Name
Abstract | ||
|---|---|---|
Shannon's information theory can be used to quantify morphological and topological features of a collective of agents in an arbitrary environment. In particular the ability of individual agents to extract information locally about global features of the collective can be quantified. Here, we considered chains of agents in a grid world. The agents are equipped with local sensors. We then quantified the amount of information the sensors contain about certain features global to the chain. Furthermore, we compared the amount of locally available information to the amount of information the whole collective could in principle acquire about a feature in different contexts. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ALIFE.2011.5954661 | Artificial Life |
Keywords | Field | DocType |
information theory,mathematical morphology,sensors,Shannon information theory,global feature,information extraction,morphological feature,sensor,topological feature | Information theory,Data mining,Random variable,Computer science,Mathematical morphology,Information extraction,Artificial intelligence,Machine learning,Grid | Conference |
ISSN | ISBN | Citations |
2160-6374 | 978-1-61284-062-8 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Malte Harder | 1 | 0 | 0.34 |
| Daniel Polani | 2 | 549 | 70.25 |
| Chrystopher L. Nehaniv | 3 | 48 | 7.30 |