HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN Planning

Real world applications as in industry and robotics need modelling rich and diverse automated planning problems. Their resolution usually requires coordinated and concurrent action execution. In several cases, these problems are naturally decomposed in a hierarchical way and expressed by a Hierarchical Task Network (HTN) formalism. HDDL, a hierarchical extension of the Planning Domain Definition Language (PDDL), unlike PDDL 2.1 does not allow to represent planning problems with numerical and temporal constraints, which are essential for real world applications. We propose to fill the gap between HDDL and these operational needs and to extend HDDL by taking inspiration from PDDL 2.1 in order to express numerical and temporal expressions. This paper opens discussions on the semantics and the syntax needed for a future HDDL 2.1 extension.Comment: 5 pages, International Workshop of Hierarchical Planning (ICAPS), 202

AMPLE: an anytime planning and execution framework for dynamic and uncertain problems in robotics

Acting in robotics is driven by reactive and deliberative reasonings which take place in the competition between execution and planning processes. Properly balancing reactivity and deliberation is still an open question for harmonious execution of deliberative plans in complex robotic applications. We propose a flexible algorithmic framework to allow continuous real-time planning of complex tasks in parallel of their executions. Our framework, named AMPLE, is oriented towards robotic modular architectures in the sense that it turns planning algorithms into services that must be generic, reactive, and valuable. Services are optimized actions that are delivered at precise time points following requests from other modules that include states and dates at which actions are needed. To this end, our framework is divided in two concurrent processes: a planning thread which receives planning requests and delegates action selection to embedded planning softwares in compliance with the queue of internal requests, and an execution thread which orchestrates these planning requests as well as action execution and state monitoring. We show how the behavior of the execution thread can be parametrized to achieve various strategies which can differ, for instance, depending on the distribution of internal planning requests over possible future execution states in anticipation of the uncertain evolution of the system, or over different underlying planners to take several levels into account. We demonstrate the flexibility and the relevance of our framework on various robotic benchmarks and real experiments that involve complex planning problems of different natures which could not be properly tackled by existing dedicated planning approaches which rely on the standard plan-then-execute loop

Translation-based approaches to automated planning with incomplete information and sensing

Artificial Intelligence Planning is about acting in order to achieve a desired goal. Under incomplete information, the task of finding the actions needed to achieve the goal can be modelled as a search problem in the belief space. This task is costly, as belief space is exponential in the number of states, which is exponential in the number of variables. Good belief representations and heuristics are thus critical for scaling up in this setting. The translation-based approach to automated planning with incomplete information deals with both issues by casting the problem of search in belief space to a search problem in state space, where each node of the search space represents a belief state. We develop plan synthesis tools that use translated versions of planning problems under uncertainty, with partial or null sensing available. We show formally under which conditions the introduced translations are polynomial, and capture all and only the plans of the original problems. We study empirically the value of these translations.La Planificación es la disciplina de Inteligencia Artificial que estudia los procesos de razonamiento necesarios para conseguir las acciones que logren un objetivo dado. En presencia de información incompleta, el problema de planificación puede ser modelado como una búsqueda en el espacio de estados de creencia, cada uno de ellos representando un conjunto de estados posibles. Este problema es costoso ya que el numero de estados de creencia puede ser exponencial en el número de estados, lo cual es exponencial en el número de variables del problema. El uso de buenas representaciónes de los estados y de heurísticas informadas resultan cruciales para escalar en este espacio de búsqueda. En esta tesis se presentan traducciones para planificación con información incompleta, que transforman el problema de búsqueda en el espacio de estados de creencia, en búsqueda en espacio de estados, donde cada nodo representa un estado de creencia. Hemos desarrollado herramientas para la generación de planes para el problema traducido, ya sea con percepción parcial o nula. A su vez, demostramos formalmente bajo qué circunstancias las traducciones son polinómicas, completas y correctas. La evaluación empírica remarca el valor de dichas traduccione

Safe LTL Assumption-Based Planning

Planning for partially observable, nondeterministic domains is a very significant and computationally hard problem. Often, reasonable assumptions can be drawn over expected/ nominal dynamics of the domain; using them to constrain the search may lead to dramatically improve the efficiency in plan generation. In turn, the execution of assumption-based plans must be monitored to prevent runtime failures that may happen if assumptions turn out to be untrue, and to replan in that case. In this paper, we use an expressive temporal logic, LTL, to describe assumptions, and we provide two main contributions. First, we describe an effective, symbolic forward-chaining mechanism to build (conditional) assumption-based plans for partially observable, nondeterministic domains. Second, we constrain the algorithm to generate safe plans, i.e. plans guaranteeing that, during their execution, the monitor will be able to univocally distinguish whether the domain behavior is one of those planned for or not. This is crucial to inhibit any chance of useless replanning episodes. We experimentally show that exploiting LTL assumptions highly improves the efficiency of plan generation, and that by enforcing safety we improve plan execution, inhibiting useless and expensive replanning episodes, without significantly affecting plan generation

A Translation-based Approach to Contingent Planning

The problem of planning in the presence of sensing has been addressed in recent years as a nondeterministic search problem in belief space. In this work, we use ideas advanced recently for compiling conformant problems into classical ones for introducing a different approach where contingent problems P are mapped into non-deterministic problems X(P) in state space. We also identify a contingent width parameter, and show that for problems P with bounded contingent width, the translation is sound, polynomial, and complete. We then solve X(P) by using a relaxation X + (P) that is a classical planning problem. The formulation is tested experimentally over contingent benchmarks where it is shown to yield a planner that scales up better than existing contingent planners.

