Interactive Exploration-Exploitation Balancing for Generative Melody Composition

2021 ◽  
Author(s):  
Yijun Zhou ◽  
Yuki Koyama ◽  
Masataka Goto ◽  
Takeo Igarashi
Keyword(s):  
Interactive Exploration ◽  
Exploration Exploitation

Reconceptualizing Creativity Through an Exploration/Exploitation Framework

2005 ◽  
Author(s):  
Neta Moye ◽  
Lucy L. Gilson ◽  
Jill E. Perry-Smith
Keyword(s):  
Exploration Exploitation

scholarly journals Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1839
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
José Lemus-Romani ◽  
Marcelo Becerra-Rozas ◽  
José M. Lanza-Gutiérrez ◽  
...  
Keyword(s):  
Combinatorial Problems ◽  
Set Covering ◽  
Covering Problem ◽  
Integration Framework ◽  
Q Learning ◽  
Whale Optimization ◽  
Sine Cosine Algorithm ◽  
Exploration Exploitation ◽  
Selection Of

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

Interactive Exploration of Multi-Dimensional Information Spaces with Preference Support

2014 ◽  
Vol 48 (2) ◽  
pp. 118-118
Author(s):  
Panagiotis Papadakos
Keyword(s):  
Information Spaces ◽  
Interactive Exploration

scholarly journals Robo-Advising: Learning Investors’ Risk Preferences via Portfolio Choices*

2020 ◽  
Author(s):  
Humoud Alsabah ◽  
Agostino Capponi ◽  
Octavio Ruiz Lacedelli ◽  
Matt Stern
Keyword(s):  
Opportunity Cost ◽  
Value Function ◽  
Risk Preference ◽  
Portfolio Decisions ◽  
Learning Framework ◽  
Portfolio Choices ◽  
Trading Decisions ◽  
Exploration Exploitation ◽  
The Value Function ◽  
Over Time

Abstract We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor’s risk preference but learns it over time by observing her portfolio choices in different market environments. We develop an exploration–exploitation algorithm that trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor’s risk aversion. We show that the approximate value function constructed by the algorithm converges to the value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor’s mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor’s opportunity cost for making portfolio decisions.

scholarly journals Interactive Exploration, Analysis, and Visualization of Complex Phenome-Genome Datasets with ASPIREdb

2016 ◽  
Vol 37 (8) ◽  
pp. 719-726 ◽  
Author(s):  
Powell Patrick Cheng Tan ◽  
Sanja Rogic ◽  
Anton Zoubarev ◽  
Cameron McDonald ◽  
Frances Lui ◽  
...  
Keyword(s):  
Interactive Exploration

Towards self-predicting systems: What if you could ask ‘what-if’?

2006 ◽  
Vol 21 (3) ◽  
pp. 261-267 ◽  
Author(s):  
ENO THERESKA ◽  
DUSHYANTH NARAYANAN ◽  
GREGORY R. GANGER
Keyword(s):  
System Design ◽  
Large System ◽  
System Management ◽  
System Behavior ◽  
Search Problems ◽  
Interactive Exploration ◽  
Design Changes ◽  
Rules Of Thumb ◽  
System Changes ◽  
Management Problems

Today, management and tuning questions are approached using if… then… rules of thumb. This reactive approach requires expertise regarding system behavior, making it difficult to deal with unforeseen uses of a system’s resources and leading to system unpredictability and large system management overheads. We propose a What…if… approach that allows interactive exploration of the effects of system changes, thus converting complex tuning problem into simpler search problems. Through two concrete management problems, automating system upgrades and deciding on service migrations, we identify system design changes that enable a system to answer What…if… questions about itself.

scholarly journals Interactive Exploration for Continuously Expanding Neuron Databases

Methods ◽  
2017 ◽  
Vol 115 ◽  
pp. 100-109 ◽  
Author(s):  
Zhongyu Li ◽  
Dimitris N. Metaxas ◽  
Aidong Lu ◽  
Shaoting Zhang
Keyword(s):  
Interactive Exploration
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