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It was shown by Pynadath and Tambe (2002b) that under costfree communication, a joint communication policy that shares the local observations at each stage is optimal. , 2007b; Roth, Simmons, and Veloso, 2007; Goldman and Zilberstein, 2008). Although models with explicit communication seem more general than the models without, it is possible to transform the former to the latter. That is, a DecPOMDP-Com can be transformed to a Dec-POMDP (Goldman and Zilberstein, 2004; Seuken and Zilberstein, 2008).

At a specific time step t, this is: oit = o0i ,o1i , . . ,oti . 4) The joint observation history, o, is the observation history for all agents: o t = o1t , . . ,ont . 5) The set of observation histories for agent i at time t is denoted Oit = ×t Oi . Similar to the notation for action-observation histories, we also use Oi and O and the empty observation history is denoted o∅ . Similarly we can define the action history as follows. 10 (Action history). The action history (AH) for agent i, ai , is the sequence of actions an agent has performed.

A different extension of the DP for Dec-POMDPs algorithm is given by Amato, Carlin, and Zilberstein (2007b). Their approach, bounded DP (BDP), establishes a bound not on the used memory, but on the quality of approximation. In particular, BDP uses ǫ-pruning in each iteration. That is, a qiτ =k that is maximizing in some region of the multiagent belief space, but improves the value in this region by at most ǫ, is also pruned. Because iterated elimination using ǫ- pruning can still lead to an unbounded reduction in value, Amato et al.

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An Applications Guide for Op Amps - (Natl Semiconductor)


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