In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at the single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing and interacting agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at the same nodes simultaneously. As all nodes are capacitated, if a particular node receives more agents than its capacity, all agents at that node will be made to wait and agents suffer collectively as a result (in terms of extra time needed for queueing). Due to the decentralized nature of the problem, MOPTCC cannot be solved in a centralized manner; instead, we need to seek out equilibrium solutions; and if this is not possible, at least approximated equilibrium solutions. The major contribution of this paper is the formulation of the problem, and our first attempt in identifying an efficient and effective equilibrium-seeking procedure for MOPTCC.
{"title":"Multi-agent orienteering problem with time-dependent capacity constraints","authors":"Cen Chen, Shih-Fen Cheng, H. Lau","doi":"10.3233/WIA-140304","DOIUrl":"https://doi.org/10.3233/WIA-140304","url":null,"abstract":"In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at the single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing and interacting agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at the same nodes simultaneously. As all nodes are capacitated, if a particular node receives more agents than its capacity, all agents at that node will be made to wait and agents suffer collectively as a result (in terms of extra time needed for queueing). Due to the decentralized nature of the problem, MOPTCC cannot be solved in a centralized manner; instead, we need to seek out equilibrium solutions; and if this is not possible, at least approximated equilibrium solutions. The major contribution of this paper is the formulation of the problem, and our first attempt in identifying an efficient and effective equilibrium-seeking procedure for MOPTCC.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131242907","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}
Shafiq Alam, G. Dobbie, Yun Sing Koh, Patricia J. Riddle
Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations.
{"title":"Web usage mining based recommender systems using implicit heterogeneous data: - A Particle Swarm Optimization based clustering approach","authors":"Shafiq Alam, G. Dobbie, Yun Sing Koh, Patricia J. Riddle","doi":"10.3233/WIA-140302","DOIUrl":"https://doi.org/10.3233/WIA-140302","url":null,"abstract":"Recommender systems have become one of the necessary tools to help a web user find a potentially interesting resource based on their preferences. In implicit recommender systems, the recommendations are made based on the implicit information of the web users i.e. data collected from web logs or cookies without knowing users preferences. Developing such a recommender system is complex due to the huge amount of anonymous noisy data. In this paper we present a Particle Swarm Optimization (PSO) based clustering approach called Hierarchical Particle Swarm Optimization based clustering (HPSO-clustering) for building a recommender system based on implicit web usage data. The approach mimics multi-agent properties of the particles of a swarm and divide the problem space into smaller sub-spaces i.e. clusters. Each cluster represents a particular group of user with similar interests. Later, the K-nearest neighbours of the most relevant cluster are generated as recommendations for a web user and ranked based on their distance. We performed different experiments for preprocessing, to assess the quality of clusters, and for the accuracy of recommendations. An overall accuracy of 65% to 95% was achieved for different scenarios, while in some cases the accuracy touched 100 precent when the selection was made from the top-5 recommendations.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124557155","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}
With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data.Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall.
{"title":"Scalable approximating SVD algorithm for recommender systems","authors":"Xun Zhou, Jing He, Guangyan Huang, Yanchun Zhang","doi":"10.3233/WIA-140303","DOIUrl":"https://doi.org/10.3233/WIA-140303","url":null,"abstract":"With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data.Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116008105","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}
State-of-the-art studies on cyberbullying detection, using text classification, predominantly take it for granted that streaming text can be completely labelled. However, the rapid growth of unlabelled data generated in real time from online content renders this virtually impossible. In this paper, we propose a session-based framework for automatic detection of cyberbullying within the large volume of unlabelled streaming text. Given that the streaming data from Social Networks arrives in large volume at the server system, we incorporate an ensemble of one-class classifiers in the session-based framework. System uses Multi-Agent distributed environment to process streaming data from multiple social network sources. The proposed strategy tackles real world situations, where only a few positive instances of cyberbullying are available for initial training. Our main contribution in this paper is to automatically detect cyberbullying in real world situations, where labelled data is not readily available. Initial results indicate the suggested approach is reasonably effective for detecting cyberbullying automatically on social networks. The experiments indicate that the ensemble learner outperforms the single window and fixed window approaches, while the learning process is based on positive and unlabelled data only, no negative data is available for training.
{"title":"Detecting cyberbullying in social networks using multi-agent system","authors":"V. Nahar, Xue Li, H. Zhang, C. Pang","doi":"10.3233/WIA-140301","DOIUrl":"https://doi.org/10.3233/WIA-140301","url":null,"abstract":"State-of-the-art studies on cyberbullying detection, using text classification, predominantly take it for granted that streaming text can be completely labelled. However, the rapid growth of unlabelled data generated in real time from online content renders this virtually impossible. In this paper, we propose a session-based framework for automatic detection of cyberbullying within the large volume of unlabelled streaming text. Given that the streaming data from Social Networks arrives in large volume at the server system, we incorporate an ensemble of one-class classifiers in the session-based framework. System uses Multi-Agent distributed environment to process streaming data from multiple social network sources. The proposed strategy tackles real world situations, where only a few positive instances of cyberbullying are available for initial training. Our main contribution in this paper is to automatically detect cyberbullying in real world situations, where labelled data is not readily available. Initial results indicate the suggested approach is reasonably effective for detecting cyberbullying automatically on social networks. The experiments indicate that the ensemble learner outperforms the single window and fixed window approaches, while the learning process is based on positive and unlabelled data only, no negative data is available for training.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115565531","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}
This special issue particularly focuses on using agent-based methods to solve the complex computational problems arising in Big Data environments. It covers the recent advances in the areas of distributed problem solving, agent-based data mining, as well as recommendation systems, working with data extracted from both physical and online environments.
