Title: Information-Based Optimization Approaches to Dynamical System
Safety Verification
Abstract: Given a heuristic estimate of the relative safety of a
hybrid dynamical system trajectory, we transform the initial safety
problem for dynamical systems into a global optimization problem. We
introduce MLLO-IQ and MLLO-RIQ, two new information-based optimization
algorithms. After demonstrating their strengths and weaknesses, we
describe the class of problems for which different optimization
methods are best-suited.
The transformation of an initial safety problem for dynamical systems
into a global optimization problem is accomplished through
construction of a heuristic function which simulates a system
trajectory and returns a heuristic evaluation of the relative safety
of that trajectory. Since each heuristic function evaluation may be
computationally expensive, it becomes desirable to invest more
computational effort in intelligent use of function evaluation
information to reduce the average number of evaluations needed. To
this end, we've developed MLLO-IQ and MLLO-RIQ, information-based
methods which approximate optimal optimization decision procedures.