Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and do- mains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph repre- sentations of planning tasks, we propose a graph neural net- work (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the con- volutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two- stage adaptive scheduling method to further improve the like- lihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at https://github.com/matenure/GNN planner

Machine learning for classical planning : neural network heuristics, online portfolios, and state space topologies

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best- first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work.Viele Alltagsprobleme können mit Hilfe der Zustandsraumsuche gelöst werden. Heuristische Suche, insbesondere die gierige Bestensuche, ist einer der erfolgreichsten Algorithmen für die Zustandsraumsuche. Wir verbessern den aktuellen Stand der Wissenschaft bezüglich heuristischer Suche auf drei Arten. Eine der wichtigsten Komponenten der heuristischen Suche ist die Heuristik. Mit einer guten Heuristik findet die Suche schnell eine Lösung. Eine gute Heuristik für ein Problem zu modellieren ist mühsam. In Teil I präsentieren wir Methoden, um automatisiert gute Heuristiken für ein Problem zu lernen. Hierfür generieren wird die Trainingsdaten mittels Zufallsbewegungen ausgehend von (Teil-) Zuständen des Problems. Wir zeigen, dass die Heuristiken, die wir für einen einzigen Zustandsraum trainieren, oft besser sind als Heuristiken, die für eine Problemklasse trainiert wurden. Weiterhin zeigen wir, dass die Qualität aller trainierten Heuristiken je nach Problemklasse stark variiert, keine Heuristik eine andere dominiert, und es nicht vorher erkennbar ist, ob eine trainierte Heuristik gut funktioniert. Wir stellen fest, dass in fast allen getesteten Problemklassen die modellbasierte Suchalgorithmen den trainierten Heuristiken überlegen sind. Lediglich in der Storage Problemklasse sind unsere Heuristiken überlegen. Oft ist es unklar, welche Heuristik oder Suchalgorithmus man für ein Problem nutzen sollte. Daher trainieren wir online Portfolios, die für ein gegebenes Problem den besten Algorithmus vorherzusagen. Die Eingabe für das online Portfolio sind bisher immer von Menschen ausgewählte Eigenschaften des Problems. In Teil II präsentieren wir neue online Portfolios, die das gesamte Problem als Eingabe bekommen. Darüber hinaus können unsere online Portfolios ihre Entscheidung einmal korrigieren. Beide Änderungen verbessern die Qualität von online Portfolios erheblich. Weiterhin zeigen wir, dass wir auch gute online Portfolios mit erklärbaren Techniken des maschinellen Lernens trainieren können. Selbst wenn wir den besten Algorithmus für ein Problem auswählen, kann es sein, dass das Problem zu schwierig ist, um in akzeptabler Zeit gelöst zu werden. In Teil III zeigen wir, wie wir von dem Verhalten einer gierigen Bestensuche auf einfachen Problemen ihr Verhalten auf schwierigeren Problemen der gleichen Problemklasse vorhersagen können. Dieses Wissen nutzen wir, um die Suche zu verbessern. Zuerst zeigen wir, wie man Fortschrittszustände erkennt. Immer wenn gierige Bestensuche einen Fortschrittszustand expandiert, wissen wir, dass es nie wieder einen Zustand mit gleichem oder höheren heuristischen Wert expandieren wird.Wir präsentieren zwei Methoden, die diesesWissen verwenden. Aufbauend auf dieser Arbeit lernen wir von einem Problem, wie man jegliches Problem der gleichen Problemklasse in eine Reihe von einfacheren Suchen aufteilen kann

Machine Learning for Classical Planning: Neural Network Heuristics, Online Portfolios, and State Space Topologies

