Title

Decentralized Stochastic Planning with Anonymity in Interactions

Publication Type

Conference Proceeding Article

Publication Date

2014

Abstract

In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only do we introduce a general model model called D-SPAIT to capture anonymity in interactions, but also provide optimization based optimal and local-optimal solutions for generalizable sub-categories of D-SPAIT.

Keywords

Decentralized Planning with Uncertainty, DEC-MDP, Stochastic Routing, Selfish Routing

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

Proceedings of Twenty Eighth AAAI Conference of Artificial Intelligence: 27-31 July 2014, Québec City

First Page

2505

Last Page

2511

ISBN

9781577356615

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8525