Section: New Results

Parameter Search with Temporal Logic Constraints

Participants : Grégory Batt, François Fages, Anthony Lins, Sylvain Soliman, Pauline Traynard, Jannis Uhlendorf, Luma Vittorino.

Our method for solving temporal logic constraints in first-order linear time logic LTL($R_{\text{lin}}$), opens up the field of model-checking to optimization through the definition of a continuous degree of satisfaction for temporal logic formulae. This satisfaction degree can be used in a number of ways, e.g. as a fitness function with continuous optimization methods to find unknown parameter values in a model, to perform sensitivity analyses and compute the robustness of a system w.r.t. a temporal property and a perturbation of the parameters. or to find control parameters.

This approach is implemented in BIOCHAM and is one unique feature of this modeling environment. In this implementation, the continuous optimization procedure we use is the Covariance Matrix Adaptation Evolutionary Strategy CMAES of Nikolaus Hansen from the EPI TAO. A parallel version of Biocham implements this method on the Jade cluster of 10000 cores at GENCI for running our most challenging parameter search problems.

This year, in collaboration with Fernando Buarque, we have explored another continuous optimization method of the family of Particle Swarm Optimization (PSO), called Fish School Optimization (FSS). In [13] , we report on our first results which are encouraging for using FSS for decreasing the sensitivity of the method to initial conditions and being able to maintain several swarms of solutions.