Iterative Statistical Verification of Probabilistic Plans

Artificial intelligence seeks to create intelligent agents. An agent can be anything: an autopilot, a self-driving car, a robot, a person, or even an anti-virus system. While the current state-of-the-art may not achieve intelligence (a rather dubious thing to quantify) it certainly achieves a sense of autonomy. A key aspect of an autonomous system is its ability to maintain and guarantee safety—defined as avoiding some set of undesired outcomes. The piece of software responsible for this is called a planner, which is essentially an automated problem solver. An advantage computer planners have over humans is their ability to consider and contrast far more complex plans of action. Safety may be defined probabilistically, in which case the probability of “failure” must be below some given threshold θ. The process of deciding the level of safety a plan achieves is called verification. The plans considered in this work are too complex to analyze analytically (the process would take too much time and/or memory to complete). This motivates a statistical sampling based approach, which works by generating “sample traces” of the plan—like simulating a roll of dice. DAGification—the systematic expansion of this representation—allows the computation of the the required probabilities for safety with bounded levels of error and in a reasonable number of samples. This work presents several new DAGification schemes with a detailed discussion of their correctness

A general framework integrating techniques for scheduling under uncertainty

Ces dernières années, de nombreux travaux de recherche ont porté sur la planification de tâches et l'ordonnancement sous incertitudes. Ce domaine de recherche comprend un large choix de modèles, techniques de résolution et systèmes, et il est difficile de les comparer car les terminologies existantes sont incomplètes. Nous avons cependant identifié des familles d'approches générales qui peuvent être utilisées pour structurer la littérature suivant trois axes perpendiculaires. Cette nouvelle structuration de l'état de l'art est basée sur la façon dont les décisions sont prises. De plus, nous proposons un modèle de génération et d'exécution pour ordonnancer sous incertitudes qui met en oeuvre ces trois familles d'approches. Ce modèle est un automate qui se développe lorsque l'ordonnancement courant n'est plus exécutable ou lorsque des conditions particulières sont vérifiées. Le troisième volet de cette thèse concerne l'étude expérimentale que nous avons menée. Au-dessus de ILOG Solver et Scheduler nous avons implémenté un prototype logiciel en C++, directement instancié de notre modèle de génération et d'exécution. Nous présentons de nouveaux problèmes d'ordonnancement probabilistes et une approche par satisfaction de contraintes combinée avec de la simulation pour les résoudre. ABSTRACT : For last years, a number of research investigations on task planning and scheduling under uncertainty have been conducted. This research domain comprises a large number of models, resolution techniques, and systems, and it is difficult to compare them since the existing terminologies are incomplete. However, we identified general families of approaches that can be used to structure the literature given three perpendicular axes. This new classification of the state of the art is based on the way decisions are taken. In addition, we propose a generation and execution model for scheduling under uncertainty that combines these three families of approaches. This model is an automaton that develops when the current schedule is no longer executable or when some particular conditions are met. The third part of this thesis concerns our experimental study. On top of ILOG Solver and Scheduler, we implemented a software prototype in C++ directly instantiated from our generation and execution model. We present new probabilistic scheduling problems and a constraintbased approach combined with simulation to solve some instances thereof

Guiding planner backjumping using verifier traces

In this paper, we show how a planner can use a modelchecking verifier to guide state space search. In our work on hard real-time, closed-loop planning, we use a modelchecker’s reachability computations to determine whether plans will be successfully executed. For planning to proceed efficiently, we must be able to efficiently repair candidate plans that are not correct. Reachability verifiers can return counterexample traces when a candidate plan violates desired properties. A core contribution of our work is our technique for automatically extracting repair candidates from counterexample traces. We map counterexample traces onto a search algorithm’s choice stack to direct backjumping. We prove that our technique will not sacrifice completeness, and present empirical results showing substantial performance improvements in difficult cases. Our results can be applied to other applications, such as automatic design, and manufacturing scheduling