Peter Stone's Selected Publications

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Ad hoc Teamwork with Behavior Switching Agents

Manish Ravula, Shani Alkobi and Peter Stone. Ad hoc Teamwork with Behavior Switching Agents. In International Joint Conference on Artificial Intelligence (IJCAI), August 2019.

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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."

BibTeX Entry

@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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