Parallel heuristic search in forward partial-order planning

[EN] Most of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.This work has been partially supported by the Spanish MINECO project TIN2014-55637-C2-2-R and cofounded by FEDER.Sapena Vercher, O.; Torreño Lerma, A.; Onaindia De La Rivaherrera, E. (2016). Parallel heuristic search in forward partial-order planning. Knowledge Engineering Review. 31(5):417-428. https://doi.org/10.1017/S0269888916000230S41742831

Accelerating Heuristic Search for AI Planning

AI Planning is an important research field. Heuristic search is the most commonly used method in solving planning problems. Despite recent advances in improving the quality of heuristics and devising better search strategies, the high computational cost of heuristic search remains a barrier that severely limits its application to real world problems. In this dissertation, we propose theories, algorithms and systems to accelerate heuristic search for AI planning. We make four major contributions in this dissertation. First, we propose a state-space reduction method called Stratified Planning to accelerate heuristic search. Stratified Planning can be combined with any heuristic search to prune redundant paths in state space, without sacrificing the optimality and completeness of search algorithms. Second, we propose a general theory for partial order reduction in planning. The proposed theory unifies previous reduction algorithms for planning, and ushers in new partial order reduction algorithms that can further accelerate heuristic search by pruning more nodes in state space than previously proposed algorithms. Third, we study the local structure of state space and propose using random walks to accelerate plateau exploration for heuristic search. We also implement two state-of-the-art planners that perform competitively in the Seventh International Planning Competition. Last, we utilize cloud computing to further accelerate search for planning. We propose a portfolio stochastic search algorithm that takes advantage of the cloud. We also implement a cloud-based planning system to which users can submit planning tasks and make full use of the computational resources provided by the cloud. We push the state of the art in AI planning by developing theories and algorithms that can accelerate heuristic search for planning. We implement state-of-the-art planning systems that have strong speed and quality performance

FLAP: Applying Least-Commitment in Forward-Chaining Planning

In this paper, we present FLAP, a partial-order planner that accurately applies the least-commitment principle that governs traditional partial-order planning. FLAP fully exploits the partial ordering among actions of a plan and hence it solves more problems than other similar approaches. The search engine of FLAP uses a combination of different state-based heuristics and applies a parallel search technique to diversify the search in different directions when a plateau is found. In the experimental evaluation, we compare FLAP with OPTIC, LPG-td and TFD, three state-of-the-art nonlinear planners. The results show that FLAP outperforms these planners in terms of number of problems solved; in addition, the plans of FLAP represent a good trade-off between quality and computational time.This work has been partly supported by the Spanish MICINN under projects Consolider Ingenio 2010 CSD2007-00022 and TIN2011-27652-C03-01, the Valencian Prometeo project II/2013/019.Sapena Vercher, O.; Onaindia De La Rivaherrera, E.; Torreño Lerma, A. (2015). FLAP: Applying Least-Commitment in Forward-Chaining Planning. AI Communications. 28(1):5-20. https://doi.org/10.3233/AIC-140613S52028

Engineering coordination : eine Methodologie für die Koordination von Planungssystemen

