Characterizing and Computing All Delete-Relaxed Dead-ends

Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this en

Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan

This paper reviews the connections between Graphplan's planning-graph and the dynamic constraint satisfaction problem and motivates the need for adapting CSP search techniques to the Graphplan algorithm. It then describes how explanation based learning, dependency directed backtracking, dynamic variable ordering, forward checking, sticky values and random-restart search strategies can be adapted to Graphplan. Empirical results are provided to demonstrate that these augmentations improve Graphplan's performance significantly (up to 1000x speedups) on several benchmark problems. Special attention is paid to the explanation-based learning and dependency directed backtracking techniques as they are empirically found to be most useful in improving the performance of Graphplan

On the Completeness of Replacing Primitive Actions with Macro-actions and its Generalization to Planning Operators and Macro-operators

Automated planning, which deals with the problem of generating sequences of actions, is an emerging research topic due to its potentially wide range of real-world application domains. As well as developing and improving planning engines, the acquisition of domain-specific knowledge is a promising way to improve the planning process. Domain-specific knowledge can be encoded into the modelling language that a range of planning engines can accept. This makes encoding domain-specific knowledge planner-independent, and entails reformulating the domain models and/or problem specifications. While many encouraging practical results have been derived from such reformulation methods (e.g. learning macro-actions), little attention has been paid to the theoretical properties such as completeness (keeping solvability of reformulated problems). In this paper, we focus on a special case – removing primitive actions replaced by macro-actions. We provide a theoretical study and come up with conditions under which it is safe to remove primitive actions, so completeness of reformulation is preserved. We extend this study also for planning operators (actions are instances of operators)

Planning Graph Heuristics for Belief Space Search

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning

Tools and Algorithms for the Construction and Analysis of Systems

This book is Open Access under a CC BY licence. The LNCS 11427 and 11428 proceedings set constitutes the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. The total of 42 full and 8 short tool demo papers presented in these volumes was carefully reviewed and selected from 164 submissions. The papers are organized in topical sections as follows: Part I: SAT and SMT, SAT solving and theorem proving; verification and analysis; model checking; tool demo; and machine learning. Part II: concurrent and distributed systems; monitoring and runtime verification; hybrid and stochastic systems; synthesis; symbolic verification; and safety and fault-tolerant systems

Generation and exploitation of intermediate goals in automated planning

Mención Internacional en el título de doctorIn automated planning, domain-independent planners often scale poorly. This is due to the exponential blow up of the effort necessary to solve a planning task as its size increases. One of the most popular ways of addressing this problem is splitting the planning problem into several smaller ones. Each subproblem is in theory exponentially easier to solve than the original one, so planners that divide the original task will tend to scale much better. To divide the task into smaller ones, we need to find domain-independent methods to derive intermediate goals. In this thesis we will study different approaches that generate and exploit intermediate goals, without limiting ourselves to simply splitting the original problem. Three main lines of research will be pursued. The first one deals with regression, first tackling its shortcomings and then using it both in bidirectional search and as a way to derive novel heuristics based on intermediate goals. In the second one we propose sampling the search space randomly and using the randomly-sampled subgoals in a tree-like algorithms that effectively balances exploration and exploitation. Finally, in the third one we study the properties of the landmark graph, which represents precedence constraints among subgoals of the task. As a contribution, we propose different characterizations of the landmark graph that improve over its original formulation by providing more information, both formal properties of the task and finer orderings of subgoals exploitable by planners that already use landmarks. ----------------------------------------------------------En planificación automática, los planificadores independientes de dominio a menudo escalan pobremente. Esto se debe a la explosión exponencial del esfuerzo necesario para resolver una tarea de planificación según su tamaño incrementa. Uno de las formas más populares de abordar este problema es dividiendo el problema de planificación en varios problemas más pequeños. Para separar la tarea en tareas más pequeñas, hay que encontrar métodos independientes de dominio capaces de derivar metas intermedias. En esta tesis se estudiarán diferentes aproximaciones que generen y aprovechen metas intermedias, sin limitarnos a una mera subdivisión del problema original. Tres líneas de investigación serán exploradas. La primera trata sobre regresión, primero encarando sus limitaciones y después usándola tanto en búsqueda bidireccional como en nuevas heurísticas basadas en metas intermedias. En la segunda línea proponemos muestrear aleatoriamente el espacio de búsqueda y usar las submetas muestreadas aleatoriamente en un algoritmo basado en árboles aleatorios que balancea exploración y explotación de forma efectiva. Finalmente, en la tercera línea de investigación estudiamos las propiedades del grafo de landmarks, el cual representa las restricciones de precedencia entre submetas de la tarea. Como contribución, proponemos diferentes caracterizaciones del grafo de landmarks que mejoran su formulación original proporcionando más información, tanto propiedades formales de la tarea como ordenaciones de submetas más informadas aprovechables por planificadores que emplean landmarks.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Manuel Molina López.- Secretario: Héctor Geffner.- Vocal: Joerg Hoffman

