Added first introduction part (Aggragate Programming paragraph)

Collektive · Nov 7, 2024 · a262750 · a262750
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diff --git a/site/docs/introduction/Aggregate Programming.md b/site/docs/introduction/Aggregate Programming.md
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+The progressive increase in the number of interconnected devices leads to a rise in the costs associated with maintaining distributed systems. This creates significant challenges in implementing software services on a global scale using the traditional approach of individually programming each agent, driving the search for solutions aimed at improving the autonomy of computing systems and reducing their complexity. In this context, aggregate programming emerges as a relevant approach: it is based on the functional composition of reusable collective behavior blocks, with the goal of efficiently achieving complex and resilient behaviors in dynamic networks. This methodology allows the analysis of fundamental properties, such as self-stabilization and independence from density. These properties are initially studied on basic building blocks, such as broadcasting, distance estimation, and data aggregation, before being transferred and validated on more complex systems created through functional composition. Aggregate programming finds its conceptual roots in Field Calculus... (continuare parlando di Field Calculus)
diff --git a/site/docs/introduction/_category_.json b/site/docs/introduction/_category_.json
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   "position": 20,
   "link": {
     "type": "generated-index",
-    "description": "TODO"
+    "description": "Descrizione della libreria come si collega ai paragrafi sotto l'indice..."
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 }
diff --git a/site/docs/introduction/introduction.md b/site/docs/introduction/introduction.md
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		The progressive increase in the number of interconnected devices leads to a rise in the costs associated with maintaining distributed systems. This creates significant challenges in implementing software services on a global scale using the traditional approach of individually programming each agent, driving the search for solutions aimed at improving the autonomy of computing systems and reducing their complexity. In this context, aggregate programming emerges as a relevant approach: it is based on the functional composition of reusable collective behavior blocks, with the goal of efficiently achieving complex and resilient behaviors in dynamic networks. This methodology allows the analysis of fundamental properties, such as self-stabilization and independence from density. These properties are initially studied on basic building blocks, such as broadcasting, distance estimation, and data aggregation, before being transferred and validated on more complex systems created through functional composition. Aggregate programming finds its conceptual roots in Field Calculus... (continuare parlando di Field Calculus)