Simulating Collaborative Learning through Decision- Theoretic Agents (bibtex)
by David V Pynadath, Ning Wang, Richard Yang
Abstract:
Simulation for team training has a long history of success in medical care and emergency response. In fields where individuals work together to make decisions and perform actions under extreme time pressure and risk (as in military teams), simulations offer safe and repeatable environments for teams to learn and practice without real-world consequences. In our team-based training simulation, we use intelligent agents to represent individual learners and to autonomously generate behavior while learning to perform a joint task. Our agents are built upon PsychSim, a social-simulation framework that uses decision theory to provide domain-independent, quantitative algorithms for representing and reasoning about uncertainty and conflicting goals. We present a collaborative learning testbed in which two PsychSim agents performed a joint “capture-the-flag” mission in the presence of an enemy agent. The testbed supports a reinforcement-learning capability that enables the agents to revise their decision-theoretic models based on their experiences in performing the target task. We can “train” these agents by having them repeatedly perform the task and refine their models through reinforcement learning. We can then “test” the agents by measuring their performance once their learning has converged to a final policy. Repeating this trainand-test cycle across different parameter settings (e.g., priority of individual vs. team goals) and learning configurations (e.g., train with the same teammate vs. train with different teammates) yields a reusable methodology for characterizing the learning outcomes and measuring the impact of such variations on training effectiveness.
Reference:
Simulating Collaborative Learning through Decision- Theoretic Agents (David V Pynadath, Ning Wang, Richard Yang), In Proceedings of the Assessment and Intervention during Team Tutoring Workshop, CEUR-WS.org, 2018.
Bibtex Entry:
@inproceedings{pynadath_simulating_2018,
	address = {London, UK},
	title = {Simulating {Collaborative} {Learning} through {Decision}- {Theoretic} {Agents}},
	url = {http://ceur-ws.org/Vol-2153/paper5.pdf},
	abstract = {Simulation for team training has a long history of success in medical care and emergency response. In fields where individuals work together to make decisions and perform actions under extreme time pressure and risk (as in military teams), simulations offer safe and repeatable environments for teams to learn and practice without real-world consequences. In our team-based training simulation, we use intelligent agents to represent individual learners and to autonomously generate behavior while learning to perform a joint task. Our agents are built upon PsychSim, a social-simulation framework that uses decision theory to provide domain-independent, quantitative algorithms for representing and reasoning about uncertainty and conflicting goals. We present a collaborative learning testbed in which two PsychSim agents performed a joint “capture-the-flag” mission in the presence of an enemy agent. The testbed supports a reinforcement-learning capability that enables the agents to revise their decision-theoretic models based on their experiences in performing the target task. We can “train” these agents by having them repeatedly perform the task and refine their models through reinforcement learning. We can then “test” the agents by measuring their performance once their learning has converged to a final policy. Repeating this trainand-test cycle across different parameter settings (e.g., priority of individual vs. team goals) and learning configurations (e.g., train with the same teammate vs. train with different teammates) yields a reusable methodology for characterizing the learning outcomes and measuring the impact of such variations on training effectiveness.},
	booktitle = {Proceedings of the {Assessment} and {Intervention} during {Team} {Tutoring} {Workshop}},
	publisher = {CEUR-WS.org},
	author = {Pynadath, David V and Wang, Ning and Yang, Richard},
	month = jun,
	year = {2018},
	keywords = {Social Simulation, UARC}
}
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