Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00093
Aymeric Blot, H. Hoos, Marie-Éléonore Kessaci, Laetitia Vermeulen-Jourdan
Multi-objective optimisation algorithms expose various parameters that have to be tuned in order to be efficient. Moreover, in multi-objective optimisation, the correlation between objective functions is known to affect search space structure and algorithm performance. Considering the recent success of automatic algorithm configuration (AAC) techniques for the design of multi-objective optimisation algorithms, this raises two interesting questions: what is the impact of correlation between optimisation objectives on (1) the efficacy of different AAC approaches and (2) on the optimised algorithm designs obtained from these automated approaches? In this work, we study these questions for multi-objective local search algorithms (MOLS) for three well-known bi-objective permutation problems, using two single-objective AAC approaches and one multi-objective approach. Our empirical results clearly show that overall, multi-objective AAC is the most effective approach for the automatic configuration of the highly parametric MOLS framework, and that there is no systematic impact of the degree of correlation on the relative performance of the three AAC approaches. We also find that the best-performing configurations differ, depending on the correlation between objectives and the size of the problem instances to be solved, providing further evidence for the usefulness of automatic configuration of multi-objective optimisation algorithms.
{"title":"Automatic Configuration of Bi-Objective Optimisation Algorithms: Impact of Correlation Between Objectives","authors":"Aymeric Blot, H. Hoos, Marie-Éléonore Kessaci, Laetitia Vermeulen-Jourdan","doi":"10.1109/ICTAI.2018.00093","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00093","url":null,"abstract":"Multi-objective optimisation algorithms expose various parameters that have to be tuned in order to be efficient. Moreover, in multi-objective optimisation, the correlation between objective functions is known to affect search space structure and algorithm performance. Considering the recent success of automatic algorithm configuration (AAC) techniques for the design of multi-objective optimisation algorithms, this raises two interesting questions: what is the impact of correlation between optimisation objectives on (1) the efficacy of different AAC approaches and (2) on the optimised algorithm designs obtained from these automated approaches? In this work, we study these questions for multi-objective local search algorithms (MOLS) for three well-known bi-objective permutation problems, using two single-objective AAC approaches and one multi-objective approach. Our empirical results clearly show that overall, multi-objective AAC is the most effective approach for the automatic configuration of the highly parametric MOLS framework, and that there is no systematic impact of the degree of correlation on the relative performance of the three AAC approaches. We also find that the best-performing configurations differ, depending on the correlation between objectives and the size of the problem instances to be solved, providing further evidence for the usefulness of automatic configuration of multi-objective optimisation algorithms.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116251134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web robots constitute nowadays more than half of the total web traffic. Malicious robots threaten the security, privacy and performance of the web, while non-malicious ones are involved in analytics skewing. The latter constitutes an important problem for large websites with unique content, as it can lead to false impressions about the popularity and impact of a piece of information. To deal with this problem, we present a novel web robot detection approach for content-rich websites, based on the assumption that human web users are interested in specific topics, while web robots crawl the web randomly. Our approach extends the typical representation of user sessions with a novel set of features that capture the semantics of the content of the requested resources. Empirical results on real-world data from the web portal of an academic publisher, show that the proposed semantic features lead to improved web robot detection accuracy.
{"title":"Web Robot Detection: A Semantic Approach","authors":"Athanasios Lagopoulos, Grigorios Tsoumakas, Georgios Papadopoulos","doi":"10.1109/ICTAI.2018.00150","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00150","url":null,"abstract":"Web robots constitute nowadays more than half of the total web traffic. Malicious robots threaten the security, privacy and performance of the web, while non-malicious ones are involved in analytics skewing. The latter constitutes an important problem for large websites with unique content, as it can lead to false impressions about the popularity and impact of a piece of information. To deal with this problem, we present a novel web robot detection approach for content-rich websites, based on the assumption that human web users are interested in specific topics, while web robots crawl the web randomly. Our approach extends the typical representation of user sessions with a novel set of features that capture the semantics of the content of the requested resources. Empirical results on real-world data from the web portal of an academic publisher, show that the proposed semantic features lead to improved web robot detection accuracy.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00057
V. Parque, T. Miyashita
Optimal topologies in networked systems is of relevant interest to integrate and coordinate multi-agency. Our interest in this paper is to compute the root location and the topology of minimal-length tree layouts given n nodes in a polygonal map, assuming an n-star network topology. Computational experiments involving 600 minimal tree planning scenarios show the feasibility and efficiency of the proposed approach.
