Not all agents are equal: scaling up distributed POMDPs for agent networks

Many applications of networks of agents, including mobile sensor networks, unmanned air vehicles, autonomous underwater vehicles, involve 100s of agents acting collaboratively under uncertainty. Distributed Partially Observable Markov Decision Problems (Distributed POMDPs) are well-suited to address such applications, but so far, only limited scale-ups of up to five agents have been demonstrated. This paper escalates the scale-up, presenting an algorithm called FANS, increasing the number of agents in distributed POMDPs for the first time into double digits. FANS is founded on finite state machines (FSMs) for policy representation and expoits these FSMs to provide three key contributions: (i) Not all agents within an agent network need the same expressivity of policy representation; FANS introduces novel heuristics to automatically vary the FSM size in different agents for scaleup; (ii) FANS illustrates efficient integration of its FSM-based policy search within algorithms that exploi...

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Janusz Marecki, Tapana Gupta, Pradeep Varakantham, Milind Tambe, Makoto Yokoo
• Keywords: Agent Networks, ATAL 2008, Finite State Machines, Intelligent Agents, Observable Markov Decision