AI Communications最新文献

{"title":"Policy adaptation for vehicle routing","authors":"T. Cazenave, J. Lucas, T. Triboulet, Hyoseok Kim","doi":"10.3233/aic-201577","DOIUrl":"https://doi.org/10.3233/aic-201577","url":null,"abstract":"Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm that learns a playout policy in order to solve a single player game. In this paper we apply NRPA to the vehicle routing problem. This problem is important for large companies that have to manage a fleet of vehicles on a daily basis. Real problems are often too large to be solved exactly. The algorithm is applied to standard problem of the literature and to the specific problems of EDF (Electricité De France, the main French electric utility company). These specific problems have peculiar constraints. NRPA gives better result than the algorithm previously used by EDF.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89676269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning vs. rule-based adaptive traffic signal control: A Fourier basis linear function approximation for traffic signal control","authors":"Theresa Ziemke, L. N. Alegre, A. Bazzan","doi":"10.3233/aic-201580","DOIUrl":"https://doi.org/10.3233/aic-201580","url":null,"abstract":"Reinforcement learning is an efficient, widely used machine learning technique that performs well when the state and action spaces have a reasonable size. This is rarely the case regarding control-related problems, as for instance controlling traffic signals. Here, the state space can be very large. In order to deal with the curse of dimensionality, a rough discretization of such space can be employed. However, this is effective just up to a certain point. A way to mitigate this is to use techniques that generalize the state space such as function approximation. In this paper, a linear function approximation is used. Specifically, SARSA ( λ ) with Fourier basis features is implemented to control traffic signals in the agent-based transport simulation MATSim. The results are compared not only to trivial controllers such as fixed-time, but also to state-of-the-art rule-based adaptive methods. It is concluded that SARSA ( λ ) with Fourier basis features is able to outperform such methods, especially in scenarios with varying traffic demands or unexpected events.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74977315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety perception and pedestrian dynamics: Experimental results towards affective agents modeling","authors":"F. Gasparini, Marta Giltri, S. Bandini","doi":"10.3233/AIC-201576","DOIUrl":"https://doi.org/10.3233/AIC-201576","url":null,"abstract":"The modeling of a new generation of agent-based simulation systems supporting pedestrian and crowd management taking into account affective states represents a new research frontier. Pedestrian behaviour involves human perception processes, based on subjective and psychological aspects. Following the concept of pedestrian environmental awareness, each walker adapts his/her crossing behaviour according to environmental conditions and his/her perception of safety. Different pedestrian behaviours can be related to subjective mobility and readiness to respond, and these factors are strongly dependent on the subjective interaction with the environment. Having additional inputs about pedestrian behaviour related to their perception processes could be useful in order to develop a more representative pedestrian dynamic model. In particular, the subjective perception of the safeness of crossing should be taken into consideration. In order to focus on the pedestrians’ perception of safe road crossing and walking, an experiment in an uncontrolled urban scenario has been carried out. Besides more conventional self-assessment questionnaires, physiological responses have been considered to evaluate the affective state of pedestrians during the interaction with the urban environment. Results from the analysis of the collected data show that physiological responses are reliable indicators of safety perception while road crossing and interacting with real urban environment, suggesting the design of agent-based models for pedestrian dynamics simulations taking in account the representation of affective states.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86918536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agriculture fleet vehicle routing: A decentralised and dynamic problem","authors":"Marin Lujak, E. Sklar, F. Semet","doi":"10.3233/aic-201581","DOIUrl":"https://doi.org/10.3233/aic-201581","url":null,"abstract":"To date, the research on agriculture vehicles in general and Agriculture Mobile Robots (AMRs) in particular has focused on a single vehicle (robot) and its agriculture-specific capabilities. Very little work has explored the coordination of fleets of such vehicles in the daily execution of farming tasks. This is especially the case when considering overall fleet performance, its efficiency and scalability in the context of highly automated agriculture vehicles that perform tasks throughout multiple fields potentially owned by