A well-studied abstract model for navigating agents in a distributed environment without collisions is known as Multi-Agent Path Finding (MAPF). MAPF has two interrelated aspects: one is to find the paths for the agents without collisions, and the other is to optimize the conflict resolutions between the paths. We focus on the first aspect, and we investigate three options: map reduction with waypoints, prioritized search and one-way restrictions. To our knowledge, our map reduction technique is a novelty in MAPF. We evaluate the three speed-up options on the scenarios of the League of Robot Runners competition. The results show that the improvement greatly depends on the size, style and layout of the maps. Prioritized search does not have big impact. The usage of one-way restriction rules is only worthwhile in specific maps. Map reduction on larger, complex maps drastically improves performance.

The general expectation is that the traffic in the cities will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. Each member of the collective of autonomous vehicles tries to adapt to the changing environment, therefore together they execute decentralised autonomous adaptation by exploiting real-time information about their environment. The routing of these vehicles needs proper computer science models to be able to develop the best information technology for their control. We review different traffic flow models in computer science, and we evaluate their usefulness and applicability to autonomous vehicles. The classical game theory model implies flow level decision making in route selection. Non-cooperative autonomous vehicles may produce unwanted traffic patterns. Improved decentralised autonomous adaptation techniques try to establish some kind of coordination among autonomous vehicles, mainly through intention awareness. The aggregation of the intentions of autonomous vehicles may help to predict future traffic situations. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The review helps to make the first steps towards research on global level control of autonomous vehicles by highlighting the strengths and weaknesses of the different formal models. The review also highlights the importance of research on intention-awareness and intention-aware traffic flow prediction methods.

In recent years, many warehouses applied mobile robots to move products from one location to another. We focus on a traditional warehouse where agents are humans, and they are engaged with tasks to navigate to the next destination one after the other. The possible destinations are determined at the beginning of the daily shift. Our real-world warehouse client asked us to minimize the total wage cost, and to minimize the irritation of the workers because of conflicts in their tasks. We define a heuristic for the optimizations for splitting the orders into warehouse carts, defining the sequence of the products within the carts, and the assignment of the carts to workers. We extend Multi-Agent Path Finding (MAPF) solution techniques. Furthermore, we have implemented our proposal in a simulation software, and we have run several experiments. According to the experiments, the make-span and the wage cost cannot be reduced with the heuristic optimization, however the heuristic optimization considerably reduces the irritation of the workers. We conclude our work with a guideline for the warehouse.

In recent years, many warehouses applied mobile robots to move products from one location to another. We focus on a traditional warehouse where agents are humans and they are engaged with tasks to navigate to the next destination one after the other. The possible destinations are determined at the beginning of the daily shift. Our real-world warehouse client asked us to minimise the total wage cost, and to minimise the irritation of the workers because of conflicts in their tasks. We extend Multi-Agent Path Finding (MAPF) solution techniques. We define a heuristic optimisation for the assignment of the packages. We have implemented our proposal in a simulation software and we have run several experiments. According to the experiments, the make-span and the wage cost cannot be reduced with the heuristic optimisation, however the heuristic optimisation considerably reduces the irritation of the workers. We conclude our work with a guideline for the warehouse.

There are different models of the routing problem. We are building a test environment, where the decision making methods of the different models can be evaluated in almost real traffic. The almost real traffic runs in a well known simulation platform. The route selections are injected into the simulation platform, and the simulation platform drives the vehicles. We demonstrate how the routing model evaluator can be run to evaluate a routing model against a dynamic equilibrium.

We expect that the traffic will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. The route selection plays an important role in optimizing the traffic. There are different models of the routing problem. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The models are important, but their applicability in real life needs further investigations. We are building a test environment, where the decision making methods of the different models can be evaluated in almost real traffic. The almost real traffic runs in a well known simulation platform. The simulation platform also provides tools to calculate a dynamic equilibrium traffic assignment. The calculation needs long time and a lot of computing resources. The routing model evaluator contains an implementation of the routing model which determines the routes for the vehicles. The route selections are injected into the simulation platform, and the simulation platform drives the vehicles. The first results of the investigations with the routing model evaluator show that the route selection of the intention-aware routing model will be able to bring the traffic close to a dynamic equilibrium in real time.

