Incremental Computation of Deterministic Extensions for Dynamic Argumentation Frameworks

2016 ◽  
pp. 288-304 ◽  
Author(s):  
Sergio Greco ◽  
Francesco Parisi
Keyword(s):  
Incremental Computation ◽  
Dynamic Argumentation

scholarly journals Efficient EMD-based Similarity Search via Batch Pruning and Incremental Computation

2021 ◽  
pp. 1-1
Author(s):  
Yu Chen ◽  
Yong Zhang ◽  
Jin Wang ◽  
Jiacheng Wu ◽  
Chunxiao Xing
Keyword(s):  
Similarity Search ◽  
Incremental Computation

scholarly journals Incremental computation with names

2015 ◽  
Vol 50 (10) ◽  
pp. 748-766 ◽  
Author(s):  
Matthew A. Hammer ◽  
Joshua Dunfield ◽  
Kyle Headley ◽  
Nicholas Labich ◽  
Jeffrey S. Foster ◽  
...  
Keyword(s):  
Incremental Computation

Incremental Computation of Queries

2016 ◽  
pp. 1-4
Author(s):  
Guozhu Dong ◽  
Jianwen Su
Keyword(s):  
Incremental Computation

Incremental Computation of Queries

2018 ◽  
pp. 1830-1833
Author(s):  
Guozhu Dong ◽  
Jianwen Su
Keyword(s):  
Incremental Computation

scholarly journals Towards Transparent Human-in-the-Loop Classification of Fraudulent Web Shops

2020 ◽  
Author(s):  
Daphne Odekerken ◽  
Floris Bex
Keyword(s):  
Case Based Reasoning ◽  
Agent Architecture ◽  
Human In The Loop ◽  
Levels Of Detail ◽  
Legal Case ◽  
Dynamic Argumentation ◽  
Case Based

We propose an agent architecture for transparent human-in-the-loop classification. By combining dynamic argumentation with legal case-based reasoning, we create an agent that is able to explain its decisions at various levels of detail and adapts to new situations. It keeps the human analyst in the loop by presenting suggestions for corrections that may change the factors on which the current decision is based and by enabling the analyst to add new factors. We are currently implementing the agent for classification of fraudulent web shops at the Dutch Police.

Dynamic Argumentation in Abstract Dialogue Frameworks

2011 ◽  
pp. 228-247 ◽  
Author(s):  
M. Julieta Marcos ◽  
Marcelo A. Falappa ◽  
Guillermo R. Simari
Keyword(s):  
Dynamic Argumentation

ORBITS

2020 ◽  
Vol 14 (3) ◽  
pp. 294-306
Author(s):  
Mourad Khayati ◽  
Ines Arous ◽  
Zakhar Tymchenko ◽  
Philippe Cudré-Mauroux
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Online Data ◽  
Incremental Computation ◽  
Online Recovery ◽  
Recovery Technique ◽  
Centroid Decomposition

With the emergence of the Internet of Things (IoT), time series streams have become ubiquitous in our daily life. Recording such data is rarely a perfect process, as sensor failures frequently occur, yielding occasional blocks of data that go missing in multiple time series. These missing blocks do not only affect real-time monitoring but also compromise the quality of online data analyses. Effective streaming recovery (imputation) techniques either have a quadratic runtime complexity, which is infeasible for any moderately sized data, or cannot recover more than one time series at a time. In this paper, we introduce a new online recovery technique to recover multiple time series streams in linear time. Our recovery technique implements a novel incremental version of the Centroid Decomposition technique and reduces its complexity from quadratic to linear. Using this incremental technique, missing blocks are efficiently recovered in a continuous manner based on previous recoveries. We formally prove the correctness of our new incremental computation, which yields an accurate recovery. Our experimental results on real-world time series show that our recovery technique is, on average, 30% more accurate than the state of the art while being vastly more efficient.

Modeling Human Decision Making with an Abstract Dynamic Argumentation Framework

2021 ◽  
pp. 3-18
Author(s):  
Maximiliano Sapino ◽  
Edgardo Ferretti ◽  
Luciana Mariñelarena Dondena ◽  
Marcelo Errecalde
Keyword(s):  
Decision Making ◽  
Argumentation Framework ◽  
Human Decision ◽  
Dynamic Argumentation
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