scholarly journals A Study on Negotiation skill as Moral Implementation

2016 ◽  
Vol null (51) ◽  
pp. 253-284
Author(s):  
Lee in tae
Keyword(s):  
Negotiation Skill

Predicting the Success of Mediation Requests Using Case Properties and Textual Information for Reducing the Burden on the Court

2021 ◽  
Author(s):  
Hsun-Ping Hsieh ◽  
JiaWei Jiang ◽  
Tzu-Hsin Yang ◽  
Renfen Hu
Keyword(s):  
Real World ◽  
Experimental Results ◽  
Criminal Cases ◽  
Time Saving ◽  
Textual Information ◽  
Negotiation Skill ◽  
Multiple Classifiers ◽  
Legal Case ◽  
Textual Data ◽  
F Measure

The success of mediation is affected by many factors, such as the context of the quarrel, personality of both parties, and the negotiation skill of the mediator, which lead to uncertainty for the predicting work. This paper takes a different approach from previous legal prediction research. It analyzes and predicts whether two parties in a dispute can reach an agreement peacefully through the conciliation of mediation. With the inference result, we can know if the mediation is a more practical and time-saving method to solve the dispute. Existing works about legal case prediction mostly focus on prosecution or criminal cases. In this work, we propose a LSTM-based framework, called LSTMEnsembler, to predict mediation results by assembling multiple classifiers. Among these classifiers, some are powerful for modeling the numerical and categorical features of case information, e.g., XGBoost and LightGBM; and, some are effective for dealing with textual data, e.g., TextCNN and BERT. The proposed LSTMEnsembler aims to not only combine the effectiveness of different classifiers intelligently, but also capture temporal dependencies from previous cases to boost the performance of mediation prediction. Our experimental results show that our proposed LSTMEnsembler can achieve 85.6% for F-measure on real-world mediation data.

Becoming a Negotiator: A Proposed Negotiation Skill Development Complexity Model

2013 ◽  
Vol 2013 (1) ◽  
pp. 11509
Author(s):  
Elizabeth Foster Clenney ◽  
Todd J. Maurer ◽  
Edward W. Miles
Keyword(s):  
Skill Development ◽  
Negotiation Skill

Special Issue on Human Symbiotic System

2010 ◽  
Vol 14 (7) ◽  
pp. 757-757 ◽  
Author(s):  
Yoichiro Maeda ◽  
Daisuke Katagami
Keyword(s):  
Research Society ◽  
Pattern Generation ◽  
Emotional Behavior ◽  
Service Robots ◽  
Human Beings ◽  
Special Issue ◽  
Symbiotic System ◽  
User Intent ◽  
Negotiation Skill ◽  
Bidirectional Communication

With opportunities for human beings to coexist with artificial agents and autonomous robots are increasing, high-level interactive communication between them is increasingly needed. These human symbiotic systems are used for research on basic intelligent interaction design principles and methods and bidirectional communication based on effective collaboration and symbiosis between human beings and robots, agents, and computers, also known as artifacts. The research society on gHuman Symbiotic System (HSS)h was implemented by the Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) in 2007. The HSS encourages academic and industrial discussion of research on Human-Agent Interaction (HAI), Human-Robot Interaction (HRI), and Human-Computer Interaction (HCI). The objective of this special issue is to activate and expand top-quality research of HSS theory and applications. Reflecting the fact that this research covers a wide range of topics, we invited researchers from fields including intelligent robotics, human-machine interfaces, and Kansei engineering to contribute. This issue thus provides much of the latest practical research on HSS, introduced by core members of the research society. Of the 22 papers received, 14 were accepted after input from two reviewers each. The first paper, by Y. Tamura et al., presents an attentive deskwork support system that delivers required items objects to deskworkers. The second, by H. Masuta et al., discusses an integrated perceptual system for intelligent service robots. The third, by S. Akiguchi, develops an automatic pattern generation system based on user impression. The fourth, by Y. Jiang et al., deals with a novel interface recognizing directional user intent based on forearm pressure exerted by the user of an omnidirectional walker. The fifth paper, by K. Terabayashi et al., investigates effects of preoperation on the experience of hands of different sizes by classifying preoperations based on the hand/object relationship. The sixth, by Y. Tamura et al., proposes segmenting a performerfs body imitating behavior observed based on a system from which values are obtained by reinforcement learning. The seventh, by D. Katagami et al., discusses group-adaptive behavior based on utterance contents and social standing of a robot. The eighth, by H. Yamaguchi et al., presents a system for using discounted utterances in spontaneous conversations applying text-mining technology. The ninth paper, by A. Otaki et al., focuses on the development of human negotiation skill through interaction between human players and computer agents in bargaining games. The tenth, by D. Katagami et al., is also related to human negotiation skill implementing human gestures in negotiation scenarios for three negotiation agents. The eleventh, by R. Taki et al., realizes interactive emotion communication - bidirectional communication based on emotional behavior between human beings and robots. The twelfth, by J. Ichino et al., investigates the psychological effects of color on online documents through a proposed online document interface. The thirteenth paper, by T. Ando et al., studies the robot facial effectiveness in human interpretation. The fourteenth, by T. Ando et al., models robot self-sufficiency applying an urge system focusing on autonomous emotion. This issue has addressed the importance of HSS and highlighted innovative approaches to the development of artificial system more friendly to users. We thank the authors and referees for their ongoing efforts, without which this issue would not have been possible.

Toward Strategic Human Skill Development Through Human and Agent Interaction: Improving Negotiation Skill by Interacting with Bargaining Agent

2010 ◽  
Vol 14 (7) ◽  
pp. 831-839 ◽  
Author(s):  
Atsushi Otaki ◽  
◽  
Kiyohiko Hattori ◽  
Keiki Takadama
Keyword(s):  
Human Subject ◽  
Skill Development ◽  
Profit Maximization ◽  
Bargaining Games ◽  
Negotiation Skill ◽  
Computer Agent ◽  
Human Skill ◽  
Agent Interaction ◽  
Human Skills ◽  
Human Player

This paper focuses on developing human skills through interaction between a human player and a computer agent, and explores its strategic method through experiments on the bargaining games where human players negotiate with computer agents. Specifically, human players negotiate with three types of agents: (a) strong/weak attitude agents making aggressive/defensive proposals in advantageous/disadvantageous situations; (b) fair agents making fair proposals; and (c) the “human-like” agents making mutually agreeable proposals as the number of games increases. Analysis of the human subject experiments has revealed the three major implications: (1) human players negotiating with the strong/weak attitude agents obtain the largest profit overall; (2) human players negotiating with “human-like” agents win many games; and (3) no relationship exists between profit maximization and a win of the games.

scholarly journals Locomotor skill acquisition in virtual reality shows sustained transfer to the real world

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Aram Kim ◽  
Nicolas Schweighofer ◽  
James M. Finley
Keyword(s):  
Virtual Reality ◽  
Skill Acquisition ◽  
Real World ◽  
Locomotor Training ◽  
Mobility Impairment ◽  
Mixed Effect ◽  
Obstacle Crossing ◽  
The Real ◽  
Negotiation Skill ◽  
Foot Clearance

Abstract Background Virtual reality (VR) is a potentially promising tool for enhancing real-world locomotion in individuals with mobility impairment through its ability to provide personalized performance feedback and simulate real-world challenges. However, it is unknown whether novel locomotor skills learned in VR show sustained transfer to the real world. Here, as an initial step towards developing a VR-based clinical intervention, we study how young adults learn and transfer a treadmill-based virtual obstacle negotiation skill to the real world. Methods On Day 1, participants crossed virtual obstacles while walking on a treadmill, with the instruction to minimize foot clearance during obstacle crossing. Gradual changes in performance during training were fit via non-linear mixed effect models. Immediate transfer was measured by foot clearance during physical obstacle crossing while walking over-ground. Retention of the obstacle negotiation skill in VR and retention of over-ground transfer were assessed after 24 h. Results On Day 1, participants systematically reduced foot clearance throughout practice by an average of 5 cm (SD 4 cm) and transferred 3 cm (SD 1 cm) of this reduction to over-ground walking. The acquired reduction in foot clearance was also retained after 24 h in VR and over-ground. There was only a small, but significant 0.8 cm increase in foot clearance in VR and no significant increase in clearance over-ground on Day 2. Moreover, individual differences in final performance at the end of practice on Day 1 predicted retention both in VR and in the real environment. Conclusions Overall, our results support the use of VR for locomotor training as skills learned in a virtual environment readily transfer to real-world locomotion. Future work is needed to determine if VR-based locomotor training leads to sustained transfer in clinical populations with mobility impairments, such as individuals with Parkinson’s disease and stroke survivors.

The Gender Gap in Real Estate Sales: Negotiation Skill or Agent Selection?

2013 ◽  
Vol 41 (3) ◽  
pp. 600-631 ◽  
Author(s):  
Philip Seagraves ◽  
Paul Gallimore
Keyword(s):  
Real Estate ◽  
Gender Gap ◽  
Negotiation Skill ◽  
Agent Selection ◽  
Real Estate Sales
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