DROPS

Document

DOI: 10.4230/LIPIcs.CSL.2026.30

Reward Interfaces with Best-Effort Implementations

Authors: Rafael Dewes and Rayna Dimitrova

Published in: LIPIcs, Volume 363, 34th EACSL Annual Conference on Computer Science Logic (CSL 2026)

Abstract

Interface theories, notably interface automata, serve as expressive frameworks for component-based design, specifying component behavior and interaction in concurrent systems. Traditional interface formalisms specify assumptions that a component’s environment must satisfy and the guarantees that each component provides. This qualitative view of component interaction based on imposing strict assumptions and Boolean guarantees may, however, not be expressive enough to capture the system’s allowed or desired behaviors under different environments. In this paper, we introduce reward interfaces to support component-based design while accommodating multi-valued correctness requirements and adaptive best-effort satisfaction of component’s guarantees. Building upon interface automata, our framework enables modeling a rich class of quantitative component specifications. We propose formal notions of implementation, refinement and compatibility for reward interfaces. We study a class of reward interfaces with automata-based representations, for which we provide algorithms for checking compatibility and refinement, and existence of best-effort implementations. Our framework offers a comprehensive approach to reward interface specification and design.

Cite as

Rafael Dewes and Rayna Dimitrova. Reward Interfaces with Best-Effort Implementations. In 34th EACSL Annual Conference on Computer Science Logic (CSL 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 363, pp. 30:1-30:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{dewes_et_al:LIPIcs.CSL.2026.30,
  author =	{Dewes, Rafael and Dimitrova, Rayna},
  title =	{{Reward Interfaces with Best-Effort Implementations}},
  booktitle =	{34th EACSL Annual Conference on Computer Science Logic (CSL 2026)},
  pages =	{30:1--30:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-411-6},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{363},
  editor =	{Guerrini, Stefano and K\"{o}nig, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CSL.2026.30},
  URN =		{urn:nbn:de:0030-drops-254553},
  doi =		{10.4230/LIPIcs.CSL.2026.30},
  annote =	{Keywords: Component-based design, interface automata, quantitative specifications}
}

Document

DOI: 10.4230/LIPIcs.CONCUR.2017.13

The Robot Routing Problem for Collecting Aggregate Stochastic Rewards

Authors: Rayna Dimitrova, Ivan Gavran, Rupak Majumdar, Vinayak S. Prabhu, and Sadegh Esmaeil Zadeh Soudjani

Published in: LIPIcs, Volume 85, 28th International Conference on Concurrency Theory (CONCUR 2017)

Abstract

We propose a new model for formalizing reward collection problems on graphs with dynamically generated rewards which may appear and disappear based on a stochastic model. The robot routing problem is modeled as a graph whose nodes are stochastic processes generating potential rewards over discrete time. The rewards are generated according to the stochastic process, but at each step, an existing reward disappears with a given probability. The edges in the graph encode the (unit-distance) paths between the rewards' locations. On visiting a node, the robot collects the accumulated reward at the node at that time, but traveling between the nodes takes time. The optimization question asks to compute an optimal (or epsilon-optimal) path that maximizes the expected collected rewards. We consider the finite and infinite-horizon robot routing problems. For finite-horizon, the goal is to maximize the total expected reward, while for infinite horizon we consider limit-average objectives. We study the computational and strategy complexity of these problems, establish NP-lower bounds and show that optimal strategies require memory in general. We also provide an algorithm for computing epsilon-optimal infinite paths for arbitrary epsilon > 0.

Cite as

Rayna Dimitrova, Ivan Gavran, Rupak Majumdar, Vinayak S. Prabhu, and Sadegh Esmaeil Zadeh Soudjani. The Robot Routing Problem for Collecting Aggregate Stochastic Rewards. In 28th International Conference on Concurrency Theory (CONCUR 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 85, pp. 13:1-13:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

Copy BibTex To Clipboard

@InProceedings{dimitrova_et_al:LIPIcs.CONCUR.2017.13,
  author =	{Dimitrova, Rayna and Gavran, Ivan and Majumdar, Rupak and Prabhu, Vinayak S. and Soudjani, Sadegh Esmaeil Zadeh},
  title =	{{The Robot Routing Problem for Collecting Aggregate Stochastic Rewards}},
  booktitle =	{28th International Conference on Concurrency Theory (CONCUR 2017)},
  pages =	{13:1--13:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-048-4},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{85},
  editor =	{Meyer, Roland and Nestmann, Uwe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2017.13},
  URN =		{urn:nbn:de:0030-drops-77920},
  doi =		{10.4230/LIPIcs.CONCUR.2017.13},
  annote =	{Keywords: Path Planning, Graph Games, Quantitative Objectives, Discounting}
}

Document

DOI: 10.4230/LIPIcs.FSTTCS.2008.1751

Abstraction Refinement for Games with Incomplete Information

Authors: Rayna Dimitrova and Bernd Finkbeiner

Published in: LIPIcs, Volume 2, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (2008)

Abstract

Counterexample-guided abstraction refinement (CEGAR) is used in automated software analysis to find suitable finite-state abstractions of infinite-state systems. In this paper, we extend CEGAR to games with incomplete information, as they commonly occur in controller synthesis and modular verification. The challenge is that, under incomplete information, one must carefully account for the knowledge available to the player: the strategy must not depend on information the player cannot see. We propose an abstraction mechanism for games under incomplete information that incorporates the approximation of the players\' moves into a knowledge-based subset construction on the abstract state space. This abstraction results in a perfect-information game over a finite graph. The concretizability of abstract strategies can be encoded as the satisfiability of strategy-tree formulas. Based on this encoding, we present an interpolation-based approach for selecting new predicates and provide sufficient conditions for the termination of the resulting refinement loop.

Cite as

Rayna Dimitrova and Bernd Finkbeiner. Abstraction Refinement for Games with Incomplete Information. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science. Leibniz International Proceedings in Informatics (LIPIcs), Volume 2, pp. 175-186, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

Copy BibTex To Clipboard

@InProceedings{dimitrova_et_al:LIPIcs.FSTTCS.2008.1751,
  author =	{Dimitrova, Rayna and Finkbeiner, Bernd},
  title =	{{Abstraction Refinement for Games with Incomplete Information}},
  booktitle =	{IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science},
  pages =	{175--186},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-08-8},
  ISSN =	{1868-8969},
  year =	{2008},
  volume =	{2},
  editor =	{Hariharan, Ramesh and Mukund, Madhavan and Vinay, V},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2008.1751},
  URN =		{urn:nbn:de:0030-drops-17510},
  doi =		{10.4230/LIPIcs.FSTTCS.2008.1751},
  annote =	{Keywords: Automatic abstraction refinement, synthesis}
}

Search Results

Documents authored by Dimitrova, Rayna

Reward Interfaces with Best-Effort Implementations

Abstract

Cite as

The Robot Routing Problem for Collecting Aggregate Stochastic Rewards

Abstract

Cite as

Abstraction Refinement for Games with Incomplete Information

Abstract

Cite as

Thanks for your feedback!

Could not send message