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A Sufficient Statistic for Influence in Structured Multiagent Environments

Frans A. Oliehoek, Stefan Witwicki, and Leslie P. Kaelbling. A Sufficient Statistic for Influence in Structured Multiagent Environments. arXiv e-prints, pp. arXiv:1907.09278, July 2019. Published in JAIR.

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Abstract

Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computation intractability of principled solution methods. A body of work in AI [4, 3, 41, 45, 47, 2] has tried to mitigate this problem by trying to bring down interaction to its core: how does the policy of one agent influence another agent? If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than that of policies [45]. However, so far these notions of influence have been restricted in their applicability to special cases of interaction. In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) [33]. This generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings.

BibTeX Entry

@ARTICLE{Oliehoek19arxiv,
       author = {{Oliehoek}, Frans A. and {Witwicki}, Stefan and {Kaelbling}, Leslie P.},
        title = "{A Sufficient Statistic for Influence in Structured Multiagent Environments}",
      journal = {arXiv e-prints},
         year = 2019,
        month = jul,
          eid = {arXiv:1907.09278},
        pages = {arXiv:1907.09278},
archivePrefix = {arXiv},
       eprint = {1907.09278},
 primaryClass = {cs.AI},
        url   = {https://arxiv.org/abs/1907.09278},
        note  = {Published in JAIR.},
    keywords =   {nonrefereed, arxiv},
    abstract = {
    Making decisions in complex environments is a key challenge in artificial
    intelligence (AI). Situations involving multiple decision makers are
    particularly complex, leading to computation intractability of principled
    solution methods. A body of work in AI [4, 3, 41, 45, 47, 2] has tried to
    mitigate this problem by trying to bring down interaction to its core: how
    does the policy of one agent influence another agent? If we can find more
    compact representations of such influence, this can help us deal with the
    complexity, for instance by searching the space of influences rather than
    that of policies [45]. However, so far these notions of influence have been
    restricted in their applicability to special cases of interaction. In this
    paper we formalize influence-based abstraction (IBA), which facilitates the
    elimination of latent state factors without any loss in value, for a very
    general class of problems described as factored partially observable
    stochastic games (fPOSGs) [33]. This generalizes existing descriptions of
    influence, and thus can serve as the foundation for improvements in
    scalability and other insights in decision making in complex settings.        
    }
}

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