different farmers and/or enterprises. The potential impact of automating AMR fleet coordination on commercial agriculture is immense. Major conglomerates with large and heterogeneous fleets of agriculture vehicles could operate on huge land areas without human operators to effect precision farming. In this paper, we propose the Agriculture Fleet Vehicle Routing Problem (AF-VRP) which, to the best of our knowledge, differs from any other version of the Vehicle Routing Problem studied so far. We focus on the dynamic and decentralised version of this problem applicable in environments involving multiple agriculture machinery and farm owners where concepts of fairness and equity must be considered. Such a problem combines three related problems: the dynamic assignment problem, the dynamic 3-index assignment problem and the capacitated arc routing problem. We review the state-of-the-art and categorise solution approaches as centralised, distributed and decentralised, based on the underlining decision-making context. Finally, we discuss open challenges in applying distributed and decentralised coordination approaches to this problem.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83291653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ORNInA: A decentralized, auction-based multi-agent coordination in ODT systems","authors":"Alaa Daoud, Flavien Balbo, Paolo Gianessi, Gauthier Picard","doi":"10.3233/aic-201579","DOIUrl":"https://doi.org/10.3233/aic-201579","url":null,"abstract":"On-Demand Transport (ODT) systems have attracted increasing attention in recent years. Traditional centralized dispatching can achieve optimal solutions, but NP-Hard complexity makes it unsuitable for online and dynamic problems. Centralized and decentralized heuristics can achieve fast, feasible solution at run-time with no guarantee on the quality. Starting from a feasible not optimal solution, we present in this paper a new solution model (ORNInA) consisting of two parallel coordination processes. The first one is a decentralized insertion-heuristic based algorithm to build vehicle schedules in order to solve a particular case of the dynamic Dial-A-Ride-Problem (DARP) as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V) and make decentralized decisions. The second coordination scheme is a continuous optimization process namely Pull-demand protocol, based on combinatorial auctions, in order to improve the quality of the global solution achieved by decentralized decision at run-time by exchanging resources between vehicles (k-opt). In its simplest implementation, k is set to 1 so that vehicles can exchange only one resource at a time. We evaluate and analyze the promising results of our contributed techniques on synthetic data for taxis operating in Saint-Etienne city, against a classical decentralized greedy approach and a centralized one that uses a classical mixed-integer linear program (MILP) solver.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86287858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 10th IJCAR automated theorem proving system competition - CASC-J10","authors":"G. Sutcliffe","doi":"10.3233/aic-201566","DOIUrl":"https://doi.org/10.3233/aic-201566","url":null,"abstract":"The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic Automated Theorem Proving (ATP) systems. CASC-J10 was the twenty-fifth competition in the CASC series. Twenty-four ATP systems and system variants competed in the various competition divisions. This paper presents an outline of the competition design, and a commentated summary of the results.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77801861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating route choice learning with experience sharing in a commuting scenario: An agent-based approach","authors":"Franziska Klügl-Frohnmeyer, A. Bazzan","doi":"10.3233/aic-201582","DOIUrl":"https://doi.org/10.3233/aic-201582","url":null,"abstract":"Navigation apps have become more and more popular, as they give information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, people tend to learn and adapt to different situations. What if the experience gained from such a learning task is shared via an app? In this paper, we analyse the effects that adaptive driver agents cause on the overall network, when those agents share their aggregated experience about route choice in a reinforcement learning setup. In particular, in this investigation, Q-learning is used and drivers share what they have learnt about the system, not just information about their current travel times. Using a classical commuting scenario, we show that experience sharing can improve convergence times that underlie a typical learning task. Further, we analyse individual learning dynamics to get an impression how aggregate and individual dynamics are related to each other. Based on that interesting pattern of individual learning dynamics can be observed that would otherwise be hidden in an only aggregate analysis.