Planning problems, like real-world planning and scheduling problems, are complex tasks. As an efficient strategy for handing such problems is the ‘divide and conquer’ strategy has been identified. Each sub problem is then solved independently. Typically the sub problems are solved in a linear way. This approach enables the generation of sub-optimal plans for a number of real world problems. Today, this approach is widely accepted and has been established e.g. in the organizational structure of companies. But existing interdependencies between the sub problems are not sufficiently regarded, as each problem are solved sequentially and no feedback information is given. The field of coordination has been covered by a number of academic fields, like the distributed artificial intelligence, economics or game theory. An important result is, that there exist no method that leads to optimal results in any given coordination problem. Consequently, a suitable coordination mechanism has to be identified for each single coordination problem. Up to now, there exists no process for the selection of a coordination mechanism, neither in the engineering of distributed systems nor in agent oriented software engineering. Within the scope of this work the ECo process is presented, that address exactly this selection problem. The Eco process contains the following five steps. • Modeling of the coordination problem • Defining the coordination requirements • Selection / Design of the coordination mechanism • Implementation • Evaluation Each of these steps is detailed in the thesis. The modeling has to be done to enable a systemic analysis of the coordination problem. Coordination mechanisms have to respect the given situation and the context in which the coordination has to be done. The requirements imposed by the context of the coordination problem are formalized in the coordination requirements. The selection process is driven by these coordination requirements. Using the requirements as a distinction for the selection of a coordination mechanism is a central aspect of this thesis. Additionally these requirements can be used for documentation of design decisions. Therefore, it is reasonable to annotate the coordination mechanisms with the coordination requirements they fulfill and fail to ease the selection process, for a given situation. For that reason we present a new classification scheme for coordination methods within this thesis that classifies existing coordination methods according to a set of criteria that has been identified as important for the distinction between different coordination methods. The implementation phase of the ECo process is supported by the CoPS process and CoPS framework that has been developed within this thesis, as well. The CoPS process structures the design making that has to be done during the implementation phase. The CoPS framework provides a set of basic features software agents need for realizing the selected coordination method. Within the CoPS process techniques are presented for the design and implementation of conversations between agents that can be applied not only within the context of the coordination of planning systems, but for multiagent systems in general. The ECo-CoPS approach has been successfully validated in two case studies from the logistic domain.Reale Planungsprobleme, wie etwa die Produktionsplanung in einer Supply Chain, sind komplex Planungsprobleme. Eine übliche Strategie derart komplexen Problemen zu lösen, ist es diese Probleme in einfachere Teilprobleme zu zerlegen und diese dann separat, meist sequentiell, zu lösen (divide-and-conquer Strategie). Dieser Ansatz erlaubt die Erstellung von (suboptimalen) Plänen für eine Reihe von realen Anwendungen, und ist heute in den Organisationsstrukturen von größeren Unternehmen