2019
AAAI
AAAI 2019
Using Automated Agents to Teach Negotiation
Abstract
Abstract Negotiation is an integral part of our daily lives regardless of occupation. Although ubiquitous to our experience, we are never taught to negotiate. This lack of training presents many consequences from unfair salary negotiation to geopolitical ramification. The ability to resolve conflicts and negotiate is becoming more critical due to the rise of automated systems which look to replace various repetitive task jobs. In hopes of improving human negotiation skills, my work seeks to develop automated negotiation agents capable of providing personalized feedback. In this paper, I provide an overview of my past , current, and future work.
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Conference Pioneer
— AAAI 2019
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning
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Keyword Pioneer
— negotiation agent
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Artificial Intelligence > Core AI > Game AI
Artificial Intelligence > Core AI > Human-AI Interaction
Artificial Intelligence > Core AI > Multi-Agent Systems
Machine Learning > Learning Types > Multi-Agent Systems
Interdisciplinary > Education
Artificial Intelligence > Core AI > Game Theory