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Full-Text Articles in Engineering
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 …
Spatio-Temporal Negotiation Protocols, Yi Luo
Spatio-Temporal Negotiation Protocols, Yi Luo
Electronic Theses and Dissertations
Canonical problems are simplified representations of a class of real world problems. They allow researchers to compare algorithms in a standard setting which captures the most important challenges of the real world problems being modeled. In this dissertation, we focus on negotiating a collaboration in space and time, a problem with many important real world applications. Although technically a multi-issue negotiation, we show that the problem can not be represented in a satisfactory manner by previous models. We propose the "Children in the Rectangular Forest" (CRF) model as a possible canonical problem for negotiating spatio-temporal collaboration. In the CRF problem, …
Multiagent Learning Through Indirect Encoding, David B. D'Ambrosio
Multiagent Learning Through Indirect Encoding, David B. D'Ambrosio
Electronic Theses and Dissertations
Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and …
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 …