scholarly journals Are Groups More Rational than Individuals? A Review of Interactive Decision Making in Groups

2012 ◽  
Author(s):  
Tamar Kugler ◽  
Edgar E. Kausel ◽  
Martin G. Kocher
Keyword(s):  
Decision Making ◽  
Interactive Decision Making

scholarly journals The influence of emotion regulation on social interactive decision-making.

Emotion ◽  
2010 ◽  
Vol 10 (6) ◽  
pp. 815-821 ◽  
Author(s):  
Mascha van't Wout ◽  
Luke J. Chang ◽  
Alan G. Sanfey
Keyword(s):  
Decision Making ◽  
Emotion Regulation ◽  
Interactive Decision Making

INTERACTIVE DECISION MAKING USING POSSIBILITY AND NECESSITY MEASURES FOR A FUZZY RANDOM MULTIOBJECTIVE 0–1 PROGRAMMING PROBLEM

2006 ◽  
Vol 37 (1) ◽  
pp. 59-74 ◽  
Author(s):  
Hideki Katagiri ◽  
Masatoshi Sakawa ◽  
Ichiro Nishizaki
Keyword(s):  
Decision Making ◽  
Programming Problem ◽  
Interactive Decision Making ◽  
Fuzzy Random

Dynamics of Trust in Automation and Interactive Decision Making during Driving Simulation Tasks

2021 ◽  
Vol 65 (1) ◽  
pp. 786-790
Author(s):  
Lucero Rodriguez Rodriguez ◽  
Carlos Bustamante Orellana ◽  
Jayci Landfair ◽  
Corey Magaldino ◽  
Mustafa Demir ◽  
...  
Keyword(s):  
Decision Making ◽  
Automated System ◽  
Automated Driving ◽  
Safety And Efficacy ◽  
High Performing ◽  
Interactive Decision Making ◽  
Driving Task ◽  
Preliminary Study ◽  
Self Driving Cars ◽  
The Impact

As technological advancements and lowered costs make self-driving cars available to more people, it becomes important to understand the dynamics of human-automation interactions for safety and efficacy. We used a dynamical approach to examine data from a previous study on simulated driving with an automated driving assistant. To maximize effect size in this preliminary study, we focused the current analysis on the two lowest and two highest-performing participants. Our visual comparisons were the utilization of the automated system and the impact of perturbations. Low-performing participants toggled and maintained reliance either on automation or themselves for longer periods of time. Decision making of high-performing participants was using the automation briefly and consistently throughout the driving task. Participants who displayed an early understanding of automation capabilities opted for tactical use. Further exploration of individual differences and automation usage styles will help to understand the optimal human-automation-team dynamic and increase safety and efficacy.

A probabilistic model for interactive decision-making

1999 ◽  
Vol 25 (4) ◽  
pp. 289-308 ◽  
Author(s):  
Pierfrancesco Reverberi ◽  
Maurizio Talamo
Keyword(s):  
Decision Making ◽  
Probabilistic Model ◽  
Interactive Decision Making

scholarly journals Highlighting space–time patterns: Effective visual encodings for interactive decision‐making

2007 ◽  
Vol 21 (8) ◽  
pp. 879-893 ◽  
Author(s):  
M. Sips ◽  
J. Schneidewind ◽  
D. A. Keim
Keyword(s):  
Decision Making ◽  
Space Time ◽  
Time Patterns ◽  
Interactive Decision Making

Mapping Minimax in the Brain

2014 ◽  
Author(s):  
Ignacio Palacios-Huerta
Keyword(s):  
Decision Making ◽  
Mixed Strategies ◽  
Interactive Decision Making ◽  
Minimax Strategies ◽  
Experimental Subjects ◽  
Competitive Games ◽  
Zero Sum ◽  
Increased Activity ◽  
The Brain

This chapter is concerned with mixed strategies. Using fMRI techniques, it peers inside the brain when experimental subjects play the penalty kick game. As we have noted already, minimax is considered a cornerstone of interactive decision-making analysis. More importantly, the minimax strategies have not been mapped in the brain previously by studying simultaneously the two testable implications of equilibrium. The results show increased activity in various bilateral prefrontal regions during the decision period. Two inferior prefrontal nodes appear to jointly contribute to the ability to optimally play the study's asymmetric zero-sum penalty kick game by ensuring the appropriate equating of payoffs across strategies and the generating of random choices within the game, respectively. This evidence contributes to the neurophysiological literature studying competitive games.

Chapter 10. Explainable Neuro-Symbolic Hierarchical Reinforcement Learning

2021 ◽  
Author(s):  
Daoming Lyu ◽  
Fangkai Yang ◽  
Hugh Kwon ◽  
Bo Liu ◽  
Wen Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Black Box ◽  
Influential Factor ◽  
Hierarchical Reinforcement Learning ◽  
Interactive Decision Making ◽  
Symbolic Approach ◽  
Trust Systems ◽  
Symbolic Planning

Human-robot interactive decision-making is increasingly becoming ubiquitous, and explainability is an influential factor in determining the reliance on autonomy. However, it is not reasonable to trust systems beyond our comprehension, and typical machine learning and data-driven decision-making are black-box paradigms that impede explainability. Therefore, it is critical to establish computational efficient decision-making mechanisms enhanced by explainability-aware strategies. To this end, we propose the Trustworthy Decision-Making (TDM), which is an explainable neuro-symbolic approach by integrating symbolic planning into hierarchical reinforcement learning. The framework of TDM enables the subtask-level explainability from the causal relational and understandable subtasks. Besides, TDM also demonstrates the advantage of the integration between symbolic planning and reinforcement learning, reaping the benefits of both worlds. Experimental results validate the effectiveness of proposed method while improving the explainability in the process of decision-making.

Game Theory-Based Framework for Modeling Human–Vehicle Interactions on the Road

2020 ◽  
Vol 2674 (9) ◽  
pp. 701-713
Author(s):  
Yalda Rahmati ◽  
Alireza Talebpour ◽  
Archak Mittal ◽  
James Fishelson
Keyword(s):  
Decision Making ◽  
Human Behavior ◽  
Ground Truth ◽  
Realistic Model ◽  
Shared Environment ◽  
The Road ◽  
Interactive Behavior ◽  
Interactive Decision Making ◽  
Decision Making Framework ◽  
On The Road

New application domains have faded the barriers between humans and robots, introducing a new set of complexities to robotic systems. The major impediment is the uncertainties associated with human decision making, which makes it challenging to predict human behavior. A realistic model of human behavior is thus vital to capture humans’ interactive behavior with their surroundings and provide robots with reliable estimates on what is most likely to happen. Focusing on operations of connected and automated vehicles (CAVs) in areas with a high presence of human actors (i.e., pedestrians), this study creates an interactive decision-making framework to predict pedestrians’ trajectories when walking in a shared environment with vehicles and other pedestrians. It develops a game theoretical structure to approximate the movement and directional components of pedestrian motion using the theory of Nash equilibria in non-cooperative games. It also introduces a novel payoff structure to address the inherent uncertainties in human behavior. Ground truth pedestrian trajectories are then used to calibrate the game parameters and evaluate the model’s performance in approximating the motion decisions of human agents in interaction with interfering vehicles and pedestrians. The main contribution of the study is to develop an interactive human–vehicle decision-making framework toward realizing human–vehicle coexistence by capturing the effect of pedestrian–vehicle and pedestrian–pedestrian interactions on choice of walking strategies. The derived knowledge could be used in CAV navigation algorithms to provide the vehicle with more accurate predictions of pedestrian behavior, and in turn, improve CAV motion planning in human-populated areas.

A fuzzy interactive decision making algorithm for bilevel multi-followers programming with partial shared variables among followers

2009 ◽  
Vol 36 (7) ◽  
pp. 10471-10474 ◽  
Author(s):  
Guangmin Wang ◽  
Xianjia Wang ◽  
Zhongping Wan
Keyword(s):  
Decision Making ◽  
Shared Variables ◽  
Interactive Decision Making
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