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

A uniform approach to the complexity and analysis of succinct systems

“ This thesis provides a unifying view on the succinctness of systems: the capability of a modeling formalism to describe the behavior of a system of exponential size using a polynomial syntax. The key theoretical contribution is the introduction of sequential circuit machines as a new universal computation model that focuses on succinctness as the central aspect. The thesis demonstrates that many well-known modeling formalisms such as communicating state machines, linear-time temporal logic, or timed automata exhibit an immediate connection to this machine model. Once a (syntactic) connection is established, many complexity bounds for structurally restricted sequential circuit machines can be transferred to a certain formalism in a uniform manner. As a consequence, besides a far-reaching unification of independent lines of research, we are also able to provide matching complexity bounds for various analysis problems, whose complexities were not known so far. For example, we establish matching lower and upper bounds of the small witness problem and several variants of the bounded synthesis problem for timed automata, a particularly important succinct modeling formalism. Also for timed automata, our complexity-theoretic analysis leads to the identification of tractable fragments of the timed synthesis problem under partial observability. Specifically, we identify timed controller synthesis based on discrete or template-based controllers to be equivalent to model checking. Based on this discovery, we develop a new model checking-based algorithm to efficiently find feasible template instantiations. From a more practical perspective, this thesis also studies the preservation of succinctness in analysis algorithms using symbolic data structures. While efficient techniques exist for specific forms of succinctness considered in isolation, we present a general approach based on abstraction refinement to combine off-the-shelf symbolic data structures. In particular, for handling the combination of concurrency and quantitative timing behavior in networks of timed automata, we report on the tool Synthia which combines binary decision diagrams with difference bound matrices. In a comparison with the timed model checker Uppaal and the timed game solver Tiga running on standard benchmarks from the timed model checking and synthesis domain, respectively, the experimental results clearly demonstrate the effectiveness of our new approach.Diese Dissertation liefert eine vereinheitlichende Sicht auf die Kompaktheit von Systemen: die Fähigkeit eines Modellierungsformalismus, das Verhalten eines Systems exponentieller Größe mit polynomieller Syntax zu beschreiben. Der wesentliche theoretische Beitrag ist die Einführung von sequenziellen Schaltkreis-Maschinen als neues universelles Berechnungsmodell, das sich auf den zentralen Aspekt der Kompaktheit konzentriert. Die Dissertation demonstriert, dass viele bekannte Modellierungsformalismen, wie z.B. kommunizierende Zustandsmaschinen, linear-Zeit temporale Logik (LTL) oder gezeitete Automaten eine direkte Verbindung zu diesem Maschinenmodell aufzeigen. Sobald eine (syntaktische) Verbindung hergestellt ist, können viele Komplexitätsschranken für strukturell beschränkte sequenzielle Schaltkreis-Maschinen für einen bestimmten Formalismus einheitlich übernommen werden. Neben einer weitreichenden Vereinheitlichung unabhängiger Forschungsrichtungen können auch zahlreiche Komplexitätsschranken für Analyse-Probleme etabliert werden, deren genaue Komplexität bisher noch nicht bekannt war. Zum Beispiel werden passende untere und obere Schranken des small witness Problems und mehrere Varianten des Synthese-Problems von Controllern mit beschränkter Größe für gezeitete Automaten bewiesen. Die theoretische Analyse deckt Fragmente geringerer Komplexität des partiell informierten Syntheseproblems für gezeitete Automaten auf. Es wird im Besonderen gezeigt, dass das gezeitete Syntheseproblem für diskrete oder Vorlagen-basierte Controller äquivalent zum Model Checking-Problem ist. Basierend auf dieser Einsicht wird ein neuartiger Model Checking-basierter Algorithmus zur effizienten Synthese von gültigen Instantiierungen von Vorlagen entwickelt. Der praktische Beitrag der Dissertation untersucht die Erhaltung von Kompaktheit in Analyse-Algorithmen durch die Benutzung symbolischer Datenstrukturen. Es wird ein allgemeiner Ansatz zur Kombination von Standard-Datenstrukturen vorgestellt, die jeweils bisher nur in Isolation verwendet werden konnten. Insbesondere wird für die Analyse von Netzwerken von gezeiteten Automaten das Tool Synthia vorgestellt, welches binäre Entscheidungs-Diagramme mit Differenzen-Matrizen verbindet. In einem experimentellen Vergleich mit den Tools Uppaal und Tiga wird klar die Effektivität des neuen Ansatzes belegt

