Abstract

In this paper, we examine a machine learning technique presented by Ishii et al. used to allow for learning in a multi-agent environment and apply an adaptation of this learning technique to the card game Sheephead. We then evaluate the effectiveness of our adaptation by running simulations against rule-based opponents. Multi-agent learning presents several layers of complexity on top of a single-agent learning in a stationary environment. This added complexity and increased state space is just beginning to be addressed by researchers. We utilize techniques used by Ishii et al. to facilitate this multi-agent learning. We model the environment of Sheephead as a partially observable Markov decision process (POMDP). This model will allow us to estimate the hidden state information inherent within the game of Sheephead. By first estimating this information, we restore the Markov property needed to model the problem as a Markov decison problem. We then solve the problem as Ishii et al. did by using a reinforcement learning technique based on the actor-critic algorithm. Though our results were positive, they were skewed by a rules-based implementation of part of the algorithm. Future research will be needed complete this implementation via a learning-based action predictor. Future research should also include testing against human subjects thus removing the rules-based bias inherent in the current algorithm. Given increased processing power, disk space, and improved AI techniques such as the techniques described above, complex multi-agent learning problems which once proved difficult may find solutions from the AI world.

Advisor

Dean Kelley

First Committee Member

Rebecca Bates

Second Committee Member

Daniel Swart

Date of Degree

2011

Language

english

Document Type

APP

Degree

Master of Science (MS)

Department

Computer Information Science

College

Science, Engineering and Technology

Creative Commons License

Creative Commons Attribution-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.

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