From Informal Sketches to Systems Engineering Models Using AI Plan Recognition

Book chapter in W.F. Lawless, Ranjeev Mittu, Donald A. Sofge, Thomas Shortell and Tom McDermott (Eds.), Systems Engineering and Artificial Intelligence. Stuttgart, Germany: Springer Nature Switzerland AG. Chapter 22. Accepted in January 2021. Forthcoming.International audienceThe transition to Computer-Aided Design (CAD) changed engineers’ day-to-day tasks in many disciplines such as mechanical or electronic ones. System engineers are still looking for the right set of tools to embrace this opportunity. Indeed, they deal with many kinds of data which evolve a lot during the development life cycle. Model-Based Systems Engineering (MBSE) should be an answer to that but failed to convince and to be accepted by system engineers and architects. The complexity of creating, editing, and annotating models of systems engineering takes its root from different sources: high abstraction levels, static representations, complex interfaces, and the time-consuming activities to keep a model and its associated diagrams consistent. As a result, system architects still heavily rely on traditional methods (whiteboards, papers, and pens) to outline a problem and its solution, and then they use modeling expert users to digitize informal data in modeling tools. In this chapter, we present an approach based on automated plan recognition to capture sketches of systems engineering models and to incrementally formalize them using specific representations. We present a first implementation of our approach with AI plan recognition, and we detail an experiment on applying plan recognition to systems engineering

BOARD-AI: A Goal Recognition-Based Objective-Aware Modeling Interface for Systems Engineering

International audiencePaper and pens remain the most commonly used tools by systems engineers to capture system models. However, digitizing models sketched on a whiteboard into Computer-Aided Systems Engineering (CASE) tools remains a difficult and error-prone activity that requires the knowledge of tool experts. This study presents a reactive whiteboard interface for model sketching. This online approach combines techniques from Automated Planning and Machine Learning to improve performance while not compromising explainability of the system's output. Our approach mainly relies on two main modules: (1) a trained neural network that separates upstream from the global recognition process, handwritten text from geometrical symbols, and (2) a goal recognition algorithm that models the sketching task as a planning problem to identify the final s ketches t hat a re d eemed p ossible. T he main benefits of BOARD-AI are its autonomy, i.e. it does not rely on any other interaction modalities (e.g., virtual keyboards), and its explainability, i.e. the outcomes of the modeling assistant are understandable since a plan provides a valid explanation of the system's suggestions. Finally, BOARD-AI usability was validated trough user evaluations of engineers, experts and non experts in software and/or system modeling design. The demo video is available at https://cloud.univ-grenoblealpes.fr/s/9ifZZCqW7eWQEdy.Papier et stylos restent les outils les plus couramment utilisés par les ingénieurs systèmes pour capturer leurs modèles. Cependant, la numérisation des modèles esquissés sur un tableau blanc dans des outils d'ingénierie des systèmes assistés par ordinateur (CASE) reste une activité difficile et sujette à erreurs qui nécessite les connaissances d'experts en la matière. Ce travail présente une interface réactive pour l'esquisse de modèles sur tableau blanc. Cette approche en ligne combine des techniques de planification automatique et d'apprentissage afin d'améliorer les performances tout en ne compromettant pas l'explicabilité des résultats du système. Notre approche repose principalement sur deux modules : (1) un réseau neuronal qui sépare, en amont du processus de reconnaissance globale, le texte manuscrit des symboles géométriques, et (2) un algorithme de reconnaissance des objectifs qui modélise la tâche de croquis comme un problème de planification afin d'identifier les croquis finaux qui sont jugés possibles. Les principaux avantages de BOARD-AI sont son autonomie, c'est-à-dire qu'il ne dépend d'aucune autre modalité d'interaction (par exemple, les claviers virtuels), et sa capacité d'explication, c'est-à-dire que les résultats de l'assistant de modélisation sont compréhensibles puisqu'un plan fournit une explication valide des suggestions du système. Enfin, la facilité d'utilisation de BOARD-AI a été validée par des évaluations d'ingénieurs, d'experts et de non experts en conception de logiciels et/ou de systèmes de modélisation. La vidéo de démonstration est disponible sur https://cloud.univ-grenoble-alpes.fr/s/9ifZZCqW7eWQEdy

Open Cultural Data and MediaWiki Software for a Museum: The Use Case of Musée Saint-Raymond (Toulouse, France)

Freely accessible online databases developed by cultural and artistic institutions (e.g., museums, libraries, universities, studios, etc.) enable the transnational dissemination of catalogues of cultural and creative works, exploiting the advantages of modern technologies. Intelligent tools, which use advanced algorithms to classify and contextualize data, can foster knowledge mainly in two ways: (1) providing a stable and accessible basis for large amounts of data; (2) promoting cultural heritage. A case of skillful use of such tools is the Saint-Raymond Museum, the archaeological museum of Toulouse. For several years it has been working on the open data front and on putting its catalogue online on Wikimedia platforms, in various forms