Understanding the functioning of genetic regulatory networks supposes a modeling of biological processes in order to simulate behaviors and to reason on the model. Unfortunately, the modeling task is confronted to incomplete knowledge about the system. To deal with this problem we propose a methodology that uses the qualitative approach developed by Thomas. A symbolic transition system can repr...

Inductive learning is the method of learning from observations. Inductive learning has important applications over a wide range of area including pattern recognition, language acquisition, bio-informatics and intelligent agent design. Because of such diverse applicability, inductive learning methods including automata learning, grammar induction, hidden markov model learning and symbolic statis...

This paper presents an overview of the current state of the art in image fusion, with an emphasis on the emergence of new techniques, often issued from other domains like arti cial intelligence and uncertainty modeling. We address the two following points: rstly the aim of data fusion and its speci city when image information has to be combined, with emphasis on the respective roles of numerica...

Biological systems consist of many components and interactions between them. In Systems Biology the principal problem is modeling complex biological systems and reconstructing interactions between their building blocks. Symbolic machine learning approaches have the power to model structured domains and relations among objects. However biological domains require uncertainty handling due to their...

The Symbolic Analysis Laboratory (SAL) is a set of tools for the specification, exploration, and verification of state-transition systems. SAL includes symbolic model-checking tools based on solvers and decision procedures for linear arithmetic, uninterpreted functions, and propositional logic, among others. This enables the analysis of a variety of infinite-state systems. In particular, SAL ca...

We discuss applications of rewriting in three different areas: design and analysis of algorithms, theorem proving and term rewriting, and modeling and analysis of biological processes. 1998 ACM Subject Classification F.4.2 [Mathematical Logic and Formal Languages] Grammars and Other Rewriting Systems–Decision problems; I.6.5 [Simulation and Modeling] Model Development–Modeling Methodologies; I....

With the recent proliferation of open-source packages for computing, power system differential-algebraic equation (DAE) modeling and simulation are being revisited to reduce programming efforts. Existing tools require manual efforts develop code numerical equations, sparse Jacobians, discontinuous components. This paper proposes a hybrid symbolic-numeric framework, exemplified by an Python-base...