State space search solves navigation tasks and many other real world problems. Heuristic search, especially greedy best-first search, is one of the most successful algorithms for state space search. We improve the state of the art in heuristic search in three directions. In Part I, we present methods to train neural networks as powerful heuristics for a given state space. We present a universal approach to generate training data using random walks from a (partial) state. We demonstrate that our heuristics trained for a specific task are often better than heuristics trained for a whole domain. We show that the performance of all trained heuristics is highly complementary. There is no clear pattern, which trained heuristic to prefer for a specific task. In general, model-based planners still outperform planners with trained heuristics. But our approaches exceed the model-based algorithms in the Storage domain. To our knowledge, only once before in the Spanner domain, a learning-based planner exceeded the state-of-the-art model-based planners. A priori, it is unknown whether a heuristic, or in the more general case a planner, performs well on a task. Hence, we trained online portfolios to select the best planner for a task. Today, all online portfolios are based on handcrafted features. In Part II, we present new online portfolios based on neural networks, which receive the complete task as input, and not just a few handcrafted features. Additionally, our portfolios can reconsider their choices. Both extensions greatly improve the state-of-the-art of online portfolios. Finally, we show that explainable machine learning techniques, as the alternative to neural networks, are also good online portfolios. Additionally, we present methods to improve our trust in their predictions. Even if we select the best search algorithm, we cannot solve some tasks in reasonable time. We can speed up the search if we know how it behaves in the future. In Part III, we inspect the behavior of greedy best-first search with a fixed heuristic on simple tasks of a domain to learn its behavior for any task of the same domain. Once greedy best-first search expanded a progress state, it expands only states with lower heuristic values. We learn to identify progress states and present two methods to exploit this knowledge. Building upon this, we extract the bench transition system of a task and generalize it in such a way that we can apply it to any task of the same domain. We can use this generalized bench transition system to split a task into a sequence of simpler searches. In all three research directions, we contribute new approaches and insights to the state of the art, and we indicate interesting topics for future work

Choosing a Classical Planner with Graph Neural Networks

Online planner selection is the task of choosing a solver out of a predefined set for a given planning problem. As planning is computationally hard, the performance of solvers varies greatly on planning problems. Thus, the ability to predict their performance on a given problem is of great importance. While a variety of learning methods have been employed, for classical cost-optimal planning the prevailing approach uses Graph Neural Networks (GNNs). In this work, we continue the line of work on using GNNs for online planner selection. We perform a thorough investigation of the impact of the chosen GNN model, graph representation and node features, as well as prediction task. Going further, we propose using the graph representation obtained by a GNN as an input to the Extreme Gradient Boosting (XGBoost) model, resulting in a more resource-efficient yet accurate approach. We show the effectiveness of a variety of GNN-based online planner selection methods, opening up new exciting avenues for research on online planner selection

Towards learning domain-independent planning heuristics

Automated planning remains one of the most general paradigms in Artificial Intelligence, providing means of solving problems coming from a wide variety of domains. One of the key factors restricting the applicability of planning is its computational complexity resulting from exponentially large search spaces. Heuristic approaches are necessary to solve all but the simplest problems. In this work, we explore the possibility of obtaining domain-independent heuristic functions using machine learning. This is a part of a wider research program whose objective is to improve practical applicability of planning in systems for which the planning domains evolve at run time. The challenge is therefore the learning of (corrections of) domain-independent heuristics that can be reused across different planning domains.Comment: Accepted for the IJCAI-17 Workshop on Architectures for Generality and Autonom

