World's Best Scientists 2026 revealed!
Héctor Muñoz-Avila

Héctor Muñoz-Avila

D-Index & Metrics

Computer Science

D-Index
38
Citations
4640
World Ranking
10403
National Ranking
4342

Overview

Héctor Muñoz-Avila is affiliated with Lehigh University in the United States. Their research primarily spans the fields of computer science and engineering, with a focus on artificial intelligence and its applications. Their work notably covers areas such as AI-based problem solving and planning, multi-agent systems and negotiation, and logic, reasoning, and knowledge.

The scientist has contributed to multiple publications, with frequent appearances in venues including:

  • arXiv (Cornell University)
  • Journal of Experimental & Theoretical Artificial Intelligence
  • Proceedings of the International Florida Artificial Intelligence Research Society Conference

Muñoz-Avila's recent papers demonstrate a range of topics within AI and planning, including:

  • "Expectations for agents with goal-driven autonomy," 2020, Journal of Experimental & Theoretical Artificial Intelligence
  • "Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis," 2020, arXiv (Cornell University)
  • "Distributed Map Classification using Local Observations," 2020, arXiv (Cornell University)
  • "HTN Learning via Transfer Learning of Domain Landmarks," 2021, Proceedings of the International Florida Artificial Intelligence Research Society Conference
  • "Task Modifiers for HTN Planning and Acting," 2022, arXiv (Cornell University)

Frequent co-authors collaborating with Muñoz-Avila include:

  • Weihang Yuan
  • Dustin Dannenhauer
  • Michael T. Cox
  • Soheila Sadeghi Eshkevari
  • Soheil Sadeghi Eshkevari

Their research topics intersect with subfields like artificial intelligence, computer vision and pattern recognition, electrical and electronic engineering, industrial and manufacturing engineering, and automotive engineering. Specific themes extend to electric vehicles and infrastructure, vehicle routing optimization methods, transportation and mobility innovations, and reinforcement learning in robotics.

This combination of research interests highlights a multidisciplinary approach to AI methodologies applied to complex systems, autonomous agents, and domain-specific learning processes across engineering and computing disciplines.

Best Publications

  • SHOP: Simple Hierarchical Ordered Planner

    Dana S. Nau;Yue Cao;Amnon Lotem;Hector Muñoz-Avila

  • Conversational Case-Based Reasoning

    David W. Aha;Leonard A. Breslow;Héctor Muñoz-Avila

  • Applications of SHOP and SHOP2

    D. Nau;T.-C. Au;O. Ilghami;U. Kuter

  • Total-order planning with partially ordered subtasks

    Dana Nau;Héctor Muñoz-Avila;Yue Cao;Amnon Lotem

  • HTN-MAKER: learning HTNs with minimal additional knowledge engineering required

    Chad Hogg;Héctor Muñoz-Avila;Ugur Kuter

  • Hierarchical plan representations for encoding strategic game AI

    Hai Hoang;Stephen Lee-Urban;Héctor Muñoz-Avila

  • HICAP: an interactive case-based planning architecture and its application to noncombatant evacuation operations

    Héctor Muñoz-Avila;David W. Aha;Len Breslow;Dana Nau

  • Automatically Generating Game Tactics through Evolutionary Learning

    Marc J. V. Ponsen;Hector Muñoz-Avila;Pieter Spronck;David W. Aha

  • Case-based reasoning integrations

    Cynthia Marling;Mohammed Sqalli;Edwina Rissland;Hector Muñoz-Avila

  • Multimodal Entity Coreference for Cervical Dysplasia Diagnosis

    Dezhao Song;Edward Kim;Xiaolei Huang;Joseph Patruno

  • Case-Based Plan Adaptation: An Analysis and Review

    H. Munoz-Avila;M.T. Cox

  • Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning

    Bryan Auslander;Stephen Lee-Urban;Chad Hogg;Héctor Muñoz-Avila

  • SiN: integrating case-based reasoning with task decomposition

    Héctor Muñoz-Avila;David W. Aha;Dana S. Nau;Rosina Weber

  • RETALIATE: learning winning policies in first-person shooter games

    Megan Smith;Stephen Lee-Urban;Héctor Muñoz-Avila

  • Case-based planning

    Michael T. Cox;Héctor Muñoz-avila;Ralph Bergmann

  • CaMeL: learning method preconditions for HTN planning

    Okhtay Ilghami;Dana S. Nau;Héctor Muñoz-Avila;David W. Aha

  • Planning for manufacturing workpieces by storing, indexing and replaying planning decisions

    Héctor Muñoz-Avila;Frank Weberskirch

  • Learning hierarchical task network domains from partially observed plan traces

    Hankz Hankui Zhuo;Héctor Muñoz-Avila;Qiang Yang

  • LCW-Based Agent Planning for the Semantic Web

    Jeff Heflin;Hector Muñoz-Avila

  • IMPACTing SHOP: Putting an AI Planner Into a Multi-Agent Environment

    Jürgen Dix;Héctor Muñoz-Avila;Dana S. Nau;Lingling Zhang

  • Conversational case-based reasoning

    David W. Aha

Frequent Co-Authors

David W. Aha
David W. Aha United States Naval Research Laboratory
Dana S. Nau
Dana S. Nau University of Maryland, College Park
Ralph Bergmann
Ralph Bergmann University of Trier
Manuela Veloso
Manuela Veloso Carnegie Mellon University
Qiang Yang
Qiang Yang Hong Kong University of Science and Technology
Francesco Ricci
Francesco Ricci Free University of Bozen-Bolzano
Eugene C. Freuder
Eugene C. Freuder University College Cork
Martin V. Butz
Martin V. Butz University of Tübingen
Sameer Antani
Sameer Antani National Institutes of Health
Peter I. Cowling
Peter I. Cowling Queen Mary University of London

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