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Ad hoc Teamwork with Behavior Switching Agents.
Manish Ravula, Shani Alkobi and Peter
Stone.
In International Joint Conference on Artificial Intelligence (IJCAI), August 2019.
As autonomous AI agents proliferate in the real world, they will increasingly need to cooperate with each other to achieve complex goals without always being able to coordinate in advance. This kind of cooperation, in which agents have to learn to cooperate on the fly, is called ad hoc teamwork. Many previous works investigating this setting assumed that teammates behave according to one of many predefined types that is fixed throughout the task. This assumption of stationarity in behaviors, is a strong assumption which cannot be guaranteed in many real-world settings. In this work, we relax this assumption and investigate settings in which teammates can change their types during the course of the task. This adds complexity to the planning problem as now an agent needs to recognize that a change has occurred in addition to figuring out what is the new type of the teammate it is interacting with. In this paper, we present a novel Convolutional-Neural-Network-based Change Point Detection (CPD) algorithm for ad hoc teamwork. When evaluating our algorithm on the modified predator prey domain, we find that it outperforms existing Bayesian CPD algorithms."
@InProceedings{IJCAI19-ravula,
author = {Manish Ravula, Shani Alkobi and Peter Stone},
title = {Ad hoc Teamwork with Behavior Switching Agents},
booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
year = {2019},
month = {August},
location = {Macau, China},
abstract = {
As autonomous AI agents proliferate in the real world, they will increasingly
need to cooperate with each other to achieve complex goals without always being
able to coordinate in advance. This kind of cooperation, in which agents have
to learn to cooperate on the fly, is called ad hoc teamwork. Many previous
works investigating this setting assumed that teammates behave according to
one of many predefined types that is fixed throughout the task. This
assumption of stationarity in behaviors, is a strong assumption which cannot
be guaranteed in many real-world settings. In this work, we relax this
assumption and investigate settings in which teammates can change their types
during the course of the task. This adds complexity to the planning problem
as now an agent needs to recognize that a change has occurred in addition to
figuring out what is the new type of the teammate it is interacting with.
In this paper, we present a novel Convolutional-Neural-Network-based
Change Point Detection (CPD) algorithm for ad hoc teamwork. When evaluating
our algorithm on the modified predator prey domain, we find that it
outperforms existing Bayesian CPD algorithms."
},
}
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