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Strategy improvement for concurrent reachability and turn-based stochastic safety games.

Krishnendu Chatterjee1, Luca de Alfaro2, Thomas A Henzinger1

  • 1IST Austria (Institute of Science and Technology Austria), Austria.

Journal of Computer and System Sciences
|October 31, 2015
PubMed
Summary

This study proves memoryless epsilon-optimal strategies exist for concurrent reachability games. New algorithms for concurrent reachability and turn-based safety games are presented, improving convergence to game values.

Keywords:
Concurrent gamesGame theoryReachability and safety objectivesStochastic gamesStrategy-improvement algorithms

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Area of Science:

  • Game theory
  • Theoretical computer science
  • Algorithmic game theory

Background:

  • Concurrent games involve simultaneous player moves determining state transitions.
  • Key objectives include safety (staying in states) and reachability (reaching states).
  • Existing proofs for memoryless optimal strategies in concurrent games can be complex.

Purpose of the Study:

  • To present a simpler, combinatorial proof for the existence of memoryless epsilon-optimal strategies in concurrent reachability games.
  • To introduce a strategy-improvement algorithm for concurrent games with reachability objectives.
  • To develop a strategy-improvement algorithm for turn-based stochastic games with safety objectives.

Main Methods:

  • A novel, elementary, and combinatorial proof technique for strategy existence.
  • Development and application of strategy-improvement (policy-iteration) algorithms.
  • Analysis of strategy sequences for monotonic convergence to game values.

Main Results:

  • A simplified proof demonstrates the existence of memoryless epsilon-optimal strategies for concurrent reachability games.
  • A new strategy-improvement algorithm is proposed for concurrent reachability games.
  • A strategy-improvement algorithm is presented for turn-based stochastic games with safety objectives.

Conclusions:

  • The study provides elementary proofs and efficient algorithms for solving concurrent and turn-based stochastic games.
  • Algorithms guarantee monotonic convergence of player-1 winning probabilities to the game's value.
  • Findings contribute to the understanding and computational solution of complex game-theoretic problems.