It is hoped that the traffic in the cities will be almost optimal when autonomous vehicles will dominate the traffic. We investigate the route selection of autonomous vehicles. We extend, implement and apply a formal model to support the trustworthy route selection of real-world autonomous agents. We trust a model, if the route selection strategy of the model selects routes which are close to the possible fastest all the time. The formal model extends the intention-aware online routing game model with parallel lanes, traffic lights and give way intersections. These extensions are needed for real-world applications. The actual parameters of the formal model are derived from real-world OpenStreetMap data. The large-scale real-world testing of the model uses the SUMO (Simulation of Urban MObility) open source simulator. The implemented intention-aware online routing game model can execute the route selection for each vehicle faster than real-time. Our hypothesis is that the extended intention-aware online routing game model produces at least as good traffic as the dynamic equilibrium route assignment. This hypothesis is confirmed in a real-world scenario.

Cyber physical systems open new ground in the automotive domain. Autonomous vehicles will try to adapt to the changing environment, and decentralized adaptation is a new type of issue that needs to be studied. This article investigates the effects of adaptive route planning when real-time online traffic information is exploited. Simulation results show that if the agents selfishly optimize their actions, then in some situations, the cyber physical system may fluctuate and sometimes the agents may be worse off with real-time data than without real-time data. The proposed solution to this problem is to use anticipatory techniques, where the future state of the environment is predicted from the intentions of the agents. This article concludes with this conjecture: if simultaneous decision-making is prevented, then intention-aware prediction can limit the fluctuation and help the cyber physical system converge to the Nash equilibrium, assuming that the incoming traffic can be predicted.

In order to ensure global behaviour of decentralized multi-agent systems, we have to have a clear understanding of the issue of equilibrium over time. Convergence to the static equilibrium is an important question in the evolutionary dynamics of multi-agent systems. The evolutionary dynamics is usually investigated in repeated games which capture the evolutionary dynamics between games. The evolutionary dynamics within a game is investigated in online routing games. It is not known if online routing games converge to the static equilibrium or not. The progress beyond the state-of-the-art is that we introduce the notion of intertemporal equilibrium in the study of the evolutionary dynamics of games, we define quantitative values to measure the intertemporal equilibrium, we use these quantitative values to evaluate a realistic scenario, and we give an insight into the influence of intertemporal expectations of the agents on the intertemporal equilibrium. An interesting result is that the prediction service, which is engineered into the environment of the multi-agent system as a novel type of coordination artifact, greatly influences the global behaviour of the multi-agent system. The main contribution of our work is a better understanding of the engineering process of the intertemporal behaviour of multi-agent systems.

In order to ensure global behaviour of decentralized multiagent systems, we have to have a clear understanding of the issue of equilibrium over time. The evolutionary dynamics within a game is investigated in online routing games. The progress beyond the state-of-theart is that we introduce the notion of intertemporal equilibrium in the study of the global behaviour of decentralized multi-agent systems, we define quantitative values to measure the intertemporal equilibrium, we use these quantitative values to evaluate a realistic scenario, and we give an insight into the influence of the designed intertemporal expectations of the agents on the global behaviour of decentralized multi-agent systems. An interesting result is that if the multi-agent system is designed in a way that agents have less precise knowledge of the future, then it leads to better global behaviour of the multi-agent system.

The objective of this work is the mechanical characterization of materials produced by 3D printing based on Fused Deposition Modelling (FDM®).The materials chosen are various poly(lactic acid) (PLA) bases reinforced with another material (e. g. glass fiber, metal powder, ….) in different weight fractions.In view of the FDM technique, producing specimens layer by layer and following predefined orientations, the main assumption considered is that the materials behave similarly to laminates formed by orthotropic layers.Great emphasis must be put on the selection of the appropriate quality filaments, therefore first the material properties of the fibers were examined.Following tensile strength tests, scanning electron microscopy (SEM) was employed to observe fracture surfaces.It was clear from the microstructure of the filaments that the morphology of the fibers are material dependent.This difference as well as the diverse types of the fibers explains the variability in material properties among the test materials examined.

Ubiquitous IoT systems open new ground in the automotive domain. With the advent of autonomous vehicles, there will be several actors that adapt to changes in traffic, and decentralized adaptation will be a new type of issue that needs to be studied. This chapter investigates the effects of adaptive route planning when real-time online traffic information is exploited. Simulation results show that if the agents selfishly optimize their actions, then in some situations the ubiquitous IoT system may fluctuate and the agents may be worse off with real-time data than without real-time data. The proposed solution to this problem is to use anticipatory techniques, where the future state of the environment is predicted from the intentions of the agents. This chapter concludes with this conjecture: if simultaneous decision making is prevented, then intention-propagation-based prediction can limit the fluctuation and help the ubiquitous IoT system converge to the Nash equilibrium.

Intention-aware prediction is regarded as an important agreement technology to help large amount of agents in aligning their activities towards an equilibrium. If the agents do not align their activities in online routing games, then the multi-agent system is not guaranteed to get to a stable equilibrium. We formally define two intention-aware prediction methods for online routing games and empirically evaluate them in a real-world scenario. The experiments confirm that the defined intention-aware routing strategies limit the fluctuation in this online routing game scenario and make the system more or less converge to the equilibrium.

