Abstract
We consider the problem of cooperative multi-agent planning (MAP) in a deterministic environment, with a completely observable state. Most tractable algorithms for MAP
problems assume sparse interactions among agents and exploitable problem structure. We consider a specific model for
representing interactions among agents using soft cooperation constraints (SCC), which enables a compact representation of symmetric dependencies. We present a two-step planning algorithm that breaks down a multi-agent problem with
K agents, to multiple instances of independent single-agent
problems, such that the aggregation of the single-agent plans
is optimal for the group. We propose an efficient algorithm
for computing the single-agent optimal plan under a given set
of soft constraints, denoted as the response function. We then
utilize a well-known graphical model for efficient min-sum
optimization in order to find the optimal aggregation of the
single agent response functions. The proposed planning algorithm is complete, optimal, and effective when interactions
among the agents are sparse. We further indicate some useful
extensions to the basic SCC formulation presented here.
problems assume sparse interactions among agents and exploitable problem structure. We consider a specific model for
representing interactions among agents using soft cooperation constraints (SCC), which enables a compact representation of symmetric dependencies. We present a two-step planning algorithm that breaks down a multi-agent problem with
K agents, to multiple instances of independent single-agent
problems, such that the aggregation of the single-agent plans
is optimal for the group. We propose an efficient algorithm
for computing the single-agent optimal plan under a given set
of soft constraints, denoted as the response function. We then
utilize a well-known graphical model for efficient min-sum
optimization in order to find the optimal aggregation of the
single agent response functions. The proposed planning algorithm is complete, optimal, and effective when interactions
among the agents are sparse. We further indicate some useful
extensions to the basic SCC formulation presented here.
| Original language | American English |
|---|---|
| Title of host publication | The 30th International Conference on Automated Planning and Scheduling |
| Subtitle of host publication | Proceedings of the 6th Workshop on Distributed and Multi-Agent Planning |
| State | Published - 2020 |
| Event | Workshop on Distributed and Multi-Agent Planning - Duration: 26 Oct 2020 → 27 Oct 2020 |
Conference
| Conference | Workshop on Distributed and Multi-Agent Planning |
|---|---|
| Abbreviated title | DMAP |
| Period | 26/10/20 → 27/10/20 |