TY - GEN
T1 - Resource allocation for pragmatically-assisted quality of information-aware networking
AU - Edwards, James
AU - Passonneau, Rebecca J.
AU - Cassidy, Taylor
AU - La Porta, Thomas F.
N1 - Funding Information:
Research was sponsored by the Army Research Laboratory and accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/9/14
Y1 - 2017/9/14
N2 - In this work, we present a framework for handling multiple, simultaneous, natural language queries, in a resource constrained environment, using Quality of Information (QoI). Incoming queries are first parsed into response graphs, tree-like structures designed to formalize a system's understanding of user intent, via a pragmatics toolkit. The system then uses a combination of QoI-awareness, adaptive intent determination, and packing algorithms to maximize the QoI realized by the system. We employ two different methods of evaluation, a one-shot model and an iterative, time-staged model. Under the one-shot model, packed jobs are answered and the rest discarded, and we aim to maximize the total realized QoI. Under the staged model, the system repeatedly packs and offers answers until all jobs are complete; here we aim to maximize time-weighted QoI and minimize completion time. We evaluate the performance of different instantiations of our system through thousands of procedurally-generated simulations.
AB - In this work, we present a framework for handling multiple, simultaneous, natural language queries, in a resource constrained environment, using Quality of Information (QoI). Incoming queries are first parsed into response graphs, tree-like structures designed to formalize a system's understanding of user intent, via a pragmatics toolkit. The system then uses a combination of QoI-awareness, adaptive intent determination, and packing algorithms to maximize the QoI realized by the system. We employ two different methods of evaluation, a one-shot model and an iterative, time-staged model. Under the one-shot model, packed jobs are answered and the rest discarded, and we aim to maximize the total realized QoI. Under the staged model, the system repeatedly packs and offers answers until all jobs are complete; here we aim to maximize time-weighted QoI and minimize completion time. We evaluate the performance of different instantiations of our system through thousands of procedurally-generated simulations.
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U2 - 10.1109/ICCCN.2017.8038434
DO - 10.1109/ICCCN.2017.8038434
M3 - Conference contribution
AN - SCOPUS:85032282837
T3 - 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
BT - 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th International Conference on Computer Communications and Networks, ICCCN 2017
Y2 - 31 July 2017 through 3 August 2017
ER -