By Frans A. Oliehoek, Christopher Amato
This booklet introduces multiagent making plans below uncertainty as formalized by means of decentralized partly observable Markov selection methods (Dec-POMDPs). The meant viewers is researchers and graduate scholars operating within the fields of synthetic intelligence relating to sequential determination making: reinforcement studying, decision-theoretic making plans for unmarried brokers, classical multiagent making plans, decentralized keep watch over, and operations examine.
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Additional resources for A Concise Introduction to Decentralized POMDPs
Joint beliefs) to individual actions need to be speciﬁed. 5 Partially Observable Stochastic Games The Dec-POMDP is a very general model in that it deals with many types of uncertainty and multiple agents. However, it is only applicable to cooperative teams of agents, since it only speciﬁes a single (team) reward. The generalization of the Dec-POMDP is the partially observable stochastic game (POSG). It has the same components as a Dec-POMDP, except that it speciﬁes not a single reward function, but a collection of reward functions, one for each agent.
ND-POMDPs will be discussed in Chapter 8, where we will revisit this example. 4 Special Cases, Generalizations and Related Models Because solving Dec-POMDPs is complex (as will be discussed in the next chapter), much research has focused on special cases of Dec-POMDPs. This section brieﬂy treats a number of special cases that have received considerable attention. For a more comprehensive overview of all the special cases, the reader is referred to the articles by Pynadath and Tambe , Goldman and Zilberstein  and Seuken and Zilberstein .
1 Histories First, we deﬁne histories that are used in Dec-POMDPs. 1 When assuming slightly more information during planning, one approach is known to compress the space of internal states: Oliehoek et al. [2013a] present an approach to lossless clustering of individual histories. This, however, does not fundamentally change the representation of all the internal states (as is done when, for example, computing a belief for a POMDP); instead only some histories that satisfy a particular criterion are clustered together.