{"title":"Obstacle-Avoiding Euclidean Steiner Trees by n-Star Bundles","authors":"V. Parque, T. Miyashita","doi":"10.1109/ICTAI.2018.00057","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00057","url":null,"abstract":"Optimal topologies in networked systems is of relevant interest to integrate and coordinate multi-agency. Our interest in this paper is to compute the root location and the topology of minimal-length tree layouts given n nodes in a polygonal map, assuming an n-star network topology. Computational experiments involving 600 minimal tree planning scenarios show the feasibility and efficiency of the proposed approach.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"344 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124246239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00132
Julien Salotti, S. Fenet, Romain Billot, Nour-Eddin El Faouzi, C. Solnon
In the context of Connected and Smart Cities, the need to predict short term traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, there is however still no clear view of the requirements involved in network-wide traffic forecasting. In this paper, the ability of several state-of-the-art methods to forecast the traffic flow at each road segment is studied. Some of the multivariate methods use the information of all sensors to predict traffic at a specific location, whereas some others rely on the selection of a suitable subset. In addition to classical methods, this paper studies the advantage of learning this subset by using a new variable selection algorithm based on time series graphical models and information theory. This method has already been successfully used in natural science applications with similar goals, but not in the traffic community. A contribution is to evaluate all these methods on two real-world datasets with different characteristics and to compare the forecasting ability of each method in both contexts. The first dataset describes the traffic flow in the city center of Lyon (France), which exhibits complex patterns due to the network structure and urban traffic dynamics. The second dataset describes inter-urban freeway traffic on the outskirts of the French city of Marseille. Experimental results validate the need for variable selection mechanisms and illustrate the complementarity of forecasting algorithms depending on the type of road and the forecasting horizon.
{"title":"Comparison of Traffic Forecasting Methods in Urban and Suburban Context","authors":"Julien Salotti, S. Fenet, Romain Billot, Nour-Eddin El Faouzi, C. Solnon","doi":"10.1109/ICTAI.2018.00132","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00132","url":null,"abstract":"In the context of Connected and Smart Cities, the need to predict short term traffic conditions has led to the development of a large variety of forecasting algorithms. In spite of various research efforts, there is however still no clear view of the requirements involved in network-wide traffic forecasting. In this paper, the ability of several state-of-the-art methods to forecast the traffic flow at each road segment is studied. Some of the multivariate methods use the information of all sensors to predict traffic at a specific location, whereas some others rely on the selection of a suitable subset. In addition to classical methods, this paper studies the advantage of learning this subset by using a new variable selection algorithm based on time series graphical models and information theory. This method has already been successfully used in natural science applications with similar goals, but not in the traffic community. A contribution is to evaluate all these methods on two real-world datasets with different characteristics and to compare the forecasting ability of each method in both contexts. The first dataset describes the traffic flow in the city center of Lyon (France), which exhibits complex patterns due to the network structure and urban traffic dynamics. The second dataset describes inter-urban freeway traffic on the outskirts of the French city of Marseille. Experimental results validate the need for variable selection mechanisms and illustrate the complementarity of forecasting algorithms depending on the type of road and the forecasting horizon.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129393898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00125
T. Khaled, B. Benhamou, P. Siegel
In this work, we introduce a new method for searching stable models of logical programs. This method is based on a relatively new semantics that has not been exploited yet. This semantics captures and extends that one of the stable models (Gelfond et al., 1988) and offers a new alternative to implement ASP solvers. The proposed method performs a DPLL enumerative process that is adapted to Answer Set Programming (ASP) framework according to the used semantics. This method has the advantage to use a Horn clause representation having the same size as the input logic program has constant spatial complexity. It avoids the workload induced by the loop management from which suffer most of the ASP solvers based on the Clark completion. Moreover, the enumeration is done on a restricted set of literals called the strong back-door (STB) of the considered logic program. This reduces the algorithm time complexity which is in theory a function of the size of the STB set. We also introduced new inference rules that the method uses to prune its search tree and hence reduces its size in practice. We implemented the proposed method and applied it to enumerate the stable models of some combinatorial problems. The method is compared to other known systems and the obtained results show that our approach is a good alternative for designing ASP solvers.
