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Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers
Exploiting Opponent Modeling For Learning In Multi-Agent Adversarial Games, Kennard R. Laviers
Electronic Theses and Dissertations
An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc way. In this dissertation, we introduce several methods for using opponent modeling, in the form of predictions about the players’ physical movements, to learn team policies. To explore the problem of decision-making in multi-agent adversarial scenarios, we use our …
A Contextual Approach To Learning Collaborative Behavior Via Observation, Cynthia L. Johnson
A Contextual Approach To Learning Collaborative Behavior Via Observation, Cynthia L. Johnson
Electronic Theses and Dissertations
This dissertation describes a novel technique to creating a simulated team of agents through observation. Simulated human teamwork can be used for a number of purposes, such as expert examples, automated teammates for training purposes and realistic opponents in games and training simulation. Current teamwork simulations require the team member behaviors be programmed into the simulation, often requiring a great deal of time and effort. None are able to observe a team at work and replicate the teamwork behaviors. Machine learning techniques for learning by observation and learning by demonstration have proven successful at observing behavior of humans or other …