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@ -13,10 +13,25 @@ design and implement artificial intelligent agents using computational logic. I'
- Explainability through verifiable chains of inference
- Defeasible reasoning under uncertainty
- Reasoning about agents and their cognitive states
- Automated planning under ethical constraints
[Notes on Automated Theorem Proving](atp)
## Integrated Planning and Reinforcement Learning
Working with [Junkyu Lee](https://researcher.ibm.com/researcher/view.php?person=ibm-Junkyu.Lee),
[Michael Katz](https://researcher.watson.ibm.com/researcher/view.php?person=ibm-Michael.Katz1),
and [Shirin Sohrabi](https://researcher.watson.ibm.com/researcher/view.php?person=us-ssohrab)
on extending and relaxing assumptions within their existing
[Planning Annotated Reinforcement Learning Framework](https://prl-theworkshop.github.io/prl2021/papers/PRL2021_paper_36.pdf) developed at IBM Research.
In this framework, automated planning is used on a higher-level version of the overall
problem with a surjective function mapping RL states to AP states. The agent is
based on the options framework in Hiearchical Reinforcement Learning where options
are defined as the grounded actions in the planning model.
More to come...
## Symbolic Methods for Cryptography
Working with [Dr. Andrew Marshall](https://www.marshallandrew.net/) and others in applying term reasoning within computational logic
towards cryptography. This collaboration was previously funded under an ONR grant. We are interested in applying techniques such