The Experts below are selected from a list of 18507 Experts worldwide ranked by ideXlab platform
Abdur Rahim - One of the best experts on this subject based on the ideXlab platform.
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WF-IoT - A unified semantic knowledge base for IoT
2014 IEEE World Forum on Internet of Things (WF-IoT), 2014Co-Authors: S. N.akshay Uttama Nambi, Chayan Sarkar, R. Venkatesha Prasad, Abdur RahimAbstract:In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic Modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically Model Dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and Modeling for IoT in Dynamic environments.
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A unified semantic knowledge base for IoT
Internet of Things (WF-IoT), 2014 IEEE World Forum on, 2014Co-Authors: S. N.akshay Uttama Nambi, Chayan Sarkar, R. Venkatesha Prasad, Ramjee Prasad, Abdur RahimAbstract:In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic Modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically Model Dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and Modeling for IoT in Dynamic environments.
Sven Koenig - One of the best experts on this subject based on the ideXlab platform.
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Incremental DCOP Search Algorithms for Solving Dynamic DCOPs
Int. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2011Co-Authors: William Yeoh, Xiaoxun Sun, Sven KoenigAbstract:Distributed constraint optimization problems (DCOPs) are well-suited for Modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we Model Dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper bounds from the previous DCOP when solving the next one in the sequence. Our experimental results show that the speedup gained from this procedure increases with the amount of memory the agents have available. Copyright © 2011, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
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Incremental DCOP Search Algorithms for Solving Dynamic DCOPs ∗ ( Extended Abstract )
Artificial Intelligence, 2011Co-Authors: William Yeoh, Xiaoxun Sun, Pradeep Varakantham, Sven KoenigAbstract:—Distributed constraint optimization (DCOP) prob-lems are well-suited for Modeling multi-agent coordination prob-lems. However, it only Models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) Model to Model Dynamic problems. In this paper, we make two key contributions: (a) a procedure to reason with the incremental changes in DDCOP problems and (b) an incremental pseudo-tree construction algorithm that can be used by DCOP algorithms such as any-space ADOPT and any-space BnB-ADOPT to solve DDCOP problems. Due to the incremental reasoning employed, our experimental results show that any-space ADOPT and any-space BnB-ADOPT are up to 42% and 38% faster, respectively, with the incremental procedure and the incremental pseudo-tree reconstruction algorithm than without them.
William Yeoh - One of the best experts on this subject based on the ideXlab platform.
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Infinite-Horizon Proactive Dynamic DCOPs
Proc. of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), 2017Co-Authors: Khoi Hoang, Roie Zivan, Ping Hou, Ferdinando Fioretto, William Yeoh, Makoto YokooAbstract:Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The current approaches to Model dynamism in DCOPs solve a sequence of static problems, reacting to the changes in the environment as the agents observe them. Such approaches, thus, ignore possible predictions on the environment evolution. To overcome such limitations, we introduce the Proactive Dynamic DCOP (PD-DCOP) Model, a novel formalism to Model Dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly Model the possible changes to the problem, and take such information into account proactively, when solving the Dynamically changing problem.
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Proactive Dynamic DCOPs
AAAI Workshop - Technical Report, 2016Co-Authors: Khoi Hoang, Ping Hou, Ferdinando Fioretto, William Yeoh, Makoto Yokoo, Roie ZivanAbstract:Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The current approaches to Model dynamism in DCOPs solve a sequence of static problems, reacting to the changes in the environment as the agents observe them. Such approaches, thus, ignore possible predictions on the environment evolution. To overcome such limitations, we introduce the Proactive Dynamic DCOP (PD-DCOP) Model, a novel formalism to Model Dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly Model the possible changes to the problem, and take such information into account proactively, when solving the Dynamically changing problem.
