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Documents authored by McIlraith, Sheila A.

Found 2 Possible Name Variants:

McIlraith, Sheila A.

Document
Invited Talk
DOI: 10.4230/LIPIcs.TIME.2017.3
Plan and Program Synthesis: A New Look at Some Old Problems (Invited Talk)

Authors: Sheila A. McIlraith

Published in: LIPIcs, Volume 90, 24th International Symposium on Temporal Representation and Reasoning (TIME 2017)

Abstract
The proliferation of programmable devices, personal assistants, and autonomous systems presents fundamental challenges to the deployment of safe, predictable systems that can work together, interact seamlessly with humans, and that are taskable and instructable by people who may not know how to program. In this talk, we will revisit the classical problem of program synthesis through the lens of AI automated planning. We will present recent advances in AI automated planning principles and computational methods that support the synthesis of plans with goals and preferences specified in Linear Temporal Logic and Regular Expressions. Moving from automated planning in deterministic domains to planning in nondeterministic domains, we will explore the pathway to synthesizing programs that are taskable and instructable by exploiting state-of-the-art AI planning technology.
Cite as

Sheila A. McIlraith. Plan and Program Synthesis: A New Look at Some Old Problems (Invited Talk). In 24th International Symposium on Temporal Representation and Reasoning (TIME 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 90, p. 3:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{mcilraith:LIPIcs.TIME.2017.3,
  author =	{McIlraith, Sheila A.},
  title =	{{Plan and Program Synthesis: A New Look at Some Old Problems}},
  booktitle =	{24th International Symposium on Temporal Representation and Reasoning (TIME 2017)},
  pages =	{3:1--3:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-052-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{90},
  editor =	{Schewe, Sven and Schneider, Thomas and Wijsen, Jef},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2017.3},
  URN =		{urn:nbn:de:0030-drops-79343},
  doi =		{10.4230/LIPIcs.TIME.2017.3},
  annote =	{Keywords: planning, program synthesis, linear temporal logic, regular expressions}
}

McIlraith, Sheila

Document
DOI: 10.4230/LIPIcs.CP.2023.33
SAT-Based Learning of Compact Binary Decision Diagrams for Classification

Authors: Pouya Shati, Eldan Cohen, and Sheila McIlraith

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
Decision trees are a popular classification model in machine learning due to their interpretability and performance. However, the number of splits in decision trees grow exponentially with their depth which can incur a higher computational cost, increase data fragmentation, hinder interpretability, and restrict their applicability to memory-constrained hardware. In constrast, binary decision diagrams (BDD) utilize the same split across each level, leading to a linear number of splits in total. Recent work has considered optimal binary decision diagrams (BDD) as compact and accurate classification models, but has only focused on binary datasets and has not explicitly optimized the compactness of the resulting diagrams. In this work, we present a SAT-based encoding for a multi-terminal variant of BDDs (MTBDDs) that incorporates a state-of-the-art direct encoding of numerical features. We then develop and evaluate different approaches to explicitly optimize the compactness of the diagrams. In one family of approaches, we learn a tree BDD first and model the size of the diagram the tree will be reduced to as a secondary objective, in a one-stage or two-stage optimization scheme. Alternatively, we directly learn diagrams that support multi-dimensional splits for improved expressiveness. Our experiments show that direct encoding of numerical features leads to better performance. Furthermore, we show that exact optimization of size leads to more compact solutions while maintaining higher accuracy. Finally, our experiments show that multi-dimensional splits are a viable approach to achieving higher expressiveness with a lower computational cost.
Cite as

Pouya Shati, Eldan Cohen, and Sheila McIlraith. SAT-Based Learning of Compact Binary Decision Diagrams for Classification. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 33:1-33:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{shati_et_al:LIPIcs.CP.2023.33,
  author =	{Shati, Pouya and Cohen, Eldan and McIlraith, Sheila},
  title =	{{SAT-Based Learning of Compact Binary Decision Diagrams for Classification}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{33:1--33:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.33},
  URN =		{urn:nbn:de:0030-drops-190700},
  doi =		{10.4230/LIPIcs.CP.2023.33},
  annote =	{Keywords: Binary Decision Diagram, Classification, Compactness, Numeric Data, MaxSAT}
}
Document
DOI: 10.4230/DagRep.12.9.200
Cognitive Robotics (Dagstuhl Seminar 22391)

