Successive Recognition using Local State Models
2002 (English)In: Proceedings SSAB02 Symposium on Image Analysis: Lund, 2002, 9-12 p.Conference paper (Refereed)
This paper describes how a world model for successive recognition can be learned using associative learning. The learned world model consists of a linear mapping that successively updates a high-dimensional system state using performed actions and observed percepts. The actions of the system are learned by rewarding actions that are good at resolving state ambiguities. As a demonstration, the system is used to resolve the localisation problem in a labyrinth.
Place, publisher, year, edition, pages
2002. 9-12 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-21652OAI: oai:DiVA.org:liu-21652DiVA: diva2:245959