5 accepted papers
Over-parameterized neural networks, i.e., models with excess capacity that can fit training data exactly, have demonstrated superior generalization performance compared to classical models with balanced capacity. Nevertheless, their deployment in safety-critical domains re- mains severely constraine
Hand-coded deliberation components are prone to flaws that may not be discovered before deployment and that can be harmful to the robot and its execution environment, including the people within it. To reduce development effort and at the same time increase confidence in robot’s safety, we propose t
The problem of passive learning of linear temporal logic formulae consists in finding the best explanation for how two sets of execution traces differ, in the form of the shortest formula that separates the two sets. We approach the problem by implementing an exhaustive search algorithm optimized fo
In this paper, we enable automated property verification of deliberative components in robot control architectures. We focus on formalizing the execution context of Behavior Trees (BTs) to provide a scalable, yet formally grounded, methodology to enable runtime verification and prevent unexpected ro…
We consider the problem of planning with arithmetic theories, and focus on generating optimal plans for numeric domains with constant and state-dependent action costs. Solving these problems efficiently requires a seamless integration between propositional and numeric reasoning. We propose…