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

Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

We establish a novel relation between delete-free planning, an important task for the AI Planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.Comment: Paper presented at the 39th International Conference on Logic Programming (ICLP 2023), 14 page

Improving delete relaxation heuristics through explicitly represented conjunctions

Heuristic functions based on the delete relaxation compute upper and lower bounds on the optimal delete-relaxation heuristic h+, and are of paramount importance in both optimal and satisficing planning. Here we introduce a principled and flexible technique for improving h+, by augmenting delete-relaxed planning tasks with a limited amount of delete information. This is done by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task, rendering h+ the perfect heuristic h* in the limit. Previous work has introduced a method in which the growth of the task is potentially exponential in the number of conjunctions introduced. We formulate an alternative technique relying on conditional effects, limiting the growth of the task to be linear in this number. We show that this method still renders h+ the perfect heuristic h* in the limit. We propose techniques to find an informative set of conjunctions to be introduced in different settings, and analyze and extend existing methods for lower-bounding and upper-bounding h + in the presence of conditional effects. We evaluate the resulting heuristic functions empirically on a set of IPC benchmarks, and show that they are sometimes much more informative than standard delete-relaxation heuristics

Improving the efficiency of the Pre-Optimization Plan Techniques

Automated planning is an important research area of Artificial Intelligence (AI). In classical planning, which is a sub-area of automated planning, attention is given to ‘agile’ planning, i.e., solving planning problems as quickly as possible regardless of the quality of solution plans. Obtaining solutions quickly is important for real-time applications as well as in situations of imminent danger. Post-planning optimisation techniques for improving the quality of solution plans are a good option for improving poor quality plans. Since such techniques are run as post-processing, this avoids situations where there is a risk of not having solution plans in time. This thesis focuses on an important sub-area of post-planning optimisation; that is, on identifying and removing redundant actions from solution plans. In particular, this study extends the existing Action Elimination and Greedy Action Elimination algorithms by introduce two approaches to improve their efficiency. The AE and GAE algorithms are thereby developed into the UAIAE and UGAIAE systems respectively. The key to our approaches is based on optimise the process while keeping the same elimination power’ (identifying and removing the same number of redundant actions). First approach improves the algorithms by considering situations where inverse actions are redundant, while the other identifies a subset of actions that cannot be present in any redundant actions set. This subset is named justified unique actions. The study’s approach to identifying this subset has been motivated by a promising heuristic approach called ‘landmarks’, which are facts or actions that cannot be eliminated to achieve the goal. The approaches in this study have been empirically evaluated using several benchmark domains, as well as several planning engines that participated in the Agile track of the International Planning Competition 2014. In addition, they have been evaluated against state-of-the-art optimal and satisficing planners, as well as they are evaluated against a plan repair technique. The methods of AE family can be understood as polynomial methods that improve the quality of a plan by removing redundant actions, or as tools to complement more sophisticated plan optimisation techniques

Introdução ao controle adaptativo a eventos discretos em base de dados RFID com planejador automático aplicado a sistemas robóticos

