Name
Abstract | ||
|---|---|---|
Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multiagent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multiagent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions. |
Year | Venue | Field |
|---|---|---|
2016 | IJCAI | Computer science,Planner,Artificial intelligence |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian J. Muise | 1 | 176 | 22.68 |
| Paolo Felli | 2 | 28 | 5.27 |
| Tim Miller | 3 | 196 | 16.45 |
| Adrian R. Pearce | 4 | 301 | 31.88 |
| Liz Sonenberg | 5 | 802 | 119.89 |