Cartesian Abstraction Can Yield ‘Cognitive Maps’

Counterexample-Guided Cartesian Abstraction Refinement

Counterexample-guided abstraction refinement (CEGAR) is a method for incrementally computing abstractions of transition systems. We propose a CEGAR algorithm for computing abstraction heuristics for optimal classical planning. Starting from a coarse abstraction of the planning task, we iteratively compute an optimal abstract solution, check if and why it fails for the concrete planning task and...

Additive Counterexample-Guided Cartesian Abstraction Refinement

We recently showed how counterexample-guided abstraction refinement can be used to derive informative heuristics for optimal classical planning. In this work we introduce an algorithm for building additive abstractions and demonstrate that they outperform other state-of-the-art abstraction heuristics on many benchmark domains. Introduction Recently, we presented a new algorithm for deriving adm...

Thread-Modular Verification and Cartesian Abstraction

Verification of multithreaded programs is difficult. It requires reasoning about state spaces that grow exponentially in the number of concurrent threads. Successful verification techniques based on modular composition of overapproximations of thread behaviors have been designed for this task. These techniques have been traditionally described in assume-guarantee style, which does not admit rea...

Cartesian abstraction and verification of multithreaded programs

Diverse and Additive Cartesian Abstraction Heuristic

We have recently shown how counterexample-guided abstraction refinement can be used to derive informative Cartesian abstraction heuristics for optimal classical planning. In this work we introduce two methods for producing diverse sets of heuristics within this framework, one based on goal facts, the other based on landmarks. In order to sum the heuristic estimates admissibly we present a novel...