Abstraction-Based Decision Making for Statistical Properties (Invited Talk)

Authors Filip Cano , Thomas A. Henzinger , Bettina Könighofer , Konstantin Kueffner , Kaushik Mallik

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Author Details

Filip Cano
  • Graz University of Technology, Austria
Thomas A. Henzinger
  • Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
Bettina Könighofer
  • Graz University of Technology, Austria
Konstantin Kueffner
  • Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
Kaushik Mallik
  • Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria

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Filip Cano, Thomas A. Henzinger, Bettina Könighofer, Konstantin Kueffner, and Kaushik Mallik. Abstraction-Based Decision Making for Statistical Properties (Invited Talk). In 9th International Conference on Formal Structures for Computation and Deduction (FSCD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 299, pp. 2:1-2:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


Sequential decision-making in probabilistic environments is a fundamental problem with many applications in AI and economics. In this paper, we present an algorithm for synthesizing sequential decision-making agents that optimize statistical properties such as maximum and average response times. In the general setting of sequential decision-making, the environment is modeled as a random process that generates inputs. The agent responds to each input, aiming to maximize rewards and minimize costs within a specified time horizon. The corresponding synthesis problem is known to be PSPACE-hard. We consider the special case where the input distribution, reward, and cost depend on input-output statistics specified by counter automata. For such problems, this paper presents the first PTIME synthesis algorithms. We introduce the notion of statistical abstraction, which clusters statistically indistinguishable input-output sequences into equivalence classes. This abstraction allows for a dynamic programming algorithm whose complexity grows polynomially with the considered horizon, making the statistical case exponentially more efficient than the general case. We evaluate our algorithm on three different application scenarios of a client-server protocol, where multiple clients compete via bidding to gain access to the service offered by the server. The synthesized policies optimize profit while guaranteeing that none of the server’s clients is disproportionately starved of the service.

Subject Classification

ACM Subject Classification
  • Theory of computation → Online algorithms
  • Theory of computation → Computational pricing and auctions
  • Theory of computation → Abstraction
  • Abstract interpretation
  • Sequential decision making
  • Counter machines


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