Year: 2003
Type: report
Abstract: In this work we present an efficient environment representation based on the use of landmarks and language-based motion programs. The approach is targeted towards applications involving expansive, imprecisely known terrain without a single global map. To handle the uncertainty inherent in real-world applications a partially-observed controlled Markov chain structure is used in which the state space is the set of landmarks and the control space is a set of motion programs. Using dynamic programming, we derive an optimal controller to maximize the probability of arriving at a desired landmark after a finite number of steps. A simple simulation is presented to illustrate the approach.
DOI: 10.21236/ada439769
Institution: University of Maryland, College Park
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4240551798