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| Publication date | 30/05/2023 |
|---|---|
| Host publication | Proceedings of AAMAS-2023 |
| Place of Publication | New York |
| Publisher | ACM |
| Pages | 140-142 |
| Number of pages | 3 |
| Volume | 2023-May |
| ISBN (print) | 9781450394321 |
| <mark>Original language</mark> | English |
| Event | 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 - London, United Kingdom Duration: 29/05/2023 → 2/06/2023 |
| Conference | 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 |
|---|---|
| Country/Territory | United Kingdom |
| City | London |
| Period | 29/05/23 → 2/06/23 |
| Name | Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS |
|---|---|
| ISSN (Print) | 1548-8403 |
| Conference | 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023 |
|---|---|
| Country/Territory | United Kingdom |
| City | London |
| Period | 29/05/23 → 2/06/23 |
In this paper, we present On-line Estimators for Ad-hoc Task Execution (OEATE), a novel algorithm for teammates' type and parameter estimation in decentralised task execution. We show theoretically that our algorithm can converge to perfect estimations, under some assumptions, as the number of tasks increases. Empirically, we show better performance against our baselines while estimating type and parameters in several different settings. This is an extended abstract of our JAAMAS paper available online [9].