Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br> SAT-Based Planning

In Verification and in (optimal) AI Planning, a successful method is to formulate the application as boolean satisfiability (SAT), and solve it with state-of-the-art DPLL-based procedures. There is a lack of understanding of why this works so well. Focussing on the Planning context, we identify a form of problem structure concerned with the symmetrical or asymmetrical nature of the cost of achieving the individual planning goals. We quantify this sort of structure with a simple numeric parameter called AsymRatio, ranging between 0 and 1. We run experiments in 10 benchmark domains from the International Planning Competitions since 2000; we show that AsymRatio is a good indicator of SAT solver performance in 8 of these domains. We then examine carefully crafted synthetic planning domains that allow control of the amount of structure, and that are clean enough for a rigorous analysis of the combinatorial search space. The domains are parameterized by size, and by the amount of structure. The CNFs we examine are unsatisfiable, encoding one planning step less than the length of the optimal plan. We prove upper and lower bounds on the size of the best possible DPLL refutations, under different settings of the amount of structure, as a function of size. We also identify the best possible sets of branching variables (backdoors). With minimum AsymRatio, we prove exponential lower bounds, and identify minimal backdoors of size linear in the number of variables. With maximum AsymRatio, we identify logarithmic DPLL refutations (and backdoors), showing a doubly exponential gap between the two structural extreme cases. The reasons for this behavior -- the proof arguments -- illuminate the prototypical patterns of structure causing the empirical behavior observed in the competition benchmarks

On the predictability of domain-independent temporal planners

Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real-world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state-of-the-art temporal planners by introducing a new set of temporal-specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real-world applications.</p

Discrete Planning

This chapter provides introductory concepts that serve as an entry point into other parts of the book. The planning problems considered here are the simplest to describe because the state space will be finite in most cases. When it is not finite, it will at least be countably infinite (i.e., a unique integer may be assigned to every state). Therefore, no geometric models or differential equations will be needed to characterize the discrete planning problems. Furthermore, no forms of uncertainty will be considered, which avoids complications such as probability theory. All models are completely known and predictable. There are three main parts to this chapter. Sections 2.1 and 2.2 define and present search methods for feasible planning, in which the only concern is to reach a goal state. The search methods will be used throughout the book in numerous other contexts, including motion planning in continuous state spaces. Following feasible planning, Section 2.3 addresses the problem of optimal planning. The principle of optimality, or the dynamic programming principle, [1] provides a key insight that greatly reduces the computation effort in many planning algorithms

Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4

In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4

What You Always Wanted to Know about the Deterministic Part of IPC 2014 (But Were too Afraid to Ask)

The International Planning Competition (IPC) is a prominent event of the AI planning community that has been organised since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning, and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques. This paper focuses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future

Étude comparative de planificateurs appliqués au domaine des jeux vidéo

RÉSUMÉ L’utilisation de la planification dans une architecture de prise de décision d’un jeu vidéo est une technique récente et il existe peu de références démontrant son efficacité par rapport aux techniques usuelles. Notre étude consiste en une évaluation expérimentale de deux planificateurs, GOAP et HTN, dans l’élaboration d’agents autonomes dans un jeu de tir. L’objectif du projet de recherche est de déterminer s’il est avantageux ou non d’utiliser ces planificateurs selon les critères suivants : la qualité de l’agent, la qualité de la planification, la jouabilité et certains attributs de qualité non fonctionnels tels que les limitations comportementales,la facilité d’implémentation et la robustesse face aux changements de conception. Nous avons comparé les deux types de planificateurs à une technique usuelle, la machine à états finis. Les résultats obtenus montrent que les architectures utilisant la planification offrent une qualité d’agent supérieure à la machine à états finis, nécessitent en moyenne moins de temps de calcul, ne nécessitent pas de prévoir toutes les situations auxquelles l’agent fera face et sont plus robustes aux changements de conception. Toutefois, elles résultent en un agent moins réactif et l’implémentation de l’architecture GOAP est une tâche plus complexe que l’implémentation de la machine à états. Finalement, GOAP offre une qualité de l’agent légèrement supérieure à HTN, mais ce dernier est plus facile d’implémentation. En définitive, sans toutefois être sans inconvénient, les planificateurs possèdent des avantages à être utilisés dans une architecture de prise de décision d’un jeu de tir. Quant au planificateur le plus approprié, le choix devrait être réalisé en fonction des exigences spécifiques du projet.----------ABSTRACT The use of planning in a decision making architecture of a video game is a recent technique and there are few references demonstrating its effectiveness compared to conventional techniques. Our study provides an experimental evaluation of two planners, GOAP and HTN, in the development of autonomous agents in a shooting game. The objective of the project is to determine whether it is advantageous or not to use these planners based on the following criteria: quality of the agent, quality of planning, gameplay and some non-functional quality attributes such as behavioral limitations, ease of implementation and robustness to design changes. We compared the two types of planners to a conventional technique, the finite state machine. The results show that the planning architectures offer a superior quality of agent than the finite state machine, require less computing time, do not require to anticipate all situations that the agent will face and are more robust to changes in design. However, they result in a less reactive agent and the implementation of the GOAP architecture is a more complex task than implementing the state machine. Finally, GOAP provides a slightly superior agent quality than HTN, but the latter is easier to implement. Ultimately, though not without downsides, planners have advantages for use in a decision making architecture of a shooter. As for the most appropriate planner, the choice should be made according to specific project requirements

Dienstekomposition in intelligenten Umgebungen basierend auf KI-Planung

In intelligenten Umgebungen wird das Zusammenspiel mehrerer Dienste benötigt, welches durch eine Dienstekomposition erzielt werden kann. KIPlanung ist eine Methode, dies umzusetzen. Im Rahmen der vorliegenden Arbeit wurde experimentell das Laufzeitverhalten von verschiedenen Planern untersucht. Daneben wurden die Möglichkeiten der Modellierung von Problemen der Dienstekomposition evaluiert, was zu einer Richtline für die verteilte Modellierung von Dienstbeschreibungen führte. Basierend auf den Erfahrungen wurde ein Composer entworfen und umgesetzt, der verschiedene Planer nutzen kann

A Critical Assessment of Benchmark Comparison in Planning

Recent trends in planning research have led to empirical comparison becoming commonplace. The field has started to settle..

A Critical Assessment of Benchmark Comparison in Planning

Recent trends in planning research have led to empirical comparison becoming commonplace. The fiel