{"title":"Agent-based problem solving methods in Big Data environment","authors":"H. Zhang, H. Lau","doi":"10.3233/WIA-140300","DOIUrl":"https://doi.org/10.3233/WIA-140300","url":null,"abstract":"This special issue particularly focuses on using agent-based methods to solve the complex computational problems arising in Big Data environments. It covers the recent advances in the areas of distributed problem solving, agent-based data mining, as well as recommendation systems, working with data extracted from both physical and online environments.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124882410","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}
We propose a syntactic possibilistic belief-change operator, which operates on a belief base of necessity-valued formulas. Such a base may be regarded as a finite and compact encoding of a possibility distribution over a possibly infinite set of interpretations. The proposed operator is designed so that it behaves like a semantic possibilistic belief-change operator for BDI agents recently proposed in the literature. The equivalence of the semantic and syntactic operators is then proved. Experimental results are presented. The aim of these experiments is to demonstrate that the cost of belief revision expressed in terms of the number of entailment checks required as well as the size of the belief base do not explode as the number of new pieces of information formulas supplied increases.
{"title":"A syntactic possibilistic belief change operator: Theory and empirical study","authors":"C. Pereira, A. Tettamanzi","doi":"10.3233/WIA-140290","DOIUrl":"https://doi.org/10.3233/WIA-140290","url":null,"abstract":"We propose a syntactic possibilistic belief-change operator, which operates on a belief base of necessity-valued formulas. Such a base may be regarded as a finite and compact encoding of a possibility distribution over a possibly infinite set of interpretations. The proposed operator is designed so that it behaves like a semantic possibilistic belief-change operator for BDI agents recently proposed in the literature. The equivalence of the semantic and syntactic operators is then proved. Experimental results are presented. The aim of these experiments is to demonstrate that the cost of belief revision expressed in terms of the number of entailment checks required as well as the size of the belief base do not explode as the number of new pieces of information formulas supplied increases.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801997","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}
Resource management is a key challenge in multiagent systems. It is especially important in dynamic environments where decisions need to be made quickly and when decisions can get obsolete quickly. In wireless local area networks WLANs, resource management includes dynamic channel assignment, dynamic transmit power control and load balancing of WLANs traffic. In this work, we present a novel decentralized framework and a distributed optimization algorithm DLB-SDPOP for load balancing in complex WLANs. Our self-stabilizing algorithm focuses on repairing the network structure instead of reconstructing it when network perturbations occur. It controls the complexity of problem solving by utilizing efficient search and leverages uncertainty to reduce the possibility of reaching myopic solutions. The size and number of inter-agent communication messages are significantly reduced using a communication filtering mechanism. We categorize different scenarios based on key characteristics i.e., load, dynamics and uncertainty in WLANs and compare our algorithm with other state of the art distributed constraint optimization algorithms in each scenario. Our empirical results show that our distributed approach improves WLANs load balancing performance significantly.
{"title":"Dynamic multiagent load balancing using distributed constraint optimization techniques","authors":"Shanjun Cheng, A. Raja, Jiang Xie","doi":"10.3233/WIA-140288","DOIUrl":"https://doi.org/10.3233/WIA-140288","url":null,"abstract":"Resource management is a key challenge in multiagent systems. It is especially important in dynamic environments where decisions need to be made quickly and when decisions can get obsolete quickly. In wireless local area networks WLANs, resource management includes dynamic channel assignment, dynamic transmit power control and load balancing of WLANs traffic. In this work, we present a novel decentralized framework and a distributed optimization algorithm DLB-SDPOP for load balancing in complex WLANs. Our self-stabilizing algorithm focuses on repairing the network structure instead of reconstructing it when network perturbations occur. It controls the complexity of problem solving by utilizing efficient search and leverages uncertainty to reduce the possibility of reaching myopic solutions. The size and number of inter-agent communication messages are significantly reduced using a communication filtering mechanism. We categorize different scenarios based on key characteristics i.e., load, dynamics and uncertainty in WLANs and compare our algorithm with other state of the art distributed constraint optimization algorithms in each scenario. Our empirical results show that our distributed approach improves WLANs load balancing performance significantly.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117183633","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}
Previously, we constructed the Japanese Wikipedia Ontology JWO via a semi-automatic process using the Japanese Wikipedia, but it had problems due to a lack of upper classes and appropriate definitions of properties. Thus, the aim of the current study was to complement the upper classes in JWO by refining and integrating JWO and Japanese WordNet JWN to build a class hierarchy with defined properties based on the considerations of property inheritance. To achieve this, we developed tools that help users to refine the class-instance relationships, to identify the JWO classes that need to be aligned with JWN synsets, and to align the JWO classes with the JWN synsets via user interaction. We also integrated JWO and JWN using a domain ontology development environment, DODDLE-OWL. We also propose a method for building a class hierarchy with defined properties by elevating the common properties defined in sibling classes to higher classes in JWO.
