By Frans A. Oliehoek, Christopher Amato

ISBN-10: 3319289276

ISBN-13: 9783319289274

ISBN-10: 3319289292

ISBN-13: 9783319289298

This publication introduces multiagent making plans below uncertainty as formalized by means of decentralized in part observable Markov determination strategies (Dec-POMDPs). The meant viewers is researchers and graduate scholars operating within the fields of synthetic intelligence on the topic of sequential selection making: reinforcement studying, decision-theoretic making plans for unmarried brokers, classical multiagent making plans, decentralized keep watch over, and operations study.

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Get A Concise Introduction to Decentralized POMDPs PDF

This ebook introduces multiagent making plans below uncertainty as formalized via decentralized partly observable Markov determination techniques (Dec-POMDPs). The meant viewers is researchers and graduate scholars operating within the fields of man-made intelligence on the topic of sequential selection making: reinforcement studying, decision-theoretic making plans for unmarried brokers, classical multiagent making plans, decentralized keep watch over, and operations study.

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A factored, n-agent Dec-MDP is said to be reward-independent if there is a monotonically nondecreasing function f such that 4 Some factored models also consider an s0 component that is a property of the environment and is not affected by any agent actions. 24 2 The Decentralized POMDP Framework R(s,a) = f (R1 (s1 ,a1 ), . . 3) If this is the case, the global reward is maximized by maximizing local rewards. 4) i∈D are frequently used. 3 Centralized Models: MMDPs and MPOMDPs In the discussion so far we have focused on models that, in the execution phase, are truly decentralized: they model agents that select actions based on local observations.

N} is the set of n agents. 6 In the most general form, the next internal states would explicitly depend on the taken action too (not shown, to avoid clutter). 4 Special Cases, Generalizations and Related Models 27 I2,t internal states I2,t+1 I1,t I1,t+1 o2,t actions / observation o1,t at ot states a2,t+1 o1,t+1 a1,t a1,t+1 at+1 ot+1 st+1 st Dec-POMDP agent component a1,t+1 o2,t+1 a2,t o1,t a2,t+1 o1,t+1 a1,t o2,t actions / observation o2,t+1 a2,t ... Rt+1 Rt Dec-POMDP environment t (internal) state transition observation probabilities identity (for replicated actions and observation) ts + 1 rewards policy Fig.

If rewards are to be observed, they should be made part of the observation. 3 Example Domains To illustrate the Dec-POMDP model, we discuss a number of example domains and benchmark problems. These range from the toy (but surprisingly hard) ‘decentralized tiger’ problem to multirobot coordination and communication network optimization. 1 Dec-Tiger We will consider the decentralized tiger (D EC -T IGER) problem Nair et al. [2003c]— a frequently used Dec-POMDP benchmark—as an example. It concerns two agents that are standing in a hallway with two doors.

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A Concise Introduction to Decentralized POMDPs by Frans A. Oliehoek, Christopher Amato


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