本文提出了一种寻找逻辑规划稳定模型的新方法。这种方法基于一种尚未被利用的相对较新的语义。这种语义捕获并扩展了一种稳定模型(Gelfond et al.， 1988)，并为实现ASP求解器提供了一种新的选择。该方法根据所使用的语义执行适合于答案集编程(ASP)框架的DPLL枚举过程。该方法的优点是使用与输入逻辑程序具有恒定空间复杂度的大小相同的Horn子句表示。它避免了大多数基于Clark完井的ASP求解器由于循环管理而带来的工作量。此外，枚举是在被考虑的逻辑程序的一组被称为强后门(STB)的受限字面值上完成的。这降低了算法的时间复杂度，这在理论上是STB集大小的函数。我们还引入了新的推理规则，该方法使用该规则来修剪其搜索树，从而在实践中减小其大小。我们实现了该方法，并将其应用于若干组合问题的稳定模型枚举。将该方法与其他已知系统进行了比较，结果表明该方法是设计ASP求解器的一个很好的选择。
{"title":"A New Method for Computing Stable Models in Logic Programming","authors":"T. Khaled, B. Benhamou, P. Siegel","doi":"10.1109/ICTAI.2018.00125","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00125","url":null,"abstract":"In this work, we introduce a new method for searching stable models of logical programs. This method is based on a relatively new semantics that has not been exploited yet. This semantics captures and extends that one of the stable models (Gelfond et al., 1988) and offers a new alternative to implement ASP solvers. The proposed method performs a DPLL enumerative process that is adapted to Answer Set Programming (ASP) framework according to the used semantics. This method has the advantage to use a Horn clause representation having the same size as the input logic program has constant spatial complexity. It avoids the workload induced by the loop management from which suffer most of the ASP solvers based on the Clark completion. Moreover, the enumeration is done on a restricted set of literals called the strong back-door (STB) of the considered logic program. This reduces the algorithm time complexity which is in theory a function of the size of the STB set. We also introduced new inference rules that the method uses to prune its search tree and hence reduces its size in practice. We implemented the proposed method and applied it to enumerate the stable models of some combinatorial problems. The method is compared to other known systems and the obtained results show that our approach is a good alternative for designing ASP solvers.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129610061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Long short-term memory (LSTM) network is an effective model architecture for deep learning approaches to sequence modeling tasks. However, the current LSTMs can't use the property of sequential data when dealing with the sequence components, which last for a certain period of time. This may make the model unable to benefit from the inherent characteristics of time series and result in poor performance as well as lower efficiency. In this paper, we present a novel adaptive LSTM for durative sequential data which exploits the temporal continuance of the input data in designing a new LSTM unit. By adding a new mask gate and maintaining span, the cell's memory update is not only determined by the input data but also affected by its duration. An adaptive memory update method is proposed according to the change of the sequence input at each time step. This breaks the limitation that the cells calculate the cell state and hidden output for each input always in a unified manner, making the model more suitable for processing the sequences with continuous data. The experimental results on various sequence training tasks show that under the same iteration epochs, the proposed method can achieve higher accuracy, but need relatively less training time compared with the standard LSTM architecture.
