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

Machine Learning for Classical Planning: Neural Network Heuristics, Online Portfolios, and State Space Topologies

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best-first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work

Machine learning for classical planning : neural network heuristics, online portfolios, and state space topologies

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best- first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work.Viele Alltagsprobleme können mit Hilfe der Zustandsraumsuche gelöst werden. Heuristische Suche, insbesondere die gierige Bestensuche, ist einer der erfolgreichsten Algorithmen für die Zustandsraumsuche. Wir verbessern den aktuellen Stand der Wissenschaft bezüglich heuristischer Suche auf drei Arten. Eine der wichtigsten Komponenten der heuristischen Suche ist die Heuristik. Mit einer guten Heuristik findet die Suche schnell eine Lösung. Eine gute Heuristik für ein Problem zu modellieren ist mühsam. In Teil I präsentieren wir Methoden, um automatisiert gute Heuristiken für ein Problem zu lernen. Hierfür generieren wird die Trainingsdaten mittels Zufallsbewegungen ausgehend von (Teil-) Zuständen des Problems. Wir zeigen, dass die Heuristiken, die wir für einen einzigen Zustandsraum trainieren, oft besser sind als Heuristiken, die für eine Problemklasse trainiert wurden. Weiterhin zeigen wir, dass die Qualität aller trainierten Heuristiken je nach Problemklasse stark variiert, keine Heuristik eine andere dominiert, und es nicht vorher erkennbar ist, ob eine trainierte Heuristik gut funktioniert. Wir stellen fest, dass in fast allen getesteten Problemklassen die modellbasierte Suchalgorithmen den trainierten Heuristiken überlegen sind. Lediglich in der Storage Problemklasse sind unsere Heuristiken überlegen. Oft ist es unklar, welche Heuristik oder Suchalgorithmus man für ein Problem nutzen sollte. Daher trainieren wir online Portfolios, die für ein gegebenes Problem den besten Algorithmus vorherzusagen. Die Eingabe für das online Portfolio sind bisher immer von Menschen ausgewählte Eigenschaften des Problems. In Teil II präsentieren wir neue online Portfolios, die das gesamte Problem als Eingabe bekommen. Darüber hinaus können unsere online Portfolios ihre Entscheidung einmal korrigieren. Beide Änderungen verbessern die Qualität von online Portfolios erheblich. Weiterhin zeigen wir, dass wir auch gute online Portfolios mit erklärbaren Techniken des maschinellen Lernens trainieren können. Selbst wenn wir den besten Algorithmus für ein Problem auswählen, kann es sein, dass das Problem zu schwierig ist, um in akzeptabler Zeit gelöst zu werden. In Teil III zeigen wir, wie wir von dem Verhalten einer gierigen Bestensuche auf einfachen Problemen ihr Verhalten auf schwierigeren Problemen der gleichen Problemklasse vorhersagen können. Dieses Wissen nutzen wir, um die Suche zu verbessern. Zuerst zeigen wir, wie man Fortschrittszustände erkennt. Immer wenn gierige Bestensuche einen Fortschrittszustand expandiert, wissen wir, dass es nie wieder einen Zustand mit gleichem oder höheren heuristischen Wert expandieren wird.Wir präsentieren zwei Methoden, die diesesWissen verwenden. Aufbauend auf dieser Arbeit lernen wir von einem Problem, wie man jegliches Problem der gleichen Problemklasse in eine Reihe von einfacheren Suchen aufteilen kann

Heuristic search under time and cost bounds

Intelligence is difficult to formally define, but one of its hallmarks is the ability find a solution to a novel problem. Therefore it makes good sense that heuristic search is a foundational topic in artificial intelligence. In this context search refers to the process of finding a solution to the problem by considering a large, possibly infinite, set of potential plans of action. Heuristic refers to a rule of thumb or a guiding, if not always accurate, principle. Heuristic search describes a family of techniques which consider members of the set of potential plans of action in turn, as determined by the heuristic, until a suitable solution to the problem is discovered. This work is concerned primarily with suboptimal heuristic search algorithms. These algorithms are not inherently flawed, but they are suboptimal in the sense that the plans that they return may be more expensive than a least cost, or optimal, plan for the problem. While suboptimal heuristic search algorithms may not return least cost solutions to the problem, they are often far faster than their optimal counterparts, making them more attractive for many applications. The thesis of this dissertation is that the performance of suboptimal search algorithms can be improved by taking advantage of information that, while widely available, has been overlooked. In particular, we will see how estimates of the length of a plan, estimates of plan cost that do not err on the side of caution, and measurements of the accuracy of our estimators can be used to improve the performance of suboptimal heuristic search algorithms

Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification

We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology. We introduce a novel criteria for selecting meaningful and discriminative exemplar patches. We optimize this function during training using a greedy submodular function optimization framework that gives a near-optimal solution with bounded approximation error. We use these selected exemplars as a dictionary basis and propose a spatially-aware sparse coding method to match testing images for identification while maintaining global shape correspondence. To accelerate the coding process for fast matching, we introduce a relaxed form that uses spatially-aware soft-thresholding during coding. Finally, we carry out an experimental study that demonstrates the effectiveness and efficiency of our exemplar selection and classification mechanisms, achieving $86.13\%$ accuracy on a difficult fine-grained species classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201

Comparing the Performance of Expert User Heuristics and an Integer Linear Program in Aircraft Carrier Deck Operations

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human–automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative