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Negotiating Team Formation Using Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. ...
We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent ...
Some previous work in multi-agent (deep) reinforcement learning for negotiation has cast the problem as one of communication, rather than team formation (e.g. ...
arXiv:2010.10380v1
fatcat:6ztnplwaoncahmloxr6f25orfi
Multi-agent Reinforcement Learning for Decentralized Coalition Formation Games
2021
AAAI Conference on Artificial Intelligence
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We propose novel decentralized heuristic learning and multi-agent reinforcement learning (MARL) approaches to train agents, and we use game-theoretic evaluation criteria such as optimality, stability, ...
We study the application of multi-agent reinforcement learning for game-theoretical problems. ...
In (Bachrach et al. 2020) , authors propose a framework for training agents to negotiate and form teams using deep RL, they have also used the grid (spatially extended) environment in their experiments ...
dblp:conf/aaai/Taywade21
fatcat:wgfjgcmcova7fl67wnea53j73u
An Integrated Multilevel Learning Approach to Multiagent Coalition Formation
2003
International Joint Conference on Artificial Intelligence
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At a tactical level, we use reinforcement learning to identify viable candidates based on their potential utility to the coalition, and case-based learning to refine negotiation strategies. ...
At a strategic level, we use distributed, cooperative casebased learning to improve general negotiation strategies. ...
Conclusions We have described an integrated multilevel approach to coalition formation, using case-based learning and reinforcement learning to learn better tactics as the agent solves a problem, and distributed ...
dblp:conf/ijcai/SohL03
fatcat:mipba3gk4fe4vnnt26m4cbuuw4
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
[article]
2020
arXiv
pre-print
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Using symmetric zero-sum matrix games, we demonstrate formally that alliance formation may be seen as a social dilemma, and empirically that na\"ive multi-agent reinforcement learning therefore fails to ...
We introduce a toy model of economic competition, and show how reinforcement learning may be augmented with a peer-to-peer contract mechanism to discover and enforce alliances. ...
[71] defines a framework for reinforcement learning in N -player games, which could be used to scale up our results, but does not explicitly tackle the issue of alliance formation. ...
arXiv:2003.00799v1
fatcat:evsc7hmlvndbrppcfwrnwzcvaq
NegotioPoly: a holistic gaming approach to negotiation teaching
2021
Organization Management Journal
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Originality/value NegotioPoly is an experiential learning tool that closes the gap between negotiation theory and pedagogy while providing deep learning and realistic practice opportunities for students ...
Social implications NegotioPoly reinforces core business competencies such as negotiation, problem solving, analytical skills and the ability to work in teams that employers look for and, therefore, is ...
in negotiation, coalition and partnership formation and multi-party and multi-round negotiations. ...
doi:10.1108/omj-02-2021-1160
fatcat:plfy44azbrbkfp2ffmxosujily
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
[article]
2018
arXiv
pre-print
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Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework. ...
Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. ...
The future work will investigate complex driving policies such as roundabout negotiations, cooperative learning between CAVs and deep reinforcement learning to traverse larger state spaces. ...
arXiv:1709.04622v4
fatcat:e7ddmcvc2nh2rf6jc653xizyt4
Using Problem-Based Learning to Develop Skills in Solving Unstructured Problems
2004
Journal of Management Education
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The article concludes by raising a related issue: If graduates possess skills in solving unstructured problems, will businesses be receptive to their use? ...
The course's design is described, including its learning outcomes, PBL projects, associated learning activities, and methods of assessing learning. ...
Our college is now working on ways to reinforce course learning so that when learned, it is called for again in later courses. ...
doi:10.1177/1052562903257310
fatcat:wgf3y25hjbfrjfzjqvkhrlk65i
Applying Constructivism Theories to Online Teaching at the Durban University of Technology
[chapter]
2021
Covid-19: Interdisciplinary Explorations of Impacts on Higher Education
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In constructivism, learning is socially mediated and interaction is essential for negotiation of meaningful learning. ...
Hence, the teaching and learning process is about sharing experiences and negotiating socially constituted knowledge. ...
doi:10.52779/9781991201195/07
fatcat:2cddxpoqkfcofpomehvl5pxbpi
International Field Studies: Tools For Enhancing Cultural Literacy
2011
Journal of College Teaching & Learning (TLC)
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This IFS provides participants with an experientially based learning opportunity to understand that there are leadership universals that every executive and manager needs to practice in order to be world-class ...
After joint presentations of the -agreement,‖ each Chinese-American negotiation team enjoyed a small-group dinner. ...
teams. ...
doi:10.19030/tlc.v2i2.1774
fatcat:y2dubcfalnfgjhj4wziehkm2xm
Action Learning in Higher Education: an investigation of its potential to develop professional capability
2004
Studies in Higher Education
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Third year student consultants reported using less surface and more deep approaches to their learning in this course design than in concurrent courses taught along more conventional (i.e. lecture and tutorial ...
This study investigated the extent to which a course, designed using peer and action learning principles to function as an'on campus practicum', can develop the professional capabilities of students. ...
We analysed students' responses using a 2 (teaching format: action learning vs. conventional [lecture/ tutorial]) Q 6 (design dimensions: staff consultation and negotiation, student activity and involvement ...
doi:10.1080/0307507042000236371
fatcat:xects6ongvcrlaloaubheae4ji
Mega-Simulations in Negotiation Teaching: Extraordinary Investments with Extraordinary Benefits
2008
Negotiation journal
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A mega-simulation is a complex-negotiations teaching exercise involving complicated issues and challenging conditions that is undertaken by three or more teams of students. ...
In this article, I draw on two decades of teaching with mega-simulations in international business negotiation courses to discuss potential learning goals for this type of experiential exercise, effective ...
Figure One Foci of Learning
Facet of phenomenon
Content Goals With respect to learning goals related to negotiation content, let me use international business negotiation as an example. ...
doi:10.1111/j.1571-9979.2008.00187.x
fatcat:kzdg4iw6hzarvm7x7plyn7t34q
Planning and Managing the Negotiation Exercise
2015
Social Science Research Network
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This specific negotiation exercise is used to illustrate the important relationship between learning process and outcome and the critical link that the debriefing plays in achieving such outcomes. ...
The professional teacher/trainer may use these objectives as an example in developing their own learning and teaching approach. ...
Building and reinforcing strategic skill is fundamental to negotiation teaching and learning. ...
doi:10.2139/ssrn.2549762
fatcat:n55funtcxjg5jdu3gs4b2kgl6y
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
[article]
2019
arXiv
pre-print
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As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. ...
This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions ...
Recent advances in single and multi-agent RL make use of deep ANNs as function approximators; this emerging paradigm is known as deep reinforcement learning (DRL). ...
arXiv:1909.02964v1
fatcat:esizgbvjfbejfacjpwq5c4ujze
Multi-agent based manufacturing: current trends and challenges
2021
2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
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The advent of Industry 4.0 and the development of future industrial applications can be achieved using Cyber-Physical Systems (CPS). ...
For example, the inclusion of deep reinforcement learning could be utilised for the creation of dynamic learning agents. ...
Pas communicate with RA teams through bids. RAs use them to enable task negotiation and PA to formulate better environmental understating. ...
doi:10.1109/etfa45728.2021.9613555
fatcat:wdhgqjg4q5crbemdfdgf5yoyfy
Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment
2020
IEEE Access
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INDEX TERMS Multi-AUV hunting, dynamic prediction, deep reinforcement learning, desired hunting point. ...
Finally, the AUVs arrive at desired hunting points rapidly through deep reinforcement learning (DRL) algorithm to achieve hunting the moving target. ...
The hunting AUVs in different locations predicted the movement of the target and used the method of deep reinforcement learning to plan the hunting path. ...
doi:10.1109/access.2020.3013032
fatcat:ix2wu2p5ebbsxlytmwjemtsavq
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