{"title":"Building up a class hierarchy with properties by refining and integrating Japanese Wikipedia Ontology and Japanese WordNet","authors":"Takeshi Morita, Yuka Sekimoto, Susumu Tamagawa, Takahira Yamaguchi","doi":"10.3233/WIA-140293","DOIUrl":"https://doi.org/10.3233/WIA-140293","url":null,"abstract":"Previously, we constructed the Japanese Wikipedia Ontology JWO via a semi-automatic process using the Japanese Wikipedia, but it had problems due to a lack of upper classes and appropriate definitions of properties. Thus, the aim of the current study was to complement the upper classes in JWO by refining and integrating JWO and Japanese WordNet JWN to build a class hierarchy with defined properties based on the considerations of property inheritance. To achieve this, we developed tools that help users to refine the class-instance relationships, to identify the JWO classes that need to be aligned with JWN synsets, and to align the JWO classes with the JWN synsets via user interaction. We also integrated JWO and JWN using a domain ontology development environment, DODDLE-OWL. We also propose a method for building a class hierarchy with defined properties by elevating the common properties defined in sibling classes to higher classes in JWO.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248404","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}
Dandan Zhu, Yusuke Fukazawa, Eleftherios Karapetsas, J. Ota
A topic model capable of assigning word pairs to associated topics is developed to explore people's activities. Considering that the form of word pairs led by verbs is a more effective way to express people's activities than separate words, we incorporate the word-connection model into the smoothed Latent Dirichlet Allocation LDA to ensure that the words are well paired and assigned to the associated topics. To quantitatively and qualitatively evaluate the proposed model, two datasets were built using Twitter posts as data sources: the wish-related and the geographical information-related datasets. The experiment results using the wish-related dataset indicate that the relatedness of words plays a key role in forming reasonable pairs, and the proposed model, word-pair generative Latent Dirichlet Allocation wpLDA, performs well in clustering. Results obtained using the geographical information-related dataset demonstrate that the proposed model works well for discovering people's activities, in which the activities are understandably represented with an intuitive character.
{"title":"Activity-based topic discovery","authors":"Dandan Zhu, Yusuke Fukazawa, Eleftherios Karapetsas, J. Ota","doi":"10.3233/WIA-140292","DOIUrl":"https://doi.org/10.3233/WIA-140292","url":null,"abstract":"A topic model capable of assigning word pairs to associated topics is developed to explore people's activities. Considering that the form of word pairs led by verbs is a more effective way to express people's activities than separate words, we incorporate the word-connection model into the smoothed Latent Dirichlet Allocation LDA to ensure that the words are well paired and assigned to the associated topics. To quantitatively and qualitatively evaluate the proposed model, two datasets were built using Twitter posts as data sources: the wish-related and the geographical information-related datasets. The experiment results using the wish-related dataset indicate that the relatedness of words plays a key role in forming reasonable pairs, and the proposed model, word-pair generative Latent Dirichlet Allocation wpLDA, performs well in clustering. Results obtained using the geographical information-related dataset demonstrate that the proposed model works well for discovering people's activities, in which the activities are understandably represented with an intuitive character.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127411899","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}
We are concerned with normative systems for communities of human and artificial agents that cooperate for improving their overall goals. Unlike open artificial multi-agent systems, we need to be more flexible in judging the behavior of the agents, and still thrive for efficiency. To attain the overall system goals norms are used for guiding the behavior of the agents, whether human or artificial. To provide flexibility in adaptation the power of ontology expressed in description logic is used. The problem consists then in conceiving some mechanism that can support flexibility for the system, without imposing a too heavy burden for such applications. Some cases are presented to show how our proposal satisfies the needs for adapting and evolving normative systems for communities of practice. The interplay of the norms and the concepts in the ontology provides a period of grace for minor offenders, wether artificial or human.
{"title":"Towards adaptive normative systems for communities of agents","authors":"I. A. Letia, O. Pop","doi":"10.3233/WIA-130279","DOIUrl":"https://doi.org/10.3233/WIA-130279","url":null,"abstract":"We are concerned with normative systems for communities of human and artificial agents that cooperate for improving their overall goals. Unlike open artificial multi-agent systems, we need to be more flexible in judging the behavior of the agents, and still thrive for efficiency. To attain the overall system goals norms are used for guiding the behavior of the agents, whether human or artificial. To provide flexibility in adaptation the power of ontology expressed in description logic is used. The problem consists then in conceiving some mechanism that can support flexibility for the system, without imposing a too heavy burden for such applications. Some cases are presented to show how our proposal satisfies the needs for adapting and evolving normative systems for communities of practice. The interplay of the norms and the concepts in the ontology provides a period of grace for minor offenders, wether artificial or human.","PeriodicalId":263450,"journal":{"name":"Web Intell. Agent Syst.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116464792","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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