{"title":"ALSTM: Adaptive LSTM for Durative Sequential Data","authors":"Dejiao Niu, Zheng Xia, Yawen Liu, Tao Cai, Tianquan Liu, Yongzhao Zhan","doi":"10.1109/ICTAI.2018.00032","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00032","url":null,"abstract":"Long short-term memory (LSTM) network is an effective model architecture for deep learning approaches to sequence modeling tasks. However, the current LSTMs can't use the property of sequential data when dealing with the sequence components, which last for a certain period of time. This may make the model unable to benefit from the inherent characteristics of time series and result in poor performance as well as lower efficiency. In this paper, we present a novel adaptive LSTM for durative sequential data which exploits the temporal continuance of the input data in designing a new LSTM unit. By adding a new mask gate and maintaining span, the cell's memory update is not only determined by the input data but also affected by its duration. An adaptive memory update method is proposed according to the change of the sequence input at each time step. This breaks the limitation that the cells calculate the cell state and hidden output for each input always in a unified manner, making the model more suitable for processing the sequences with continuous data. The experimental results on various sequence training tasks show that under the same iteration epochs, the proposed method can achieve higher accuracy, but need relatively less training time compared with the standard LSTM architecture.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124572103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00121
Liming Yuan, Xianbin Wen, Lu Zhao, Haixia Xu
The instance selection based model is an effective multiple-instance learning (MIL) framework, which solves the MIL problems by embedding examples (bags of instances) into a new feature space formed by some concepts (represented by some selected instances). Most previous studies use single-point concepts for the instance selection, where every possible concept is represented by only a single instance. In this paper, we apply multiple-point concepts for choosing instances, in which each possible concept is jointly represented by a group of similar instances. Furthermore, we establish an iterative instance selection based MIL framework based on multiple-point concepts, which is guaranteed to automatically converge to the needed number of concepts for a given problem. The experimental results demonstrate that the proposed framework can better handle not only common MIL problems but also hybrid ones compared to state-of-the-art MIL algorithms.
{"title":"An Iterative Instance Selection Based Framework for Multiple-Instance Learning","authors":"Liming Yuan, Xianbin Wen, Lu Zhao, Haixia Xu","doi":"10.1109/ICTAI.2018.00121","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00121","url":null,"abstract":"The instance selection based model is an effective multiple-instance learning (MIL) framework, which solves the MIL problems by embedding examples (bags of instances) into a new feature space formed by some concepts (represented by some selected instances). Most previous studies use single-point concepts for the instance selection, where every possible concept is represented by only a single instance. In this paper, we apply multiple-point concepts for choosing instances, in which each possible concept is jointly represented by a group of similar instances. Furthermore, we establish an iterative instance selection based MIL framework based on multiple-point concepts, which is guaranteed to automatically converge to the needed number of concepts for a given problem. The experimental results demonstrate that the proposed framework can better handle not only common MIL problems but also hybrid ones compared to state-of-the-art MIL algorithms.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128716918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the past few years, the e-commerce platforms and marketplaces have enriched their services with new features to improve their user experience and increase their profitability. Such features include relevant products suggestion, personalized recommendations, query understanding algorithms and numerous others. To effectively implement all these features, a robust products categorization method is required. Due to its importance, the problem of the automatic products classification into a given taxonomy has attracted the attention of multiple researchers. In the current literature, we encounter a broad variety of solutions, ranging from supervised and deep learning algorithms, as well as convolutional and recurrent neural networks. In this paper we introduce a supervised learning method which performs morphological analysis of the product titles by extracting and processing a combination of words and n-grams. In the sequel, each of these tokens receives an importance score according to several criteria which reflect the strength of the correlation of the token with a category. Based on these importance scores, we also propose a dimensionality reduction technique to reduce the size of the feature space without sacrificing much of the performance of the algorithm. The experimental evaluation of our method was conducted by using a real-world dataset, comprised of approximately 320 thousand product titles, which we acquired by crawling a product comparison Web platform. The results of this evaluation indicate that our approach is highly accurate, since it achieves a remarkable classification accuracy of over 95%.
