Name
Abstract | ||
|---|---|---|
Goal-driven autonomy (GDA) agents reason about goals while introspectively examining if their course of action matches their expectations. Many GDA agents adopt a hierarchical planning model to generate plans but limit reasoning with expectations to individual actions or projecting the expected state. In this paper we present a relaxation of this limitation. Taking advantage of hierarchical planning principles, our GDA agent elicits expectations that not only validate the next action but the overall plan trajectory without requiring validation against the complete state. We report on (1) a formalization of GDA's expectations that covers trajectories, (2) an implementation of these ideas and (3) benchmarking on two domains used in the GDA literature. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Course of action,Computer science,Autonomy,Artificial intelligence,Trajectory,Benchmarking,Process management |
DocType | Citations | PageRank |
Conference | 5 | 0.53 |
References | Authors | |
18 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dustin Dannenhauer | 1 | 16 | 4.55 |
| Hector Muñoz-Avila | 2 | 522 | 44.02 |