Two-Mode Threshold Graph Dynamical Systems for Modeling Evacuation Decision-Making During Disaster Events

2019 ◽  
pp. 519-531
Author(s):  
Nafisa Halim ◽  
Chris J. Kuhlman ◽  
Achla Marathe ◽  
Pallab Mozumder ◽  
Anil Vullikanti
Keyword(s):  
Decision Making ◽  
Dynamical Systems ◽  
Threshold Graph

scholarly journals Combining decision making and dynamical systems for monitoring and executing manipulation tasks

2020 ◽  
Vol 137 (6) ◽  
pp. 309-315
Author(s):  
Matteo Saveriano ◽  
Justus Piater
Keyword(s):  
Decision Making ◽  
Dynamical Systems ◽  
Task Scheduling ◽  
Sorting Task ◽  
Reactive System ◽  
Level Control ◽  
Motion Generation ◽  
Unified Framework ◽  
External Disturbances ◽  
Behavior Trees

Abstract In this paper, we propose a unified framework for online task scheduling, monitoring, and execution that integrates reconfigurable behavior trees, a decision-making framework with integrated low-level control functionalities, and reactive motion generation with stable dynamical systems. In this way, we realize a flexible and reactive system capable of coping with unexpected variations in the executive context without penalizing modularity, expressiveness, and readability of humans. The framework is evaluated in a simulated sorting task showing promising results in terms of flexibility regarding task scheduling and robustness to external disturbances.

Generalized Ordinal Learning Framework (GOLF) for Decision Making with Future Simulated Data

2019 ◽  
Vol 36 (06) ◽  
pp. 1940011
Author(s):  
Giulia Pedrielli ◽  
K. Selcuk Candan ◽  
Xilun Chen ◽  
Logan Mathesen ◽  
Alireza Inanalouganji ◽  
...  
Keyword(s):  
Dynamical System ◽  
Decision Making ◽  
Dynamical Systems ◽  
Real Time ◽  
Simulated Data ◽  
Simulation Models ◽  
Real Data ◽  
Learning Framework ◽  
Main Challenge ◽  
Specific Scenario

Real-time decision making has acquired increasing interest as a means to efficiently operating complex systems. The main challenge in achieving real-time decision making is to understand how to develop next generation optimization procedures that can work efficiently using: (i) real data coming from a large complex dynamical system, (ii) simulation models available that reproduce the system dynamics. While this paper focuses on a different problem with respect to the literature in RL, the methods proposed in this paper can be used as a support in a sequential setting as well. The result of this work is the new Generalized Ordinal Learning Framework (GOLF) that utilizes simulated data interpreting them as low accuracy information to be intelligently collected offline and utilized online once the scenario is revealed to the user. GOLF supports real-time decision making on complex dynamical systems once a specific scenario is realized. We show preliminary results of the proposed techniques that motivate the authors in further pursuing the presented ideas.

Modeling and Decision-Making Support for Dynamical Systems with Uncertainty

2015 ◽  
Vol 41 (6) ◽  
pp. 374-380
Author(s):  
Tomoyuki Yajima ◽  
Yukihiro Soeda ◽  
Satoru Hashizume ◽  
Susumu Hashizume ◽  
Katsuaki Onogi
Keyword(s):  
Decision Making ◽  
Dynamical Systems ◽  
Decision Making Support

Discarding optimality: Throwing out the baby with the bathwater?

2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı
Keyword(s):  
Decision Making ◽  
Perceptual Decision Making ◽  
Reward Rate ◽  
Perceptual Decision ◽  
Standard Observer ◽  
Rate Maximization ◽  
Reward Rate Maximization ◽  
Descriptive Standard ◽  
Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

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