Name
Abstract | ||
|---|---|---|
Although several recent studies have been published on goal reasoning (i.e., the study of agents that can self-select their goals), none have focused on the task of learning and acting on large state and action spaces. We introduce GDA-C, a case-based goal reasoning algorithm that divides the state and action space among cooperating learning agents. Cooperation between agents emerges because (1) they share a common reward function and (2) GDA-C formulates the goal that each agent needs to achieve. We claim that its case-based approach for goal formulation is critical to the agents’ performance. To test this claim we conducted an empirical study using the Wargus RTS environment, where we found that GDA-C outperforms its non-GDA ablation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-39056-2_12 | ICCBR |
Keywords | Field | DocType |
strategy,multiagent systems,reasoning | Computer science,Multi-agent system,Goal reasoning,Artificial intelligence,Case-based reasoning,Empirical research | Conference |
Citations | PageRank | References |
7 | 0.63 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ulit Jaidee | 1 | 45 | 3.55 |
| Héctor Muñoz-Avila | 2 | 674 | 55.13 |
| David W. Aha | 3 | 4103 | 620.93 |