institutionalisiert worden. Allerdings werden Abhängigkeiten zwischen den Teilproblemen nicht ausreichend berücksichtigt, da die Partialprobleme sequentiell ohne Feedback gelöst werden. Die erstellten Teillösungen müssen deswegen oft nachträglich koordiniert werden. Das Gebiet der Koordination wird in verschiedenen Forschungsgebieten, wie etwa der verteilten Künstlichen Intelligenz, den Wirtschaftswissenschaften oder der Spieltheorie untersucht. Ein zentrales Ergebnis dieser Forschung ist, dass es keinen für alle Situationen geeigneten Koordinationsmechanismus gibt. Es stellt sich also die Aufgabe aus den zahlreichen vorgeschlagenen Koordinationsmechanismen eine Auswahl zu treffen, die für die aktuelle Situation den geeigneten Mechanismus identifiziert. Für die Auswahl eines solchen Mechanismus existiert bisher jedoch kein strukturiertes Verfahren für die Entwicklung von verteilten Systems und insbesondere im Bereich der Agenten orientierter Softwareentwicklung. Im Rahmen dieser Arbeit wird genau hierfür ein Verfahren vorgestellt, der ECo-Prozess. Mit Hilfe dieses Prozesses wird der Auswahlprozess in die folgenden Schritte eingeteilt: • Modellierung der Problemstellung und des relevante Kontextes • Formulierung von Anforderungen an einen Koordinationsmechanismus (coordination requirements) • Auswahl/Entwurf eines Koordinationsmechanismuses • Implementierung des Koordinationsverfahrens • Evaluation des Koordinationsverfahrens Diese Schritte werden im Rahmen der vorliegenden Arbeit detailliert beschrieben. Die Modellierung der Problemstellung stellt dabei den ersten Schritt dar, um die Problemstellung analytisch zugänglich zu machen. Koordinationsverfahren müssen die Gegebenheiten, den Kontext und die Domäne, in der sie angewendet werden sollen hinreichend berücksichtigen um anwendbar zu sein. Dieses kann über Anforderungen an den Koordinationsprozess formalisiert werden. Der von den Anforderungen getrieben Auswahlprozess ist ein Kernstück der hier vorgestellten Arbeit. Durch die Formulierung der Anforderungen und der Annotation eines Koordinationsmechanismus bezüglich der erfüllten und nicht erfüllten Anforderungen werden die Motive für Designentscheidungen dieses Verfahren expliziert. Wenn Koordinationsverfahren anhand dieser Anforderungen klassifiziert werden können, ist es weiterhin möglich den Auswahlprozess (unabhängig vom ECo-Ansatz) zu vereinfachen und zu beschleunigen. Im Rahmen dieser Arbeit wird eine Klassifikation von Koordinationsansätzen anhand von allgemeinen Kriterien vorgestellt, die die Identifikation von geeigneten Kandidaten erleichtern. Diese Kandidaten können dann detaillierter untersucht werden. Dies wurde in den vorgestellten Fallstudien erfolgreich demonstriert. Für die Unterstützung der Implementierung eines Koordinationsansatzes wird in dieser Arbeit zusätzlich der CoPS Prozess vorgeschlagen. Der CoPS Prozess erlaubt einen ganzheitlichen systematischen Ansatz für den Entwurf und die Implementierung eines Koordinationsverfahrens. Unterstürzt wird der CoPS Prozess durch das CoPS Framework, das die Implementierung erleichtert, indem es als eine Plattform mit Basisfunktionalität eines Agenten bereitstellt, der für die Koordination von Planungssystemen verantwortlich ist. Im Rahmen des CoPS Verfahrens werden Techniken für den Entwurf und die Implementierung von Konversation im Kontext des agenten-orientiertem Software Engineerings ausführlich behandelt. Der Entwurf von Konversationen geht dabei weit über Fragestellung der Formatierung von Nachrichten hinaus, wie dies etwa in den FIPA Standards geregelt ist, und ist für die Implementierung von agentenbasierten Systemen im Allgemeinen von Bedeutung. Die Funktionsweise des ECo-CoPS Ansatzes wird anhand von zweierfolgreich durchgeführten Fallstudien aus dem betriebswirtschaftlichen Kontext vorgestellt

