TEA-modeling: Testing effective algorithms for modeling

Date of Publication

2009

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Remedios de Dios Bulos

Defense Panel Member

Nelson Marcos
Rigan Ap-Apid

Abstract/Summary

This research aims to evaluate Machine Learning (Reinforcement Learning and Best-Response Learning Algorithm) and Data Mining algorithms (Classification, Association, and Neural Network) in terms of providing rationality and human believability in an agent. Rationality considers the time, cost and space used up in reaching the goal. It concerns mainly on making the agent the best player of the game. Human believability, on the other hand, considers how an agent manifests human-like behavior as it competes with a player or an agent. It concerns mainly on fooling human players into thinking the agent is also a fellow human. An existing snake game from a previous research shall be used as a test bed to deploy and evaluate the agents. The testing process for the rationality aspect of the agent will be based on a methodology previously researched by John C. Duchi and John E. Laird (2000), while the believability aspect will be based on a research by Christian Bauckhage, et al. (2007). In the said methodology, rationality is hugely based on win-and-loss of the agent while believability is hugely based on observation ratings on how human-like the agent is.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU19879

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 v. (various foliations) : illustrations ; 28 cm. + one computer optical disc.

Keywords

Data Mining; Reinforcement learning; Intelligent control systems; Computer adaptive testing

Embargo Period

1-25-2022

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