{"title":"Effective Products Categorization with Importance Scores and Morphological Analysis of the Titles","authors":"Leonidas Akritidis, Athanasios Fevgas, Panayiotis Bozanis","doi":"10.1109/ICTAI.2018.00041","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00041","url":null,"abstract":"During the past few years, the e-commerce platforms and marketplaces have enriched their services with new features to improve their user experience and increase their profitability. Such features include relevant products suggestion, personalized recommendations, query understanding algorithms and numerous others. To effectively implement all these features, a robust products categorization method is required. Due to its importance, the problem of the automatic products classification into a given taxonomy has attracted the attention of multiple researchers. In the current literature, we encounter a broad variety of solutions, ranging from supervised and deep learning algorithms, as well as convolutional and recurrent neural networks. In this paper we introduce a supervised learning method which performs morphological analysis of the product titles by extracting and processing a combination of words and n-grams. In the sequel, each of these tokens receives an importance score according to several criteria which reflect the strength of the correlation of the token with a category. Based on these importance scores, we also propose a dimensionality reduction technique to reduce the size of the feature space without sacrificing much of the performance of the algorithm. The experimental evaluation of our method was conducted by using a real-world dataset, comprised of approximately 320 thousand product titles, which we acquired by crawling a product comparison Web platform. The results of this evaluation indicate that our approach is highly accurate, since it achieves a remarkable classification accuracy of over 95%.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116308083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00135
F. Golpayegani, S. Clarke
Taxi-sharing is an emergent transport mode, which has shown promising results economically, by splitting the travel cost between passengers and environmentally, by serving more people in each trip. Intelligent taxi-dispatch approaches can also manage demand by distributing taxis according to population density in a city. Current approaches to taxi-sharing recommend passengers share a taxi by matching their origin and destination, and taxi-dispatch approaches simply send more taxis to populated areas. However, each passenger may have multiple preferences (e.g., level of convenience, time, cost, and environmental factors), and require a mechanism that offers options considering these preferences. Similarly, taxi drivers may have multiple preferences (e.g., number of hours to work, minimum revenue per day) that need to be considered during a taxi-dispatch planning process. This paper presents a multi-agent collaborative passenger matching and taxi-dispatch model. Passengers and drivers are modeled as autonomous agents having multiple often-conflicting preferences. Passenger agents collaboratively take actions to form a group for a taxi-share, and taxi agents collaborate to achieve a dispatch plan.
{"title":"Co-Ride: Collaborative Preference-Based Taxi-Sharing and Taxi-Dispatch","authors":"F. Golpayegani, S. Clarke","doi":"10.1109/ICTAI.2018.00135","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00135","url":null,"abstract":"Taxi-sharing is an emergent transport mode, which has shown promising results economically, by splitting the travel cost between passengers and environmentally, by serving more people in each trip. Intelligent taxi-dispatch approaches can also manage demand by distributing taxis according to population density in a city. Current approaches to taxi-sharing recommend passengers share a taxi by matching their origin and destination, and taxi-dispatch approaches simply send more taxis to populated areas. However, each passenger may have multiple preferences (e.g., level of convenience, time, cost, and environmental factors), and require a mechanism that offers options considering these preferences. Similarly, taxi drivers may have multiple preferences (e.g., number of hours to work, minimum revenue per day) that need to be considered during a taxi-dispatch planning process. This paper presents a multi-agent collaborative passenger matching and taxi-dispatch model. Passengers and drivers are modeled as autonomous agents having multiple often-conflicting preferences. Passenger agents collaboratively take actions to form a group for a taxi-share, and taxi agents collaborate to achieve a dispatch plan.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126479849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00060
Nelly Elsayed, A. Maida, M. Bayoumi
This paper empirically evaluates and analyzes the effect of the choice of recurrent activation and unit activation functions on the unsupervised convolutional LSTM learning process. The goal of this work is to provide guidance for selecting the optimal non-linear activation function for the convolutional LSTM models which target the video prediction problem. This paper shows an empirical analysis of different non-linear activation functions that are commonly implemented in different deep learning APIs. We used the moving MNIST dataset as the most common benchmark for video prediction problems.
{"title":"Empirical Activation Function Effects on Unsupervised Convolutional LSTM Learning","authors":"Nelly Elsayed, A. Maida, M. Bayoumi","doi":"10.1109/ICTAI.2018.00060","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00060","url":null,"abstract":"This paper empirically evaluates and analyzes the effect of the choice of recurrent activation and unit activation functions on the unsupervised convolutional LSTM learning process. The goal of this work is to provide guidance for selecting the optimal non-linear activation function for the convolutional LSTM models which target the video prediction problem. This paper shows an empirical analysis of different non-linear activation functions that are commonly implemented in different deep learning APIs. We used the moving MNIST dataset as the most common benchmark for video prediction problems.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128968988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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