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Simulation and Performance Assessment of Poker Agents

  • Conference paper
Multi-Agent-Based Simulation XIII (MABS 2012)

Abstract

The challenge in developing agents for incomplete information games resides in the fact that the maximum utility decision for given information set is not always ascertainable. For large games like Poker, the agents’ strategies require opponent modeling, since Nash equilibrium strategies are hard to compute. In light of this, simulation systems are indispensable for accurate assessment of agents’ capabilities. Nevertheless, current systems do not accommodate the needs of computer poker research since they were designed mainly as an interface for human players competing against agents. In order to contribute towards improving computer poker research, a new simulation system was developed. This system introduces scientifically unexplored game modes with the purpose of providing a more realistic simulation environment, where the agent must play carefully to manage its initial resources. An evolutionary simulation feature was also included so as to provide support for the improvement of adaptive strategies. The simulator has built-in odds calculation, an agent development API, other platform agents and several variants support and an agent classifier with realistic game indicators including exploitability estimation. Tests and qualitative analysis have proven this simulator to be faster and better suited for thorough agent development and performance assessment.

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Teófilo, L.F., Rossetti, R., Reis, L.P., Cardoso, H.L., Nogueira, P.A. (2013). Simulation and Performance Assessment of Poker Agents. In: Giardini, F., Amblard, F. (eds) Multi-Agent-Based Simulation XIII. MABS 2012. Lecture Notes in Computer Science(), vol 7838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38859-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-38859-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38858-3

  • Online ISBN: 978-3-642-38859-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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