Authors: Fredrik Heintz, Gerhard Lakemeyer, and Sheila McIlraith

Published in: Dagstuhl Reports, Volume 12, Issue 9 (2023)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22391 on the topic of "Cognitive Robotics". Cognitive Robotics is concerned with endowing robots or software agents with higher level cognitive functions that involve reasoning, for example, about goals, perception, actions, the mental states of other agents, and collaborative task execution. The seminar is the latest event in a series of events on this topic that were initiated in 1998. With its roots in knowledge representation and reasoning, the program for this seminar was influenced by transformative advances in machine learning and deep learning, by recent advances in human-robot interactions, and by issues that arise in the development of trustworthy cognitive robotic systems. Reflective of this, the seminar featured sessions devoted to the following four themes: cognitive robotics and KR, verification of cognitive robots, human-robot interaction and robot ethics, and planning and learning. Each theme consisted of plenary talks, plenary discussions and working groups resulting in a research road map for the coming years. There was also a poster session where new or published results could be presented by the participants. The seminar was very successful and well received by the participants thanks to the excellent environment for exchanging ideas provided by Schloss Dagstuhl.
Cite as

Fredrik Heintz, Gerhard Lakemeyer, and Sheila McIlraith. Cognitive Robotics (Dagstuhl Seminar 22391). In Dagstuhl Reports, Volume 12, Issue 9, pp. 200-219, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{heintz_et_al:DagRep.12.9.200,
  author =	{Heintz, Fredrik and Lakemeyer, Gerhard and McIlraith, Sheila},
  title =	{{Cognitive Robotics (Dagstuhl Seminar 22391)}},
  pages =	{200--219},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{9},
  editor =	{Heintz, Fredrik and Lakemeyer, Gerhard and McIlraith, Sheila},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.200},
  URN =		{urn:nbn:de:0030-drops-178132},
  doi =		{10.4230/DagRep.12.9.200},
  annote =	{Keywords: Artificial Intelligence, Knowledge Representation and Reasoning, Cognitive Robotics, Verification, Human-robot Interaction, Robot Ethics, Machine Learning, Planning}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.50
SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features

Authors: Pouya Shati, Eldan Cohen, and Sheila McIlraith

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

Abstract
Decision trees are a popular classification model in machine learning due to their interpretability and performance. Traditionally, decision-tree classifiers are constructed using greedy heuristic algorithms, however these algorithms do not provide guarantees on the quality of the resultant trees. Instead, a recent line of work has studied the use of exact optimization approaches for constructing optimal decision trees. Most of the recent approaches that employ exact optimization are designed for datasets with binary features. While numeric and categorical features can be transformed to binary features, this transformation can introduce a large number of binary features and may not be efficient in practice. In this work, we present a novel SAT-based encoding for decision trees that supports non-binary features and demonstrate how it can be used to solve two well-studied variants of the optimal decision tree problem. We perform an extensive empirical analysis that shows our approach obtains superior performance and is often an order of magnitude faster than the current state-of-the-art exact techniques on non-binary datasets.
Cite as

Pouya Shati, Eldan Cohen, and Sheila McIlraith. SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 50:1-50:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{shati_et_al:LIPIcs.CP.2021.50,
  author =	{Shati, Pouya and Cohen, Eldan and McIlraith, Sheila},
  title =	{{SAT-Based Approach for Learning Optimal Decision Trees with Non-Binary Features}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{50:1--50:16},
  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.2021.50},
  URN =		{urn:nbn:de:0030-drops-153416},
  doi =		{10.4230/LIPIcs.CP.2021.50},
  annote =	{Keywords: Decision Tree, Classification, Numeric Data, Categorical Data, SAT, MaxSAT}
}
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