합성곱 신경망의 효율적인 실행을 위한 실행 계획 자동 생성

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. Bernhard Egger.Over the past years, a large number of architectures and accelerators for Deep Neural Networks (DNNs) have been proposed. While exhibiting common features, the number and arrangement of processing elements, the sizes and types of on-chip memory, and the possibilities of parallel execution vary significantly especially in the embedded system domain. The number of off-chip memory accesses and the performance of a DNN on a given accelerator depends not only on the supported computational patterns and the available on-chip memory but also on the sizes and shapes of each layer. Finding a computational pattern that minimizes off-chip memory accesses while maximizing performance is thus a tedious and error-prone task. This thesis presents e-PlaNNer, a compiler framework that generates an optimized execution plan for a given embedded accelerator and Convolutional Neural Network (CNN). For each layer, e-PlaNNer determines the performance-optimal configuration by considering the data movement, tiling, and work distribution. The generated execution plan is transformed to code, allowing for a fast development cycle with different CNNs and hardware accelerators. Evaluated with five neural networks under varying memory configurations and compared to previous works on the Nvidia Jetson TX2, e-PlaNNer achieves 6x speedup and 21.14% reduction of off-chip memory access volume on average. In addition, e-PlaNNer shows meaningful performance compared to well-known deep learning frameworks in terms of end-to-end execution.지난 몇 년간 심층신경망을 위한 수많은 아키텍처와 가속기가 제안되었다. 이를 통해, 일반적인 심층신경망 수행 방식들이 함께 제안되었으나, 구체적인 연산 배치 방식과 온칩 메모리의 크기 및 종류, 그리고 병렬 실행 방식은 특히 내장형 시스템에서 다양하게 나타날 수 있다. 뿐만 아니라, 오프칩 메모리 접근 크기 및 신경망의 성능은 연산 형태 및 온칩 메모리의 크기 뿐 아니라 신경망 각 계층의 크기 및 형태에 따라서 달라질 수 있다. 따라서, 최대 성능을 내면서 오프칩 메모리 접근을 최소화하는 연산 형태를 일일이 찾는 것은 상당히 번거로운 작업이며, 많은 오류를 발생 시킬 수 있다. 본 논문에서 소개할 e-PlaNNer는 주어진 내장형 하드웨어 가속기와 합성곱 신경망에 대하여 최적화된 실행 계획을 생성해주는 컴파일러 프레임워크이다. e-PlaNNer는 심층신경망의 각 신경망 계층에 대하여 데이터 이동, 타일링, 그리고 작업 배분을 고려한 성능 최적화된 실행 계획을 결정한다. 또한, 생성된 실행 계획을 실제 컴파일 가능한 코드로 변환함으로써, 서로 다른 다양한 합성곱 신경망과 하드웨어 가속기에 대하여 빠른 개발 주기를 제공한다. 다양한 메모리 구성으로 다섯 가지 합성곱 신경망 응용을 Nvidia의 Jetson TX2 에서 검증하여 기존의 연구와 비교한 결과, e-PlaNNer는 평균적으로 6배의 성능 향상과 21.14% 의 오프칩 메모리 데이터 접근량 감소를 보였다. 뿐만 아니라, e-PlaNNer는 전체 심층신경망의 실행 관점에서 기존에 잘 알려진 딥러닝 프레임워크와의 비교에서도 의미있는 결과를 보였다.Chapter 1 Introduction 1 Chapter 2 Related Work 5 Chapter 3 Background 8 3.1 Convolutional Neural Networks 8 3.2 DNN Accelerator 9 3.3 Roofline Model 11 Chapter 4 Graph Level Processing 13 4.1 Graph Construction 13 4.2 Schedule Caching 14 Chapter 5 Convolutional Layer Analysis 15 5.1 Loop Structure 16 5.2 Loop Tiling 17 5.3 Dataflow 18 Chapter 6 Execution Planning 20 6.1 Architecture Con figurations 20 6.2 Modeling Off-Chip Memory Accesses 22 6.3 Modeling Performance 24 6.4 Search Space Exploration 25 Chapter 7 Code Generation 32 7.1 Intermediate Representation 33 7.2 Target Code Generation 34 Chapter 8 Evaluation 36 8.1 Experimental Setup 36 8.2 Performance Results 39 8.3 Comparison of Off-chip Memory Access 40 8.4 Framework Results 42 Chapter 9 Discussion 46 Chapter 10 Conclusion 47 Bibliography 48 요약 57Maste

Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services

Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, traditional model serving paradigms usually resort to the cloud by fully uploading geo-distributed input data to remote datacenters. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse and dynamic resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environments. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.Comment: Accepted by IEEE/ACM Transactions on Networkin