Learning for Classical Planning

This thesis is mainly about classical planning for artificial intelligence (AI). In planning, we deal with searching for a sequence of actions that changes the environment from a given initial state to a goal state. Planning problems in general are ones of the hardest problems not only in the area of AI, but in the whole computer science. Even though classical planning problems do not consider many aspects from the real world, their complexity reaches EXPSPACE-completeness. Nevertheless, there exist many planning systems (not only for classical planning) that were developed in the past, mainly thanks to the International Planning Competitions (IPC). Despite the current planning systems are very advanced, we have to boost these systems with additional knowledge provided by learning. In this thesis, we focused on developing learning techniques which produce additional knowledge from the training plans and transform it back into planning do mains and problems. We do not have to modify the planners. The contribution of this thesis is included in three areas. First, we provided theoretical background for plan analysis by investigating action dependencies or independencies. Second, we provided a method for generating macro-operators and removing unnecessary primitive operators. Experimental evaluation of this...Katedra teoretické informatiky a matematické logikyDepartment of Theoretical Computer Science and Mathematical LogicFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Décomposition des problèmes de planification de tâches basée sur les landmarks

The algorithms allowing on-the-fly computation of efficient strategies solving a heterogeneous set of problems has always been one of the greatest challenges faced by research in Artificial Intelligence. To this end, classical planning provides to a system reasoning capacities, in order to help it to interact with its environment autonomously. Given a description of the world current state, the actions the system is able to perform, and the goal it is supposed to reach, a planner can compute an action sequence yielding a state satisfying the predefined goal. The planning problem is usually intractable (PSPACE-hard), however some properties of the problems can be automatically extracted allowing the design of efficient solvers.Firstly, we have developed the Landmark-based Meta Best-First Search (LMBFS) algorithm. Unlike state-of-the-art planners, usually based on state-space heuristic search, LMBFS reenacts landmark-based planning problem decomposition. A landmark is a fluent appearing in each and every solution plan. The LMBFS algorithm splits the global problem in a set of subproblems and tries to find a global solution using the solutions found for these subproblems. Secondly, we have adapted classical planning techniques to enhance the performance of our base algorithm, making LMBFS a competitive planner. Finally, we have tested and compared these methods.Les algorithmes permettant la création de stratégies efficaces pour la résolution d’ensemble de problèmes hétéroclites ont toujours été un des piliers de la recherche en Intelligence Artificielle. Dans cette optique, la planification de tâches a pour objectif de fournir à un système la capacité de raisonner pour interagir avec son environnement de façon autonome afin d’atteindre les buts qui lui ont été assignés. À partir d’une description de l’état initial du monde, des actions que le système peut exécuter, et des buts qu’il doit atteindre, un planificateur calcule une séquence d’actions dont l’exécution permet de faire passer l’état du monde dans lequel évolue le système vers un état qui satisfait les buts qu’on lui a fixés. Le problème de planification est en général difficile à résoudre (PSPACE-difficile), cependant certaines propriétés des problèmes peuvent être automatiquement extraites permettant ainsi une résolution efficace.Dans un premier temps, nous avons développé l’algorithme LMBFS (Landmarkbased Meta Best-First Search). À contre-courant des planificateurs state-of-the-art, basés sur la recherche heuristique dans l’espace d’états, LMBFS est un algorithme qui réactualise la technique de décomposition des problèmes de planification basés sur les landmarks. Un landmark est un fluent qui doit être vrai à un certain moment durant l’exécution de n’importe quel plan solution. L’algorithme LMBFS découpe le problème principal en un ensemble de sous-problèmes et essaie de trouver une solution globale grâce aux solutions trouvées pour ces sous-problèmes. Dans un second temps, nous avons adapté un ensemble de techniques pour améliorer les performances de l’algorithme. Enfin, nous avons testé et comparé chacune de ces méthodes permettant ainsi la création d’un planificateur efficace

Modesty in modelling : on the applicability of interactive planning systems : with a case study in pot plant cultivation

This book is a statement about the applicability of automated support for planning. ]be planning situations considered are those that bear upon the next production cycle of an organization and in which a planner has to allocate 'production goods' to each other. Such situations are for instance tactical planning, production planning, scheduling or timetabling. Drawing from various bodies of literature, a marked position is taken. It is argued that the practical impact of planning systems developed by academics is lower than it could be because formal models play too prominent a part during the development process.Based on views taken from organization theory, psychology and systems theory, a SCanning Aid for Planning SltuationS (SCAPSIS) is developed dig places thew situations on a continuum from 'messy' to 'well-structured'. Not only the problem situation in a strict sense but also the organizational context are taken into account. It is argued that in practice the majority of planning situations are closer to the 'messy' end of the continuum than to the other. The planners deal with these situations as 'open problems', i.e., problems of which they can change the definition at any moment.A planning system for such an open problem will have to grant the user a prominent role. Usability precedes normative support both m importance and in chronology: a user interface familiar to the planner and close to his train of thought is the first and most important element of the system to develop whereas a formal model of the planning problem is definitely not. Such a model, if created at an early stage, can act as a baffler to communication for both parties. As a result, chances are that the resulting system ends up solving the developer's formal problem but not the planner's open one.To come up with a usable system, developer and user must closely cooperate right from the start of a project and aim to make a first usable system as rapidly as possible. Only then, on the basis of experience with this system. can it be considered whether to proceed, for instance by incorporating model-based components in the system.A modelling technique with some promise is the category of interactive heuristics, i.e. heuristics that allow the user to intervene at each iteration. Desirable properties of such heuristics are discussed. It is concluded that this category merits more attention as an element of interactive planning systems.The stance taken towards planning situations in this study is that they am instances of decision making in general, embedded in the functioning of the organization. With this in mind, a tool is created to help evaluate decision support systems. This 'QUality ESTimator' (QUEST) takes into consideration both the process of system development and the resulting system as it functions in the organization.A case study in which a planning system was developed to support cultivation planning at pot plant nurseries serves as an illustration throughout the book. The development of this system is an example of how design ideas that are attractive from an academic developer's point of view can fail to succeed in practice, and how cooperative development and modesty in modelling can lead to a simple but usable system.The ideas expressed in the book can be readily generalized to non-agricultural domains