Star-topology decoupled state-space search in AI planning and model checking

State-space search is a widely employed concept in many areas of computer science. The well-known state explosion problem, however, imposes a severe limitation to the effective implementation of search in state spaces that are exponential in the size of a compact system description, which captures the state-transition semantics. Decoupled state-space search, decoupled search for short, is a novel approach to tackle the state explosion. It decomposes the system such that the dependencies between components take the form of a star topology with a center and several leaf components. Decoupled search exploits that the leaves in that topology are conditionally independent. Such independence naturally arises in many kinds of factored model representations, where the overall state space results from the product of several system components. In this work, we introduce decoupled search in the context of artificial intelligence planning and formal verification using model checking. Building on common formalisms, we develop the concept of the decoupled state space and prove its correctness with respect to capturing reachability of the underlying model exactly. This allows us to connect decoupled search to any search algorithm, and, important for planning, adapt any heuristic function to the decoupled state representation. Such heuristics then guide the search towards states that satisfy a desired goal condition. In model checking, we address the problems of verifying safety properties, which express system states that must never occur, and liveness properties, that must hold in any infinite system execution. Many approaches have been proposed in the past to tackle the state explosion problem. Most prominently partial-order reduction, symmetry breaking, Petri-net unfolding, and symbolic state representations. Like decoupled search, all of these are capable of exponentially reducing the search effort, either by pruning part of the state space (the former two), or by representing large state sets compactly (the latter two). For all these techniques, we prove that decoupled search can be exponentially more efficient, confirming that it is indeed a novel concept that exploits model properties in a unique way. Given such orthogonality, we combine decoupled search with several complementary methods. Empirically, we show that decoupled search favourably compares to state-of-the-art planners in common algorithmic planning problems using standard benchmarks. In model checking, decoupled search outperforms well-established tools, both in the context of the verification of safety and liveness properties.Die Zustandsraumsuche ist ein weit verbreitetes Konzept in vielen Bereichen der Informatik, deren effektive Anwendung jedoch durch das Problem der Zustandsexplosion deutlich erschwert wird. Die Zustandsexplosion ist dadurch charakterisiert dass kompakte Systemmodelle exponentiell große Zustandsräume beschreiben. Entkoppelte Zustandsraumsuche (entkoppelte Suche) beschreibt einen neuartigen Ansatz der Zustandsexplosion entgegenzuwirken indem die Struktur des Modells, insbesondere die bedingte Unabhängigkeit von Systemkomponenten in einer Sterntopologie, ausgenutzt wird. Diese Unabhängigkeit ergibt sich bei vielen faktorisierten Modellen deren Zustandsraum sich aus dem Produkt mehrerer Komponenten zusammensetzt. In dieser Arbeit wird die entkoppelte Suche in der Planung, als Teil der Künstlichen Intelligenz, und der Verifikation mittels Modellprüfung eingeführt. In etablierten Formalismen wird das Konzept des entkoppelten Zustandsraums entwickelt und dessen Korrektheit bezüglich der exakten Erfassung der Erreichbarkeit von Modellzuständen bewiesen. Dies ermöglicht die Kombination der entkoppelten Suche mit beliebigen Suchalgorithmen. Wichtig für die Planung ist zudem die Nutzung von Heuristiken, die die Suche zu Zuständen führen, die eine gewünschte Zielbedingung erfüllen, mit der entkoppelten Zustandsdarstellung. Im Teil zur Modellprüfung wird die Verifikation von Sicherheits- sowie Lebendigkeitseigenschaften betrachtet, die unerwünschte Zustände, bzw. Eigenschaften, die bei unendlicher Systemausführung gelten müssen, beschreiben. Es existieren diverse Ansätze um die Zustandsexplosion anzugehen. Am bekanntesten sind die Reduktion partieller Ordnung, Symmetriereduktion, Entfaltung von Petri-Netzen und symbolische Suche. Diese können, wie die entkoppelte Suche, den Suchaufwand exponentiell reduzieren. Dies geschieht durch Beschneidung eines Teils des Zustandsraums, oder durch die kompakte Darstellung großer Zustandsmengen. Für diese Verfahren wird bewiesen, dass die entkoppelte Suche exponentiell effizienter sein kann. Dies belegt dass es sich um ein neuartiges Konzept handelt, das sich auf eigene Art der Modelleigenschaften bedient. Auf Basis dieser Beobachtung werden, mit Ausnahme der Entfaltung, Kombinationen mit entkoppelter Suche entwickelt. Empirisch kann die entkoppelte Suche im Vergleich zu modernen Planern zu deutlichen Vorteilen führen. In der Modellprüfung werden, sowohl bei der Überprüfung von Sicherheit-, als auch Lebendigkeitseigenschaften, etablierte Programme übertroffen.Deutsche Forschungsgesellschaft; Star-Topology Decoupled State Space Searc