**Title:** On the solution of cooperating stochastic models

**Time:** 14:00

**Location:** Meeting room

**Type:** Research Result

**Speaker:** Gian-Luca dei Rossi

**Abstract:**

However, while the compositionality of those formalism is a useful property which makes the modelling phase easier, exploiting it to get solutions more efficiently is a non-trivial task. Ideally one should be able to either detect a product-form solution and analyse the components in isolation or, if a product form cannot be detected, use other techniques to reduce the complexity of the solution, e.g., reducing the state space of either the single components or the joint process. Both tasks raised considerable interest in the literature, e.g., the RCAT theorem for the product-form detection or the Strong Equivalence relation of PEPA to aggregate states in a component-wise fashion.This talk deals with the aforementioned problem of efficiently solving complex Markovian models expressed in term of multiple components. We restrict our analysis to models in which components interact using an active-passive semantics. The main contributions rely on automatic product-forms detection, in components-wise lumping of forward and reversed processes and in showing that those two problems are indeed related, introducing the concept of conditional product-forms.