Industry 4.0 technologies integrate devices and data, bringing flexibility and efficiency, derived from decentralization of information sources and processing, which is fundamental to further advance applications. This work aims to introduce Cyber Physical Systems (CPS) working with decision affecting passive objects, a system with distributed data, and automatic planners, to achieve a self-sufficient process manipulator that does not require external goal insertion and can self-adjust given an exception, in real-time. This solution is more flexible and autonomous than state machines. Applying the technologies introduced in Industry 4.0 and methods that were previously treated separately, such as symbolic artificial intelligence and robot kinematics, the system can perform perception, planning and actuation processes. This system is capable of extracting information inside passive passive entities in the physical domain by using Radio Frequency IDentification (RFID) to acquire predicates, data, about each object current and objective states using the Predicate inside RFID Database (PRD) tool. This data is treated to produce a domain snapshot, by joining distributed information and generating a problem definition, through the Grouped Individual State Predicates (GISP) methodology. This problem definition may then be fed into a planning module, implemented on an Edge or Cloud server, where discrete-action and trajectory planning are concatenated to output control references, using a generic symbolic planner and a numeric trajectory generator. Then, the active agent may actuate, verify for exceptions and update the passive objects information if the obtained state is perceived with no exceptions, else it must reiterate to satisfy the global goal. This work structures the adaptive discrete event control architecture with a RFID database containing parts of predicate logic.Pesquisa sem auxílio de agências de fomentoTrabalho de Conclusão de Curso (Graduação)As tecnologias da indústria 4.0 integram dispositivos e dados, trazendo flexibilidade e eficiência, derivada da decentralização das fontes de informação e processamento, que é fundamental para o avanço das aplicações. Esse trabalho busca introduzir sistemas ciber físicos trabalhando com objetos passivos com capacidade de afetar decisões, um sistema com informação distribuida, e planejadores automáticos, para alcançar um manipulador de processos autossuficiente que não requer inserção externa de objetivos e que pode auto-ajustar-se de acordo com exceções, em tempo de execução. Essa é uma solução mais flexível e autônoma que o uso de máquinas de estado. Aplicando as tecnologias introduzidas na Indústria 4.0 e métodos que eram tratados em separado, como inteligência artificial simbólica e cinemática de robôs, o sistema pode realizar os processos de percepção, planejamento e atuação. Esse sistema é capaz de extrair informação de entidades passivas no domínio físico utilizando Identificação por Rádio Frequência (RFID) para adquirir predicados, dados, sobre os estados corrente e objetivo de cada objeto através da ferramenta PRD (Predicados dentro de base de Dados RFID). Esses dados são tratados para produzir um retrato do domínio, através da união da informação distribuída e produção de uma definição de problema usando a metodologia GISP (Predicados de Estado Individual Agrupados). Essa definição de problema pode ser alimentada no módulo de planejamento, implementado num servidor local ou na nuvem, onde planemento de ações discretas e trajetória são concatenados para retornar referências de controle, usando um planejador simbólico genérico e um gerador numérico de trajetória. Então, o agente ativo pode atuar, verificar exceções e atualizar a informação nos objetos passivos caso o estado obtido seja percebido livre de exceções, senão deve reiterar até que se satisfaça o objetivo global. Esse trabalho estrutura a arquitetura de controle adaptativa a eventos discretos com base de dados RFID contendo partes de lógica de predicados

Efficient Probabilistic Reasoning Using Partial State-Space Exploration

Planning, namely the ability of an autonomous agent to make decisions leading towards a certain goal, is one of the fundamental components of intelligent behavior. In the face of uncertainty, this problem is typically modeled as a Markov Decision Process (MDP). The MDP framework is highly expressive, and has been used in a variety of applications, such as mobile robots, flow assignment in heterogeneous networks, optimizing software in mobile phones, and aircraft collision avoidance. However, its wide adoption in real-world scenarios is still impaired by the complexity of solving large MDPs. Developing effective ways to tackle this complexity barrier is a challenging research problem. This thesis focuses on the development of scalable and robust MDP solution approaches for partially exploring the state space of an MDP. The main contribution is a series of mathematical and algorithmic techniques for selecting the parts of the state space that are the most critical for effective planning, with the ultimate goal of maximizing performance in the presence of bounded resources. The proposed approaches work on two distinct axes: i) constructing reduced MDP models that are computationally easier to solve, but whose policies still result in near-optimal performance when applied to the original model, and ii) using sampling-based exploration that is biased towards states for which additional computation can be more productive, in a well-defined sense. The first part of the thesis addresses the model reduction component, introducing an MDP reduction framework that generalizes popular solution approaches based on determinization. In particular, the framework encompasses a spectrum of MDP reductions differing along two dimensions: i) the number of outcomes per state-action pair that are fully accounted for, and ii) the number of occurrences of the remaining, exceptional, outcomes that are planned for in advance. An important insight resulting from this work is that the choice of reduction is crucial for achieving good performance, an issue under-explored by the planning community, even for determinization-based planners. The second part of the thesis presents a sampling-based approach that does not require modification of the MDP model. The key idea is to avoid computation in states whose estimated optimal values are more likely to be correct, and rather direct it towards states whose values (which are closely related to policy quality) can be improved the most. The proposed approach represents a novel algorithmic framework that generalizes MDP algorithms based on labeling, a widely used technique in state-of-the-art planners. The framework can be leveraged to create a variety of MDP solvers with different trade-offs between computational complexity and policy quality, and its application to a variety of standard MDP benchmarks results in state-of-the-art performance

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