
## 3 Environment Study And Creation Of Semantic Features 3.1 Situation Formalisation

To formalise a situation means to create a formal system, in an attempt to capture the essential features of the real-world. To realise this, **we model the** world as a collection of objects, where each one holds some properties. The aim is to define the environment objects following the object paradigm. **Therefore,**
we build a structural and hierarchical form in order to give a meaning **to the** various relations that may exist between them. The dynamic change **of these**
objects states and more still the interactions that could be entrenched between them will provide us a snapshot description of the environment. In our context, information are decomposed in atomic data where each one is associated to a given object.

## 3.2 Semantic Features

A semantic feature is an elementary piece of information coming from **the environment and which represents a fact that occurred in the world. Each semantic**
feature is related to an object (defined in section 3.1), and allows to **define** all or a part of this object. A semantic feature has the following form: (key,
(qualif ication, value)
+**), where key is the described object and (**qualif ication, value)
+ **is a set of couples formed by: the qualification of the object and its**
associated value. As example of a semantic feature related to a phenomenon object: (phenomenon\#1, type, fire, location, \#4510, time, 9:33). The object described by this semantic feature is phenomenon\#1, and has as qualifications:
type, location, and time.

The modelling of semantic features makes it possible to obtain a homogeneous structure. This homogeneity is of primary importance because it allows to establish comparisons between these data. The latter are managed by factual agents, where each one carries one semantic feature and of **which behaviour**
depends on the type of this information.

According to FIPA communicative acts [6], the agents must share the same language and vocabulary to communicate. The use of semantic features in communications process implies to define an ontology.

Inside the representation layer (the first layer of the system), agents evolve by comparing their semantic features. These comparisons allow to establish semantic distances between the agents, and are computed thanks to proximity measures.

We distinguish three types of proximities: time proximity, spatial proximity and semantic proximity. The global proximity multiplies these three proximities together. The measurement of a semantic proximity is related to ontologies.

Whereas time proximity and spatial proximity are computed according to specific functions.

Proximities computation provides values on [−1, **1] and is associated to a**
scale. The reference value in this scale is 0 that means neutral relation between