Name
Abstract | ||
|---|---|---|
An autonomous agent may largely benefit from its ability to reconstruct another agent's reasoning principles from records of past events and general knowledge about the world. In our approach, the meta-agent maintains a first-order logic theory, called the community model, yielding predictions about other agents' decisions. In this contribution we introduce a query-based collective reasoning process where the semi-collaborative meta-agents use active learning technique to improve their models. We provide empirical results that demonstrate the viability of the concept and show the benefits of collective meta-reasoning. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11559221_63 | CEEMAS |
Keywords | Field | DocType |
empirical result,semi-collaborative meta-agents,general knowledge,first-order logic theory,community model,autonomous agent,query-based collective reasoning process,past event,reasoning principle,active learning technique,collective meta-reasoning | Data science,Autonomous agent,Database query,Active learning,Theory,Computer science,First-order logic,Autonomous system (mathematics),General knowledge,Artificial intelligence,Active systems,Distributed computing | Conference |
Volume | ISSN | ISBN |
3690 | 0302-9743 | 3-540-29046-X |
Citations | PageRank | References |
2 | 0.52 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jan Tozicka | 1 | 58 | 14.00 |
| Filip Železný | 2 | 129 | 13.09 |
| Michal Pěchouček | 3 | 1134 | 133.88 |
| J Tozicka | 4 | 4 | 1.31 |
| M Pechoucek | 5 | 47 | 6.54 |