The Interacting Multiple Models Algorithm with State-Dependent Value Assignment

<p>The value of a state is a measure of its worth, so that, for example, waypoints have high value and regions inside of obstacles have very small value. We propose two methods of incorporating world information as state-dependent modifications to the interacting multiple models (IMM) algorithm, and then we use a game's player-controlled trajectories as ground truths to compare the normal IMM algorithm to versions with our proposed modifications. The two methods involve modifying the model probabilities in the update step and modifying the transition probability matrix in the mixing step based on the assigned values of different target states. The state-dependent value assignment modifications are shown experimentally to perform better than the normal IMM algorithm in both estimating the target's current state and predicting the target's next state.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering

Year

2012

Author dc:creator

• Rastgoufard, Rastin

Contributors dc:contributor

• Dr. X. R. Li
• Dr. H. Chen
• Dr. V. Jilkov

• IMM
• state-dependent
• constraints
• penalty function
• waypoints
• obstacles
• Artificial Intelligence and Robotics
• Signal Processing
• Statistical Models

Repository record dc:identifier

https://scholarworks.uno.edu/td/1477

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-2421