Exploring Uncertainty and Movement in Categorical Perception Using Robots
| Autor: | Nathaniel V. Powell, Josh C. Bongard |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Categorical perception
Class (computer programming) Computer science Movement (music) business.industry Robotics Cognition 02 engineering and technology 03 medical and health sciences 0302 clinical medicine Categorization Action (philosophy) 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Computer vision Artificial intelligence business 030217 neurology & neurosurgery Cognitive psychology |
| Zdroj: | Parallel Problem Solving from Nature – PPSN XIV ISBN: 9783319458229 PPSN |
| DOI: | 10.1007/978-3-319-45823-6_58 |
| Popis: | Cognitive agents are able to perform categorical perception through physical interaction (active categorical perception; ACP), or passively at a distance (distal categorical perception; DCP). It is possible that the former scaffolds the learning of the latter. However, it is unclear whether ACP indeed scaffolds DCP in humans and animals, nor how a robot could be trained to likewise learn DCP from ACP. Here we demonstrate a method for doing so which involves uncertainty: robots are trained to perform ACP when uncertain and DCP when certain. We found evidence in these trials that suggests such scaffolding may be occurring: Early during training, robots moved objects to reduce uncertainty as to their class (ACP), but later in training, robots exhibited less action and less class uncertainty (DCP). Furthermore, we demonstrate that robots trained in such a manner are more competent at categorizing novel objects than robots trained to categorize in other ways. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|