Towards An Autonomous Agent that Provides Automated Feedback on Students' Negotiation Skills (bibtex)
by Emmanuel Johnson, Jonathan Gratch, David DeVault
Abstract:
Although negotiation is an integral part of daily life, most people are unskilled negotiators. To improve one's skill set, a range of costly options including self-study guides, courses, and training programs are o⬚ered by various companies and educational institutions. For those who can't a⬚ord costly training options, virtual role playing agents o⬚er a low-costalternative. To be e⬚ective, these systems must allow students to engage in experiential learning exercises and provide personalized feedback on the learner's performance. In this paper, we show how a number of negotiation principles can be formalized and quanti⬚ed. We then establish the pedagogical relevance of several automatic metrics, and show that these metrics are signi⬚cantly correlated with negotiation outcomes in a human-agent negotiation. This illustrates the realism and helps to validate these principles. It also shows the potential of technology being used to quantify feedback that is traditionally provided through more qualitative approaches. The metrics we describe can provide students with personalized feedback on the errors they make in a negotiation exercise and thereby support guided experiential learning.
Reference:
Towards An Autonomous Agent that Provides Automated Feedback on Students' Negotiation Skills (Emmanuel Johnson, Jonathan Gratch, David DeVault), In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, International Foundation for Autonomous Agents and Multiagent Systems, 2017.
Bibtex Entry:
@inproceedings{johnson_towards_2017,
	address = {Sao Paulo, Brazil},
	title = {Towards {An} {Autonomous} {Agent} that {Provides} {Automated} {Feedback} on {Students}' {Negotiation} {Skills}},
	url = {http://dl.acm.org/citation.cfm?id=3091187},
	abstract = {Although negotiation is an integral part of daily life, most people are unskilled negotiators. To improve one's skill set, a range of costly options including self-study guides, courses, and training programs are o⬚ered by various companies and educational institutions. For those who can't a⬚ord costly training options, virtual role playing agents o⬚er a low-costalternative. To be e⬚ective, these systems must allow students to engage in experiential learning exercises and provide personalized feedback on the learner's performance. In this paper, we show how a number of negotiation principles can be formalized and quanti⬚ed. We then establish the pedagogical relevance of several automatic metrics, and show that these metrics are signi⬚cantly correlated with negotiation outcomes in a human-agent negotiation. This illustrates the realism and helps to validate these principles. It also shows the potential of technology being used to quantify feedback that is traditionally provided through more qualitative approaches. The metrics we describe can provide students with personalized feedback on the errors they make in a negotiation exercise and thereby support guided experiential learning.},
	booktitle = {Proceedings of the 16th {Conference} on {Autonomous} {Agents} and {MultiAgent} {Systems}},
	publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
	author = {Johnson, Emmanuel and Gratch, Jonathan and DeVault, David},
	month = may,
	year = {2017},
	keywords = {UARC, Virtual Humans},
	pages = {410--418}
}
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