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90432263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demand-responsive rebalancing zone generation for reinforcement learning-based on-demand mobility","authors":"A. Castagna, Maxime Guériau, G. Vizzari, Ivana Dusparic","doi":"10.3233/AIC-201575","DOIUrl":"https://doi.org/10.3233/AIC-201575","url":null,"abstract":"Enabling Ride-sharing (RS) in Mobility-on-demand (MoD) systems allows reduction in vehicle fleet size while preserving the level of service. This, however, requires an efficient vehicle to request assignment, and a vehicle rebalancing strategy, which counteracts the uneven geographical spread of demand and relocates unoccupied vehicles to the areas of higher demand. Existing research into rebalancing generally divides the coverage area into predefined geographical zones. Division is done statically, at design-time, impeding adaptivity to evolving demand patterns. To enable more accurate dynamic rebalancing, this paper proposes a Dynamic Demand-Responsive Rebalancer (D2R2) for RS systems. D2R2 uses Expectation-Maximization (EM) technique to recalculate zones at each decision step based on current demand. We integrate D2R2 with a Deep Reinforcement Learning multi-agent MoD system consisting of 200 vehicles serving 10,000 trips from New York taxi dataset. Results show a more fair workload division across the fleet when compared to static pre-defined equiprobable zones.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79562024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drone-assisted automated plant diseases identification using spiking deep conventional neural learning","authors":"K. Demir, Vedat Tümen","doi":"10.3233/aic-210009","DOIUrl":"https://doi.org/10.3233/aic-210009","url":null,"abstract":"Detection and diagnosis of the plant diseases in the early stage significantly minimize yield losses. Image-based automated plant diseases identification (APDI) tools have started to been widely used in pest managements strategies. The current APDI systems rely on images captured in laboratory conditions, which hardens the usage of the APDI systems by smallholder farmers. In this study, we investigate whether the smallholder farmers can exploit APDI systems using their basic and cheap unmanned autonomous vehicles (UAVs) with standard cameras. To create the tomato images like the one taken by UAVs, we build a new dataset from a public dataset by using image processing tools. The dataset includes tomato leaf photographs separated into 10 classes (diseases or healthy). To detect the diseases, we develop a new hybrid detection model, called SpikingTomaNet, which merges a novel deep convolutional neural network model with spiking neural network (SNN) model. This hybrid model provides both better accuracy rates for the plant diseases identification and more energy efficiency for the battery-constrained UAVs due to the SNN’s event-driven architecture. In this hybrid model, the features extracted from the CNN model are used as the input layer for SNNs. To assess our approach’s performance, firstly, we compare the proposed CNN model inside the developed hybrid model with well-known AlexNet, VggNet-5 and LeNet models. Secondly, we compare the developed hybrid model with three hybrid models composed of combinations of the well-known models and SNN model. To train and test the proposed neural network, 32022 images in the dataset are exploited. The results show that the SNN method significantly increases the success, especially in the augmented dataset. The experiment result shows that while the proposed hybrid model provides 97.78% accuracy on original images, its success on the created datasets is between 59.97%–82.98%. In addition, the results shows that the proposed hybrid model provides better overall accuracy in the classification of the diseases in comparison to the well-known models and LeNet and their combination with SNN.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77175651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An approach for outlier and novelty detection for text data based on classifier confidence","authors":"N. Pizurica, S. Tomovic","doi":"10.3233/aic-200649","DOIUrl":"https://doi.org/10.3233/aic-200649","url":null,"abstract":"In this paper we present an approach for novelty detection in text data. The approach can also be considered as semi-supervised anomaly detection because it operates with the training dataset containing labelled instances for the known classes only. During the training phase the classification model is learned. It is assumed that at least two known classes exist in the available training dataset. In the testing phase instances are classified as normal or anomalous based on the classifier confidence. In other words, if the classifier cannot assign any of the known class labels to the given instance with sufficiently high confidence (probability), the instance will be declared as novelty (anomaly). We propose two procedures to objectively measure the classifier confidence. Experimental results show that the proposed approach is comparable to methods known in the literature.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79415826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}