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Proactive Dynamic Distributed Constraint Optimization
Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 2016Co-Authors: Khoi D Hoang, Ping Hou, Ferdinando Fioretto, William Yeoh, Makoto Yokoo, Roie ZivanAbstract:Current approaches that Model dynamism in DCOPs solve a se-quence of static problems, reacting to changes in the environment as the agents observe them. Such approaches thus ignore possi-ble predictions on future changes. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel for-malism to Model Dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly Model the possible changes to the problem, and take such information into account proactively, when solving the dy-namically changing problem. The additional expressivity of this formalism allows it to Model a wider variety of distributed opti-mization problems. Our work presents both theoretical and prac-tical contributions that advance current Dynamic DCOP Models: (i) we introduce the PD-DCOP Model, which explicitly captures Dynamic changes of the DCOP over time; (ii) we discuss the com-plexity of this new class of DCOPs; and (iii) we develop both exact and approximation algorithms with quality guarantees to solve PD-DCOPs proactively.
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Incremental DCOP Search Algorithms for Solving Dynamic DCOPs
Int. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2011Co-Authors: William Yeoh, Xiaoxun Sun, Sven KoenigAbstract:Distributed constraint optimization problems (DCOPs) are well-suited for Modeling multi-agent coordination problems. However, most research has focused on developing algorithms for solving static DCOPs. In this paper, we Model Dynamic DCOPs as sequences of (static) DCOPs with changes from one DCOP to the next one in the sequence. We introduce the ReuseBounds procedure, which can be used by any-space ADOPT and any-space BnB-ADOPT to find cost-minimal solutions for all DCOPs in the sequence faster than by solving each DCOP individually. This procedure allows those agents that are guaranteed to remain unaffected by a change to reuse their lower and upper bounds from the previous DCOP when solving the next one in the sequence. Our experimental results show that the speedup gained from this procedure increases with the amount of memory the agents have available. Copyright © 2011, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
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Incremental DCOP Search Algorithms for Solving Dynamic DCOPs ∗ ( Extended Abstract )
Artificial Intelligence, 2011Co-Authors: William Yeoh, Xiaoxun Sun, Pradeep Varakantham, Sven KoenigAbstract:—Distributed constraint optimization (DCOP) prob-lems are well-suited for Modeling multi-agent coordination prob-lems. However, it only Models static problems, which do not change over time. Consequently, researchers have introduced the Dynamic DCOP (DDCOP) Model to Model Dynamic problems. In this paper, we make two key contributions: (a) a procedure to reason with the incremental changes in DDCOP problems and (b) an incremental pseudo-tree construction algorithm that can be used by DCOP algorithms such as any-space ADOPT and any-space BnB-ADOPT to solve DDCOP problems. Due to the incremental reasoning employed, our experimental results show that any-space ADOPT and any-space BnB-ADOPT are up to 42% and 38% faster, respectively, with the incremental procedure and the incremental pseudo-tree reconstruction algorithm than without them.
S. N.akshay Uttama Nambi - One of the best experts on this subject based on the ideXlab platform.
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WF-IoT - A unified semantic knowledge base for IoT
2014 IEEE World Forum on Internet of Things (WF-IoT), 2014Co-Authors: S. N.akshay Uttama Nambi, Chayan Sarkar, R. Venkatesha Prasad, Abdur RahimAbstract:In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic Modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically Model Dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and Modeling for IoT in Dynamic environments.
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A unified semantic knowledge base for IoT
Internet of Things (WF-IoT), 2014 IEEE World Forum on, 2014Co-Authors: S. N.akshay Uttama Nambi, Chayan Sarkar, R. Venkatesha Prasad, Ramjee Prasad, Abdur RahimAbstract:In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic Modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically Model Dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and Modeling for IoT in Dynamic environments.
Chayan Sarkar - One of the best experts on this subject based on the ideXlab platform.
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WF-IoT - A unified semantic knowledge base for IoT
2014 IEEE World Forum on Internet of Things (WF-IoT), 2014Co-Authors: S. N.akshay Uttama Nambi, Chayan Sarkar, R. Venkatesha Prasad, Abdur RahimAbstract:In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic Modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically Model Dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and Modeling for IoT in Dynamic environments.
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A unified semantic knowledge base for IoT
Internet of Things (WF-IoT), 2014 IEEE World Forum on, 2014Co-Authors: S. N.akshay Uttama Nambi, Chayan Sarkar, R. Venkatesha Prasad, Ramjee Prasad, Abdur RahimAbstract:In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic Modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically Model Dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and Modeling for IoT in Dynamic environments.
