computer asks to print a
After having interacted with a printer, the computer stores the
related to its experience with the printer (works as expected, only one
no impression at all, etc.). Depending on the outcome of the
or if it has been requested to do so, the computer may want to share
with some of the other computers. It will then inform the others that
printer is not actually double-sided, but only single sided, or that
printer went out of toner, and is no longer available, or that one of
printers is faulty and has a random behaviour.
This example shows that: printers
and computers can exchange information
about their respective functional and non-functional behaviour;
can exchange information among themselves about the printers and other
state or actual capabilities (independently of their claimed
the shared knowledge allows computers to efficiently use the remaining
of working printers (adaptation, resource management), as well as to
inform the user about the nearest well functioning printer. This
as well the validity of information. The faulty printer has a random
this is a long term valid information (information is not very
but not volatile). However, if the printer has been able to print two
ago, we can almost be sure that it will be able to print in the next
of minutes, but not necessarily later (information is accurate but
volatile). This example raises also the question of the accuracy of a
information. In the case of the printer, it claims that it can print,
actually it cannot. In the case of a computer, it can claim that
printer is out of order, but it may lie. In both cases, sharing
about printers or other computers helps circumvent the problem, and
the personals as well as the collective behaviour to the environment.
This project started October 2004
and ended September 2005.
G. Di Marzo Serugendo