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

Making Hill-Climbing Great Again through Online Relaxation Refinement and Novelty Pruning

Delete relaxation is one of the most successful approaches to classical planning as heuristic search. The precision of these heuristics can be improved by taking some delete information into account, in particular through atomic conjunctions in the hCFF heuristic. It has recently been shown that this heuristic is especially effective when these conjunctions are learned online in a hill-climbing search algorithm. In this work, we devise a natural extension to this approach using novelty pruning, a recently-developed technique that prunes states based on whether they contain facts not seen before in the search. We evaluate our extension on the IPC benchmarks, where it beats LAMA, Mercury, and Dual-BFWS on many domains

A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search

Best-first width search (BFWS) is a recent approach to satisficing planning that combines traditional heuristics with novelty measures to achieve a balance between exploration and effective search guidance (exploitation). One such novelty measure is based on counting the number of subgoals achieved on the path from a state in which a relaxed plan was computed. We introduce a new lookahead strategy for greedy best-first search based on this idea, where after each expansion, a bounded lookahead search is guided by relaxed subgoal counting. Furthermore, we combine this technique with partial delete relaxation heuristics to improve the subgoals. Using the hCFF heuristic with online-refinement of conjunctions, we obtain a planner that significantly outperforms the state of the art in satisficing planning on the IPC benchmarks

Explicit Conjunctions without Compilation: Computing h-FF(Pi-C) in Polynomial Time

A portrait of David Atkins in jump gear. Atkins is a crew member at the Siskiyou Smokejumper Base.https://dc.ewu.edu/beck/1344/thumbnail.jp

Online Refinement of Cartesian Abstraction Heuristics

In classical planning as heuristic search, the guiding heuristic function is typically treated as a black box. While many heuristics support refinement operations, they are typically only used for its initialization before search, but further refinement during search could make use of additional information not available in the initial state. We explore online refinement for additive Cartesian abstraction heuristics. These abstractions are computed through counter-example guided abstraction refinement, which can be applied online as well to further improve the abstractions. We introduce three operations, refinement, merging, and reordering, which are combined to a converging online-refinement algorithm. We describe how online refinement can effectively be used in A* and evaluate our approach on the IPC benchmarks, where it outperforms offline-generated abstractions in many domains

Complete Local Search: Boosting Hill-Climbing through Online Relaxation Refinement

Several known heuristic functions can capture the input at different levels of precision, and support relaxation-refinement operations guaranteeing to converge to exact information in a finite number of steps. A natural idea is to use such refinement online, during search, yet this has barely been addressed. We do so here for local search, where relaxation refinement is particularly appealing: escape local minima not by search, but by removing them from the search surface. Thanks to convergence, such an escape is always possible. We design a family of hill-climbing algorithms along these lines. We show that these are complete, even when using helpful actions pruning. Using them with the partial delete relaxation heuristic hCFF, the best-performing variant outclasses FF's enforced hill-climbing, outperforms FF, outperforms dual-queue greedy best-first search with hFF, and in 6 IPC domains outperforms both LAMA and Mercury

Explicit Conjunctions without Compilation: Computing h-FF(Pi-C) in Polynomial Time

A successful partial delete relaxation method is to compute hFF in a compiled planning task PiC which represents a set C of conjunctions explicitly. While this compilation view of such partial delete relaxation is simple and elegant, its meaning with respect to the original planning task is opaque. We provide a direct characterization of h+(PiC), without compilation, making explicit how it arises from a "marriage" of the critical-path heuristic hm with (a somewhat novel view of) h+. This explicit view allows us to derive a direct characterization of hFF(PiC), which in turn allows us to compute a version of that heuristic function in time polynomial in |C|

Ranking Conjunctions for Partial Delete Relaxation Heuristics in Planning

Heuristic search is one of the most successful approaches to classical planning, finding solution paths in large state spaces. A major focus has been the development of domain-independent heuristic functions. One recent method are partial delete relaxation heuristics, improving over the standard delete relaxation heuristic through imposing a set C of conjunctions to be treated as atomic. Practical methods for selecting C are based on counter-example guided abstraction refinement, where iteratively a relaxed plan is checked for conflicts and new atomic conjunctions are introduced to address these. However, in each refinement step, the choice of possible new conjunctions is huge. The literature so far offers merely one simple strategy to make that choice. Here we fill that gap, considering a sizable space of basic ranking strategies as well as combinations thereof. We furthermore devise ranking strategies for conjunction-forgetting, where the ranking pertains to the current conjunctions and thus statistics over their usefulness can be maintained. Our experiments show that ranking strategies do make a large difference in performance, and that our new strategies can be useful