Computing Agent Competency in First Order Markov Processes
Artificial agents are usually designed to achieve specific goals. An agent's competency can be defined as its ability to accomplish its goals under different conditions. This thesis restricts attention to a specific type of goal, namely reaching a desired state without exceeding a tolerance thr...
Main Author: | Cao, Xuan |
---|---|
Format: | Others |
Published: |
BYU ScholarsArchive
2021
|
Subjects: | |
Online Access: | https://scholarsarchive.byu.edu/etd/9336 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=10345&context=etd |
Similar Items
-
MCMCpack: Markov Chain Monte Carlo in R
by: Andrew D. Martin, et al.
Published: (2011-08-01) -
Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects
by: Sudeep Pradhan, et al.
Published: (2017-12-01) -
Sampling approaches in Bayesian computational statistics with R
by: Sun, Wenwen
Published: (2010) -
Bayesian Models for Repeated Measures Data Using Markov Chain Monte Carlo Methods
by: Li, Yuanzhi
Published: (2016) -
Parameter Estimation in Population Balance through Bayesian Technique Markov Chain Monte Carlo
by: Carlos H.R. Moura, et al.
Published: (2021-04-01)