id: 05282111
dt: a
an: 05282111
au: Plamondon, Pierrick; Chaib-draa, Brahim; Benaskeur, Abder Rezak
ti: An efficient resource allocation approach in real-time stochastic
    environment.
so: Lamontagne, Luc (ed.) et al., Advances in artificial intelligence. 19th
    conference of the Canadian Society for Computational Studies of
    Intelligence, Canadian AI 2006, Québec City, Québec, Canada, June
    7‒9, 2006. Proceedings. Berlin: Springer (ISBN
    978-3-540-34628-9/pbk). Lecture Notes in Computer Science 4013. Lecture
    Notes in Artificial Intelligence, 49-60 (2006).
py: 2006
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/11766247_5
ab: Summary: We are interested in contributing to solving effectively a
    particular type of real-time stochastic resource allocation problem.
    Firstly, one distinction is that certain tasks may create other tasks.
    Then, positive and negative interactions among the resources are
    considered, in achieving the tasks, in order to obtain and maintain an
    efficient coordination. A standard Multiagent Markov Decision Process
    (MMDP) approach is too prohibitive to solve this type of problem in
    real-time. To address this complex resource management problem, the
    merging of an approach which considers the complexity associated to a
    high number of different resource types (i.e. Multiagent Task
    Associated Markov Decision Processes (MTAMDP)), with an approach which
    considers the complexity associated to the creation of task by other
    tasks (i.e. Acyclic Decomposition) is proposed. The combination of
    these two approaches produces a near-optimal solution in much less time
    than a standard MMDP approach.
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