3 Search Results for "Lozano-Perez, Tomas"

Document
DOI: 10.4230/LIPIcs.SAT.2025.30
Bridging Language Models and Symbolic Solvers via the Model Context Protocol

Authors: Stefan Szeider

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
This paper presents the MCP Solver, a system that bridges large language models with symbolic solvers through the Model Context Protocol (MCP). The system includes a server and a client component. The server provides an interface to constraint programming (via MiniZinc Python), propositional satisfiability and maximum satisfiability (both via PySAT), and SAT modulo Theories (via Python Z3). The client contains an agent that connects to the server via MCP and uses a language model to autonomously translate problem statements (given in English) into encodings through an incremental editing process and runs the solver. Our experiments demonstrate that this neurosymbolic integration effectively combines the natural language understanding of language models with robust solving capabilities across multiple solving paradigms.
Cite as

Stefan Szeider. Bridging Language Models and Symbolic Solvers via the Model Context Protocol. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 30:1-30:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{szeider:LIPIcs.SAT.2025.30,
  author =	{Szeider, Stefan},
  title =	{{Bridging Language Models and Symbolic Solvers via the Model Context Protocol}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{30:1--30:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.30},
  URN =		{urn:nbn:de:0030-drops-237649},
  doi =		{10.4230/LIPIcs.SAT.2025.30},
  annote =	{Keywords: Large Language Models, Agents, Constraint Programming, Satisfiability Solvers, Maximum Satisfiability, SAT Modulo Theories, Model Context Protocol}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2025.5
RESCUE: Multi-Robot Planning Under Resource Uncertainty and Objective Criticality

Authors: Franco Cordeiro, Samuel Tardieu, and Laurent Pautet

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Robot planning in distributed systems, such as drone fleets performing active perception missions, presents complex challenges. These missions require cooperation to achieve objectives like collecting sensor data or capturing images. Multi-robot systems offer significant advantages, including faster execution and increased robustness, as robots can compensate for individual failures. However, resource costs, affected by environmental factors such as wind or terrain, are highly uncertain, impacting battery consumption and overall performance. Mission objectives are often prioritized by criticality, such as retrieving data from low-battery sensors to prevent data loss. Addressing these priorities requires sophisticated scheduling to navigate high-dimensional state-action spaces. While heuristics are useful for approximating solutions, few approaches extend to multi-robot systems or adequately address cost uncertainty and criticality, particularly during replanning. The Mixed-Criticality (MC) paradigm, extensively studied in real-time scheduling, provides a framework for handling cost uncertainty by ensuring the completion of high-critical tasks. Despite its potential, the application of MC in distributed systems remains limited. To address the decision-making challenges faced by distributed robots operating under cost uncertainty and objective criticality, we propose four contributions: a comprehensive model integrating criticality, uncertainty, and robustness; distributed synchronization and replanning mechanisms; the incorporation of mixed-criticality principles into multi-robot systems; and enhanced resilience against robot failures. We evaluated our solution, named RESCUE, in a simulated scenario and show how it increases the robustness by reducing the oversizing of the system and completing up to 40% more objectives. We found an increase in resilience of the multi-robot system as our solution not only guaranteed the safe return of every non-faulty robot, but also reduced the effects of a faulty robot by up to 14%. We also computed the performance gain compared to using MCTS in a single robot of up to 2.31 for 5 robots.
Cite as

Franco Cordeiro, Samuel Tardieu, and Laurent Pautet. RESCUE: Multi-Robot Planning Under Resource Uncertainty and Objective Criticality. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 5:1-5:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{cordeiro_et_al:LIPIcs.ECRTS.2025.5,
  author =	{Cordeiro, Franco and Tardieu, Samuel and Pautet, Laurent},
  title =	{{RESCUE: Multi-Robot Planning Under Resource Uncertainty and Objective Criticality}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{5:1--5:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.5},
  URN =		{urn:nbn:de:0030-drops-235835},
  doi =		{10.4230/LIPIcs.ECRTS.2025.5},
  annote =	{Keywords: Multi-Robot Systems, Embedded Systems, Safety/Mixed-Critical Systems, Real-Time Systems, Monte-Carlo Tree Search}
}
Document
DOI: 10.4230/DagSemProc.08091.9
Learning Grammatical Models for Object Recognition

Authors: Meg Aycinena Lippow, Leslie Pack Kaelbling, and Tomas Lozano-Perez

Published in: Dagstuhl Seminar Proceedings, Volume 8091, Logic and Probability for Scene Interpretation (2008)

Abstract
Many object recognition systems are limited by their inability to share common parts or structure among related object classes. This capability is desirable because it allows information about parts and relationships in one object class to be generalized to other classes for which it is relevant. This ability has the potential to allow effective parameter learning from fewer examples and better generalization of the learned models to unseen instances, and it enables more efficient recognition. With this goal in mind, we have designed a representation and recognition framework that captures structural variability and shared part structure within and among object classes. The framework uses probabilistic geometric grammars (PGGs) to represent object classes recursively in terms of their parts, thereby exploiting the hierarchical and substitutive structure inherent to many types of objects. To incorporate geometric and appearance information, we extend traditional probabilistic context-free grammars to represent distributions over the relative geometric characteristics of object parts as well as the appearance of primitive parts. We describe an efficient dynamic programming algorithm for object categorization and localization in images given a PGG model. We also develop an EM algorithm to estimate the parameters of a grammar structure from training data, and a search-based structure learning approach that finds a compact grammar to explain the image data while sharing substructure among classes. Finally, we describe a set of experiments that demonstrate empirically that the system provides a performance benefit.
Cite as

Meg Aycinena Lippow, Leslie Pack Kaelbling, and Tomas Lozano-Perez. Learning Grammatical Models for Object Recognition. In Logic and Probability for Scene Interpretation. Dagstuhl Seminar Proceedings, Volume 8091, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{aycinenalippow_et_al:DagSemProc.08091.9,
  author =	{Aycinena Lippow, Meg and Kaelbling, Leslie Pack and Lozano-Perez, Tomas},
  title =	{{Learning Grammatical Models for Object Recognition}},
  booktitle =	{Logic and Probability for Scene Interpretation},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8091},
  editor =	{Anthony G. Cohn and David C. Hogg and Ralf M\"{o}ller and Bernd Neumann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08091.9},
  URN =		{urn:nbn:de:0030-drops-16113},
  doi =		{10.4230/DagSemProc.08091.9},
  annote =	{Keywords: Object recognition, grammars, structure learning}
}
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