Hi, I'm Hector Munoz-Avila.
Dr. Munoz-Avila is a Program Director at NSF's Information and Intelligent Systems (IIS) Division, where he is cluster lead for the Information Integration and Informatics (III) program. Dr. Munoz-Avila is also an affiliated researcher with American University. Prior to joining NSF, Dr. Muñoz-Avila was a (tenured) professor of Computer Science and Engineering and of Cognitive Science at Lehigh University. He was co-director of Lehigh's Institute for Data, Intelligent Systems, and Computation (I-DISC). Dr. Muñoz-Avila is recipient of a National Science Foundation (NSF) CAREER and held a Lehigh Class of 1961 Professorship. He has been chair for various international scientific meetings including the Sixth International Conference on Case-Based Reasoning (ICCBR-05) and the twenty-fifth Innovative Applications of AI Conference (IAAI-13). He was funded by the Office of Naval Research (ONR), the National Science Foundation (NSF), the Defense Advanced Research Projects Agency (DARPA), the Naval Research Laboratory (NRL) and the Air Force Research Laboratory (AFRL).
Dr. Munoz-Avila holds a PhD (Dr. rer. nat.) in computer science from the Universität Kaiserslautern (now Universität Kaiserslautern-Landau, Germany), an MS in computer science, a BS in Mathematics and a BS in computer Science from the Universidad de los Andes (Colombia).
Research
I have a broad range of interests in different areas including capturing and reusing episodic knowledge (i.e., case-based reasoning), generalizing and abstracting episodic knowledge into general domain knowledge (i.e., learning), using this knowledge to solve new problems (i.e., planning), adapting to changes in the environment (i.e., agents) and introspectively reasoning about its own actions (i.e., cognitive systems).
The following are topics of current interest:
Goal Reasoning and Safety. In part motivated by topics such as agency safety, I am interested in goal reasoning, a form of agency where the agents formulate their own goals. For example, agents formulate goals as a reaction to conditions in the environment where the agent is operating. Such as an agent deviating of course as a result of a mechanical failure. In such cases the agent can self-formulate a new goal to make a correction.
Hierarchical Task Network (HTN) planning and execution. HTN planning is a problem-solving paradigm for generating plans that achieve some input tasks. Tasks are activities to be performed. They can be concrete such as achieving conditions in the world (i.e., goals) or more abstract conditions such as ”protect the house”. HTN planning generates a plan by using a recursive procedure in which tasks of higher level of abstraction are decomposed into simpler, more concrete tasks. The task decomposition process terminates when a sequence of actions is generated solving the input tasks. One recurrent interest is in monitoring the execution of HTN plans in complex environments.
Automated learning of HTNs. I am interested in HTN learning encompassing learning of methods (knowledge artifacts indicating when to decompose a task into subtasks), operators (knowledge artifacts indicating how the world changes) and the tasks themselves. I am interested HTN learning on a variety of contexts: deterministic and nondeterministic domains, domains with symbolic and numerical conditions, and when the tasks are goals (hierarchical goal networks).
For more details, please see my Google scholar page.
For NSF-related inquiries please contact: hmunoz@nsf.gov; For all other inquiries please contact: hhhhmmmm02@gmail.com