Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups

Design of a solver for multi-agent epistemic planning

As the interest in Artificial Intelligence continues to grow it is becoming more and more important to investigate formalization and tools that allow us to exploit logic to reason about the world. In particular, given the increasing number of multi-agents systems that could benefit from techniques of automated reasoning, exploring new ways to define not only the world's status but also the agents' information is constantly growing in importance. This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning. In our work we will try to formalize this concept, expressed through epistemic logic, for dynamic domains. In particular we will attempt to define a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to be able to reason on different domains, e.g., economy, security, justice and politics, where reasoning about others' beliefs could lead to winning strategies or help in changing a group of agents' view of the world.Comment: In Proceedings ICLP 2019, arXiv:1909.07646. arXiv admin note: text overlap with arXiv:1511.01960 by other author

Adaptive search techniques in AI planning and heuristic search

State-space search is a common approach to solve problems appearing in artificial intelligence and other subfields of computer science. In such problems, an agent must find a sequence of actions leading from an initial state to a goal state. However, the state spaces of practical applications are often too large to explore exhaustively. Hence, heuristic functions that estimate the distance to a goal state (such as straight-line distance for navigation tasks) are used to guide the search more effectively. Heuristic search is typically viewed as a static process. The heuristic function is assumed to be unchanged throughout the search, and its resulting values are directly used for guidance without applying any further reasoning to them. Yet critical aspects of the task may only be discovered during the search, e.g., regions of the state space where the heuristic does not yield reliable values. Our work here aims to make this process more dynamic, allowing the search to adapt to such observations. One form of adaptation that we consider is online refinement of the heuristic function. We design search algorithms that detect weaknesses in the heuristic, and address them with targeted refinement operations. If the heuristic converges to perfect estimates, this results in a secondary method of progress, causing search algorithms that are otherwise incomplete to eventually find a solution. We also consider settings that inherently require adaptation: In online replanning, a plan that is being executed must be amended for changes in the environment. Similarly, in real-time search, an agent must act under strict time constraints with limited information. The search algorithms we introduce in this work share a common pattern of online adaptation, allowing them to effectively react to challenges encountered during the search. We evaluate our contributions on a wide range of standard benchmarks. Our results show that the flexibility of these algorithms makes them more robust than traditional approaches, and they often yield substantial improvements over current state-of-the-art planners.Die Zustandsraumsuche ist ein oft verwendeter Ansatz um verschiedene Probleme zu lösen, die in der Künstlichen Intelligenz und anderen Bereichen der Informatik auftreten. Dabei muss ein Akteur eine Folge von Aktionen finden, die einen Pfad von einem Startzustand zu einem Zielzustand bilden. Die Zustandsräume von praktischen Anwendungen sind häufig zu groß um sie vollständig zu durchsuchen. Aus diesem Grund leitet man die Suche mit Heuristiken, die die Distanz zu einem Zielzustand abschätzen; zum Beispiel lässt sich die Luftliniendistanz als Heuristik für Navigationsprobleme einsetzen. Heuristische Suche wird typischerweise als statischer Prozess angesehen. Man nimmt an, dass die Heuristik während der Suche eine unveränderte Funktion ist, und die resultierenden Werte werden direkt zur Leitung der Suche benutzt ohne weitere Logik darauf anzuwenden. Jedoch könnten kritische Aspekte des Problems erst im Laufe der Suche erkannt werden, wie zum Beispiel Bereiche des Zustandsraums in denen die Heuristik keine verlässlichen Abschätzungen liefert. In dieser Arbeit wird der Suchprozess dynamischer gestaltet und der Suche ermöglicht sich solchen Beobachtungen anzupassen. Eine Art dieser Anpassung ist die Onlineverbesserung der Heuristik. Es werden Suchalgorithmen entwickelt, die Schwächen in der Heuristik erkennen und mit gezielten Verbesserungsoperationen beheben. Wenn die Heuristik zu perfekten Werten konvergiert ergibt sich daraus eine zusätzliche Form von Fortschritt, wodurch auch Suchalgorithmen, die sonst unvollständig sind, garantiert irgendwann eine Lösung finden werden. Es werden auch Szenarien betrachtet, die schon von sich aus Anpassung erfordern: In der Onlineumplanung muss ein Plan, der gerade ausgeführt wird, auf Änderungen in der Umgebung angepasst werden. Ähnlich dazu muss sich ein Akteur in der Echtzeitsuche unter strengen Zeitauflagen und mit eingeschränkten Informationen bewegen. Die Suchalgorithmen, die in dieser Arbeit eingeführt werden, folgen einem gemeinsamen Muster von Onlineanpassung, was ihnen ermöglicht effektiv auf Herausforderungen zu reagieren die im Verlauf der Suche aufkommen. Diese Ansätze werden auf einer breiten Reihe von Benchmarks ausgewertet. Die Ergebnisse zeigen, dass die Flexibilität dieser Algorithmen zu erhöhter Zuverlässigkeit im Vergleich zu traditionellen Ansätzen führt, und es werden oft deutliche Verbesserungen gegenüber modernen Planungssystemen erzielt.DFG grant 389792660 as part of TRR 248 – CPEC (see https://perspicuous-computing.science), and DFG grant HO 2169/5-1, "Critically Constrained Planning via Partial Delete Relaxation

SOTER on ROS: A Run-Time Assurance Framework on the Robot Operating System

We present an implementation of SOTER, a run-time assurance framework for building safe distributed mobile robotic (DMR) systems, on top of the Robot Operating System (ROS). The safety of DMR systems cannot always be guaranteed at design time, especially when complex, off-the-shelf components are used that cannot be verified easily. SOTER addresses this by providing a language-based approach for run-time assurance for DMR systems. SOTER implements the reactive robotic software using the language P, a domain-specific language designed for implementing asynchronous event-driven systems, along with an integrated run-time assurance system that allows programmers to use unfortified components but still provide safety guarantees. We describe an implementation of SOTER for ROS and demonstrate its efficacy using a multi-robot surveillance case study, with multiple run-time assurance modules. Through rigorous simulation, we show that SOTER enabled systems ensure safety, even when using unknown and untrusted components.Comment: 20th International Conference on Runtime Verificatio