Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
The composition and validation of heterogeneous control laws
Automatica (Journal of IFAC)
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Proving properties of continuous systems: qualitative simulation and temporal logic
Artificial Intelligence
A non-parametric Monte Carlo technique for controller verification
Automatica (Journal of IFAC)
Qualitative and quantitative simulation: bridging the gap
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Business Dynamics
Improved Filtering for the QSIM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Duration Consistency Filtering for Qualitative Simulation
Annals of Mathematics and Artificial Intelligence
Sound and complete qualitative simulation is impossible
Artificial Intelligence
Qualitative heterogeneous control of higher order systems
HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Order-Preserving Symmetric Encryption
EUROCRYPT '09 Proceedings of the 28th Annual International Conference on Advances in Cryptology: the Theory and Applications of Cryptographic Techniques
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We present improvements to the function representation and generation method used in the Monte Carlo analysis of incomplete ordinary differential equations. Our method widens the scope of the technique to cover cases in which no envelopes have been specified for the function under consideration, thereby extending the applicability of the Monte Carlo approach to the full repertoire of models developed for qualitative reasoning algorithms, and paving the ground for the integrated operation of these two highly complementary techniques. Our new representation does not entail unjustified implicit assumptions about the shape of the generated functions, and provides better coverage of the space of models defined by the input specifications. Our simulator (MOCASSIM) also has the capability of imposing additional restrictions (e.g., convexity) on function shapes, which is particularly useful when the Monte Carlo technique is applied for solving system dynamics problems.