The agents of a distributed adaptive system perceive the current state of their environment and make decisions which action to perform. The actions are both reactive and proactive. Reactivity can be supported by the availability of real-time data and proactivity can be supported by anticipatory techniques. Recent investigations proved that if the agents use selfish strategy, then in some situations sometimes the system maybe worst off with real-time data than without real-time data, even if anticipatory techniques are applied to predict the future state of the environment. This study investigates that version of the Braess paradox, where each subsequent agent of the flow may select a different route, using real-time data and anticipatory techniques. The authors contribute to the state-of-the-art by proving that the traffic distribution in this Braess paradox approximates the Nash equilibrium.

The collective of autonomous cars is expected to generate almost optimal traffic. In this position paper we discuss the multi-agent models and the verification results of the collective behaviour of autonomous cars. We argue that non-cooperative autonomous adaptation cannot guarantee optimal behaviour. The conjecture is that intention aware adaptation with a constraint on simultaneous decision making has the potential to avoid unwanted behaviour. The online routing game model is expected to be the basis to formally prove this conjecture.

In an earlier paper Rakonczai et al. (2014), we have emphasized the effective sample size for autocorrelated data. The simulations were based on the block bootstrap methodology. However, the discreteness of the usual block size did not allow for exact calculations. In this paper we propose a generalisation of the block bootstrap methodology, relate it to the existing optimisation procedures and apply it to a temperature data set. Our other focus is on statistical tests, where quite often the actual sample size plays an important role, even in case of relatively large samples. This is especially the case for copulas. These are used for investigating the dependencies among data sets. As in quite a few real applications the time dependence cannot be neglected, we investigated the effect of this phenomenon to the used test statistic. The critical values can be computed by the proposed new block bootstrap simulation, where the block sizes are determined e.g. by fitting a VAR model to the observations. The results are illustrated for models of the used temperature data.

With the online routing game model we can investigate the online routing problem, where each subsequent agent of the traffic flow may select different route based on real-time data. Recent investigations proved that if the agents of such system use the selfish shortest path search strategy, then in some situations sometimes the multi-agent system may be worse off with real-time data than without real-time data, even if anticipatory techniques are applied to predict the future state of the environment. We investigate the online Braess paradox, where each subsequent agent of the traffic flow may select different route, using anticipatory techniques.

The routing problem in traffic networks is a prominent problem of distributed adaptive systems. The online routing game model is used to investigate the routing problem, when each subsequent agent of the traffic flow may select different route based on real-time data. Recent investigations proved that if the agents of such system use shortest path search strategy, then in some situations sometimes the multi-agent system may be worse off with real-time data than without real-time data. This paper contributes to the state-of-the-art by proving the guaranteed worst case benefit of online real-time data in the Braess paradox where each subsequent agent of the traffic flow may select different route using anticipatory techniques.

This paper presents applications of the peaks-over-threshold methodology for both the univariate and the recently introduced bivariate case, combined with a novel bootstrap approach. We compare the proposed bootstrap methods to the more traditional profile likelihood. We have investigated 63 years of the European Climate Assessment daily precipitation data for five Hungarian grid points, first separately for the summer and winter months, then aiming at the detection of possible changes by investigating 20 years moving windows. We show that significant changes can be observed both in the univariate and the bivariate cases, the most recent period being the most dangerous in several cases, as some return values have increased substantially. We illustrate these effects by bivariate coverage regions.

It is widely believed that road traffic as a whole self-adapts to the current situation to make travel times shorter, if the navigation devices exploit real-time traffic information. A novel theoretical approach to study this belief is the online routing game model. This chapter describes the model of online routing games in order to be able to determine how we can measure and prove the benefits of online real-time data in navigation systems. Three different notions of the benefit of online data and two classes of online routing games are defined. The class of simple naive online routing games represents the current commercial car navigation systems. Simple naive online routing games may have undesirable properties: stability is not guaranteed, single flow intensification may be possible and the worst case benefit of online data may be bigger than one, i.e. it may be a "price". One of the approaches to avoid such problems of car navigation is intention propagation where agents share their intention and can forecast future travel times. The class of simple naive intention propagation online routing games represents the navigation systems that use shortest path planning based on forecast future travel times. In spite of exploiting intention propagation in online routing games, single flow intensification may be possible, the traffic may fluctuate and the worst case benefit may be bigger than one. These theoretical investigations point out issues that need to be solved by future research on decision strategies for self-adapting traffic flows with autonomous navigation.