@inproceedings{5977,
  abstract     = {We consider the stochastic shortest path (SSP)problem for succinct Markov decision processes(MDPs), where the MDP consists of a set of vari-ables, and a set of nondeterministic rules that up-date the variables. First, we show that several ex-amples from the AI literature can be modeled assuccinct MDPs.  Then we present computationalapproaches for upper and lower bounds for theSSP problem: (a) for computing upper bounds, ourmethod is polynomial-time in the implicit descrip-tion of the MDP; (b) for lower bounds, we present apolynomial-time (in the size of the implicit descrip-tion) reduction to quadratic programming. Our ap-proach is applicable even to infinite-state MDPs.Finally, we present experimental results to demon-strate the effectiveness of our approach on severalclassical examples from the AI literature.},
  author       = {Chatterjee, Krishnendu and Fu, Hongfei and Goharshady, Amir and Okati, Nastaran},
  booktitle    = {Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence},
  isbn         = {978-099924112-7},
  issn         = {10450823},
  location     = {Stockholm, Sweden},
  pages        = {4700--4707},
  publisher    = {IJCAI},
  title        = {{Computational approaches for stochastic shortest path on succinct MDPs}},
  doi          = {10.24963/ijcai.2018/653},
  volume       = {2018},
  year         = {2018},
}

@inproceedings{1003,
  abstract     = {Network games (NGs) are played on directed graphs and are extensively used in network design and analysis. Search problems for NGs include finding special strategy profiles such as a Nash equilibrium and a globally optimal solution. The networks modeled by NGs may be huge. In formal verification, abstraction has proven to be an extremely effective technique for reasoning about systems with big and even infinite state spaces. We describe an abstraction-refinement methodology for reasoning about NGs. Our methodology is based on an abstraction function that maps the state space of an NG to a much smaller state space. We search for a global optimum and a Nash equilibrium by reasoning on an under- and an overapproximation defined on top of this smaller state space. When the approximations are too coarse to find such profiles, we refine the abstraction function. Our experimental results demonstrate the efficiency of the methodology.},
  author       = {Avni, Guy and Guha, Shibashis and Kupferman, Orna},
  issn         = {10450823},
  location     = {Melbourne, Australia},
  pages        = {70 -- 76},
  publisher    = {AAAI Press},
  title        = {{An abstraction-refinement methodology for reasoning about network games}},
  doi          = {10.24963/ijcai.2017/11},
  year         = {2017},
}