Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning

We propose Stable Yet Memory Bounded Open-Loop (SYMBOL) planning, a general memory bounded approach to partially observable open-loop planning. SYMBOL maintains an adaptive stack of Thompson Sampling bandits, whose size is bounded by the planning horizon and can be automatically adapted according to the underlying domain without any prior domain knowledge beyond a generative model. We empirically test SYMBOL in four large POMDP benchmark problems to demonstrate its effectiveness and robustness w.r.t. the choice of hyperparameters and evaluate its adaptive memory consumption. We also compare its performance with other open-loop planning algorithms and POMCP.Comment: Accepted at IJCAI 2019. arXiv admin note: substantial text overlap with arXiv:1905.0402

Emergence and resilience in multi-agent reinforcement learning

Our world represents an enormous multi-agent system (MAS), consisting of a plethora of agents that make decisions under uncertainty to achieve certain goals. The interaction of agents constantly affects our world in various ways, leading to the emergence of interesting phenomena like life forms and civilizations that can last for many years while withstanding various kinds of disturbances. Building artificial MAS that are able to adapt and survive similarly to natural MAS is a major goal in artificial intelligence as a wide range of potential real-world applications like autonomous driving, multi-robot warehouses, and cyber-physical production systems can be straightforwardly modeled as MAS. Multi-agent reinforcement learning (MARL) is a promising approach to build such systems which has achieved remarkable progress in recent years. However, state-of-the-art MARL commonly assumes very idealized conditions to optimize performance in best-case scenarios while neglecting further aspects that are relevant to the real world. In this thesis, we address emergence and resilience in MARL which are important aspects to build artificial MAS that adapt and survive as effectively as natural MAS do. We first focus on emergent cooperation from local interaction of self-interested agents and introduce a peer incentivization approach based on mutual acknowledgments. We then propose to exploit emergent phenomena to further improve coordination in large cooperative MAS via decentralized planning or hierarchical value function factorization. To maintain multi-agent coordination in the presence of partial changes similar to classic distributed systems, we present adversarial methods to improve and evaluate resilience in MARL. Finally, we briefly cover a selection of further topics that are relevant to advance MARL towards real-world applicability.Unsere Welt stellt ein riesiges Multiagentensystem (MAS) dar, welches aus einer Vielzahl von Agenten besteht, die unter Unsicherheit Entscheidungen treffen müssen, um bestimmte Ziele zu erreichen. Die Interaktion der Agenten beeinflusst unsere Welt stets auf unterschiedliche Art und Weise, wodurch interessante emergente Phänomene wie beispielsweise Lebensformen und Zivilisationen entstehen, die über viele Jahre Bestand haben und dabei unterschiedliche Arten von Störungen überwinden können. Die Entwicklung von künstlichen MAS, die ähnlich anpassungs- und überlebensfähig wie natürliche MAS sind, ist eines der Hauptziele in der künstlichen Intelligenz, da viele potentielle Anwendungen wie zum Beispiel das autonome Fahren, die multi-robotergesteuerte Verwaltung von Lagerhallen oder der Betrieb von cyber-phyischen Produktionssystemen, direkt als MAS formuliert werden können. Multi-Agent Reinforcement Learning (MARL) ist ein vielversprechender Ansatz, mit dem in den letzten Jahren bemerkenswerte Fortschritte erzielt wurden, um solche Systeme zu entwickeln. Allerdings geht der Stand der Forschung aktuell von sehr idealisierten Annahmen aus, um die Effektivität ausschließlich für Szenarien im besten Fall zu optimieren. Dabei werden weiterführende Aspekte, die für die echte Welt relevant sind, größtenteils außer Acht gelassen. In dieser Arbeit werden die Aspekte Emergenz und Resilienz in MARL betrachtet, welche wichtig für die Entwicklung von anpassungs- und überlebensfähigen künstlichen MAS sind. Es wird zunächst die Entstehung von emergenter Kooperation durch lokale Interaktion von selbstinteressierten Agenten untersucht. Dazu wird ein Ansatz zur Peer-Incentivierung vorgestellt, welcher auf gegenseitiger Anerkennung basiert. Anschließend werden Ansätze zur Nutzung emergenter Phänomene für die Koordinationsverbesserung in großen kooperativen MAS präsentiert, die dezentrale Planungsverfahren oder hierarchische Faktorisierung von Evaluationsfunktionen nutzen. Zur Aufrechterhaltung der Multiagentenkoordination bei partiellen Veränderungen, ähnlich wie in klassischen verteilten Systemen, werden Methoden des Adversarial Learning vorgestellt, um die Resilienz in MARL zu verbessern und zu evaluieren. Abschließend wird kurz eine Auswahl von weiteren Themen behandelt, die für die Einsatzfähigkeit von MARL in der echten Welt relevant sind

Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search

Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively optimized by destroying and repairing a fixed number of parts, i.e., the neighborhood, of the solution, using randomized destroy heuristics and prioritized planning. Despite their recent success in various MAPF instances, current LNS-based approaches lack exploration and flexibility due to greedy optimization with a fixed neighborhood size which can lead to low quality solutions in general. So far, these limitations have been addressed with extensive prior effort in tuning or offline machine learning beyond actual planning. In this paper, we focus on online learning in LNS and propose Bandit-based Adaptive LArge Neighborhood search Combined with Exploration (BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the selection of destroy heuristics and neighborhood sizes on the fly during search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and empirically demonstrate cost improvements of at least 50% compared to state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson Sampling performs particularly well compared to alternative multi-armed bandit algorithms.Comment: Accepted to AAAI 202

Leveraging Statistical Multi-Agent Online Planning with Emergent Value Function Approximation

Making decisions is a great challenge in distributed autonomous environments due to enormous state spaces and uncertainty. Many online planning algorithms rely on statistical sampling to avoid searching the whole state space, while still being able to make acceptable decisions. However, planning often has to be performed under strict computational constraints making online planning in multi-agent systems highly limited, which could lead to poor system performance, especially in stochastic domains. In this paper, we propose Emergent Value function Approximation for Distributed Environments (EVADE), an approach to integrate global experience into multi-agent online planning in stochastic domains to consider global effects during local planning. For this purpose, a value function is approximated online based on the emergent system behaviour by using methods of reinforcement learning. We empirically evaluated EVADE with two statistical multi-agent online planning algorithms in a highly complex and stochastic smart factory environment, where multiple agents need to process various items at a shared set of machines. Our experiments show that EVADE can effectively improve the performance of multi-agent online planning while offering efficiency w.r.t. the breadth and depth of the planning process.Comment: Accepted at AAMAS 201

Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Submicron Systems Architecture Project: Semiannual Technical Report

No abstract available

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

Radical Artificial Intelligence: A Postmodern Approach

The dynamic response of end-clamped monolithic beams and sandwich beams has been measured by loading the beams at mid-span using metal foam projectiles. The AISI 304 stainless-steel sandwich beams comprise two identical face sheets and either prismatic Y-frame or corrugated cores. The resistance to shock loading is quantified by the permanent transverse deflection at mid-span of the beams as a function of projectile momentum. The prismatic cores are aligned either longitudinally along the beam length or transversely. It is found that the sandwich beams with a longitudinal core orientation have a higher shock resistance than the monolithic beams of equal mass. In contrast, the performance of the sandwich beams with a transverse core orientation is very similar to that of the monolithic beams. Three-dimensional finite element (FE) simulations are in good agreement with the measured responses. The FE calculations indicate that strain concentrations in the sandwich beams occur at joints within the cores and between the core and face sheets; the level of maximum strain is similar for the Y-frame and corrugated core beams for a given value of projectile momentum. The experimental and FE results taken together reveal that Y-frame and corrugated core sandwich beams of equal mass have similar dynamic performances in terms of rear-face deflection, degree of core compression and level of strain within the beam