3 Search Results for "Nguyen, Alain"

Document

DOI: 10.4230/LIPIcs.ECRTS.2024.6

Optimizing Per-Core Priorities to Minimize End-To-End Latencies

Authors: Francesco Paladino, Alessandro Biondi, Enrico Bini, and Paolo Pazzaglia

Published in: LIPIcs, Volume 298, 36th Euromicro Conference on Real-Time Systems (ECRTS 2024)

Abstract

Logical Execution Time (LET) allows decoupling the schedule of real-time periodic tasks from their communication, with the advantage of isolating the communication pattern from the variability of the schedule. However, when such tasks are organized in chains, the usage of LET at the task level does not necessarily transfer the same LET properties to the chain level. In this paper, we extend a LET-like model from tasks to chains spanning over multiple cores. We leverage the designed constant latency chains to optimize per-core priority assignment. Finally, we also provide a set of heuristic algorithms, that are compared in a large-scale experimental evaluation.

Cite as

Francesco Paladino, Alessandro Biondi, Enrico Bini, and Paolo Pazzaglia. Optimizing Per-Core Priorities to Minimize End-To-End Latencies. In 36th Euromicro Conference on Real-Time Systems (ECRTS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 298, pp. 6:1-6:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{paladino_et_al:LIPIcs.ECRTS.2024.6,
  author =	{Paladino, Francesco and Biondi, Alessandro and Bini, Enrico and Pazzaglia, Paolo},
  title =	{{Optimizing Per-Core Priorities to Minimize End-To-End Latencies}},
  booktitle =	{36th Euromicro Conference on Real-Time Systems (ECRTS 2024)},
  pages =	{6:1--6:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-324-9},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{298},
  editor =	{Pellizzoni, Rodolfo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2024.6},
  URN =		{urn:nbn:de:0030-drops-203094},
  doi =		{10.4230/LIPIcs.ECRTS.2024.6},
  annote =	{Keywords: Cause-Effect Chains, Logical Execution Time, End-to-End Latency, Design Optimization, Task Priorities, Data Age, Reaction Time}
}

Document

DOI: 10.4230/DagMan.10.1.1

Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)

Authors: James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter

Published in: Dagstuhl Manifestos, Volume 10, Issue 1 (2024)

Abstract

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022,sser a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.

Cite as

James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, and Frank Wolter. Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282). In Dagstuhl Manifestos, Volume 10, Issue 1, pp. 1-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{delgrande_et_al:DagMan.10.1.1,
  author =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  title =	{{Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Perspectives Workshop 22282)}},
  pages =	{1--61},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2024},
  volume =	{10},
  number =	{1},
  editor =	{Delgrande, James P. and Glimm, Birte and Meyer, Thomas and Truszczynski, Miroslaw and Wolter, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.10.1.1},
  URN =		{urn:nbn:de:0030-drops-201403},
  doi =		{10.4230/DagMan.10.1.1},
  annote =	{Keywords: Knowledge representation and reasoning, Applications of logics, Declarative representations, Formal logic}
}

Document

DOI: 10.4230/LIPIcs.CP.2021.14

Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem

Authors: Valentin Antuori, Emmanuel Hebrard, Marie-José Huguet, Siham Essodaigui, and Alain Nguyen

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract

Many state-of-the-art methods for combinatorial games rely on Monte Carlo Tree Search (MCTS) method, coupled with machine learning techniques, and these techniques have also recently been applied to combinatorial optimization. In this paper, we propose an efficient approach to a Travelling Salesman Problem with time windows and capacity constraints from the automotive industry. This approach combines the principles of MCTS to balance exploration and exploitation of the search space and a backtracking method to explore promising branches, and to collect relevant information on visited subtrees. This is done simply by replacing the Monte-Carlo rollouts by budget-limited runs of a DFS method. Moreover, the evaluation of the promise of a node in the Monte-Carlo search tree is key, and is a major difference with the case of games. For that purpose, we propose to evaluate a node using the marginal increase of a lower bound of the objective function, weighted with an exponential decay on the depth, in previous simulations. Finally, since the number of Monte-Carlo rollouts and hence the confidence on the evaluation is higher towards the root of the search tree, we propose to adjust the balance exploration/exploitation to the length of the branch. Our experiments show that this method clearly outperforms the best known approaches for this problem.

Cite as

Valentin Antuori, Emmanuel Hebrard, Marie-José Huguet, Siham Essodaigui, and Alain Nguyen. Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 14:1-14:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{antuori_et_al:LIPIcs.CP.2021.14,
  author =	{Antuori, Valentin and Hebrard, Emmanuel and Huguet, Marie-Jos\'{e} and Essodaigui, Siham and Nguyen, Alain},
  title =	{{Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{14:1--14:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.14},
  URN =		{urn:nbn:de:0030-drops-153052},
  doi =		{10.4230/LIPIcs.CP.2021.14},
  annote =	{Keywords: Monte-Carlo Tree Search, Travelling Salesman Problem, Scheduling}
}

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1 Antuori, Valentin
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