Name
Abstract | ||
|---|---|---|
An important problem for societies of natural and artificial animals is to converge upon a similar language in order to communicate We call this the language convergence problem In this paper we study the complexity of finding the optimal (in terms of time to convergence) algorithm for language convergence We map the language convergence problem to instances of a Decentralized Partially Observable Markov Decision Process to show that the complexity can vary from P-complete to NEXP-complete based on the scenario being studied. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840541_66 | SAB |
Keywords | Field | DocType |
language convergence problem,language convergence,similar language,important problem,artificial animal,decentralized partially observable markov,decision process,optimal policy | Convergence (routing),Artificial life,Decentralised system,Sparse language,Computer science,Partially observable Markov decision process,Convergence problem,Artificial intelligence,Minimum time | Conference |
Volume | ISSN | ISBN |
4095 | 0302-9743 | 3-540-38608-4 |
Citations | PageRank | References |
1 | 0.37 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kiran Lakkaraju | 1 | 445 | 36.90 |
| Les Gasser | 2 | 1601 | 261.00 |