Model Recognition as Planning

Given a partially observed plan execution, and a set of pos-sible planning models (models that share the same state vari-ables but different action schemata),model recognitionis thetask of identifying the model that explains the observation.The paper formalizes this task and introduces a novel methodthat estimates the probability of a STRIPSmodel to producean observation of a plan execution. This method builds on topof off-the-shelf classical planning algorithms and it is robustto missing actions and intermediate states in the observation.The effectiveness of the method is tested in three experiments,each encoding a set of different STRIPSmodels and all us-ing empty-action observations: (1) a classical string classifi-cation task; (2) identification of the model that encodes a fail-ure present in an observation; and (3) recognition of a robotnavigation policy

Falsification of Cyber-Physical Systems Using PDDL+ Planning

This work explores the capabilities of current planning technologies to tackle the falsification of safety requirements for cyber-physical systems. Cyber-physical systems are systems where software and physical processes interact over time, and their requirements are commonly specified in temporal logic with time bounds. Roughly, falsification is the process of finding a trajectory of the cyber-physical system that violates the safety requirements, and it is a task typically tackled with black-box algorithms. We analyse the challenges posed by industry-driven falsification benchmarks taken from the ARCH-COMP competition, and propose a first attempt to deal with these problems through PDDL+ planning instead. Our experimental analysis on a selection of these problems provides empirical evidence on the feasibility and effectiveness of planning-based approaches, whilst also identifying the main areas of improvement

Explaining the Behaviour of Hybrid Systems with PDDL+ Planning

The aim of this work is to explain the observed behaviour of a hybrid system (HS). The explanation problem is cast as finding a trajectory of the HS that matches some observations. By using the formalism of hybrid automata (HA), we characterize the explanations as the language of a network of HA that comprises one automaton for the HS and another one for the observations, thus restricting the behaviour of the HS exclusively to trajectories consistent with the observations. We observe that this problem corresponds to a reachability problem in model-checking, but that state-of-the-art model checkers struggle to find concrete trajectories. To overcome this issue we provide a formal mapping from HA to PDDL+ and rely on off-the-shelf automated planners. An experimental analysis over domains with piece-wise constant, linear and nonlinear dynamics reveals that the proposed PDDL+ approach is much more efficient than solving directly the explanation problem with model-checking solvers