diff --git a/content/research/_index.md b/content/research/_index.md index bd6d35a..bbdef31 100644 --- a/content/research/_index.md +++ b/content/research/_index.md @@ -7,25 +7,36 @@ Description: A list of my research Projects **Broad Research Interests:** Automated Reasoning, Automated Planning, Artificial Intelligence, Formal Methods +Currently, I'm a Computer Science PhD Candidate at Rensselaer Polytechnic Institute. I enjoy using logic-based techniques and designing algorithms to solve problems. + +Jump to: +- [Planning under uncertainty](#planning-under-uncertainty) +- [Logic](#logic) +- [Symbolic Methods for Cryptography](#symbolic-methods-for-cryptography) + ## Planning under Uncertainty -During my PhD I have been primarily focused on investigating planning and sequential decision -making under uncertainty: -- I created a new framework which allows agents to make plans under *qualitative uncertainty*. -This helps in settings where the user doesn't have exact probabilities that various -facts holds, but can instead bucket them into different likelihood values. -This work is supervised under [Selmer Bringsjord](https://homepages.rpi.edu/~brings/). -- Additionally with Selmer Bringsjord in the [RAIR Lab](https://rair.cogsci.rpi.edu/), I have looked at planning through automated reasoning. -I further developed [Spectra](https://github.com/rairlab/spectra) and the underlying -planning with formulas framework to show classes of uncertainty problems that -are easy to encode. Additionally, I wrote a QA algorithm for ShadowProver to integrate to Spectra -for planning under epistemic uncertatinty. +My dissertation topic is on automatically finding and recognizing plans +when agents are uncertain about the environment but can compare the +uncertainty between events *qualitatively*. For example, it is totally +expected when we stack a block that it stays on the top. However, there is a +smaller likelihood that the block falls off. +How can we best make use of this qualitative uncertainty? +- Agents when operating under uncertainty will seek plans which maximize the likelihood of their goals. +I designed an algorithm for recognizing these plans under qualitative possibility theory. This work is supervised under [Selmer Bringsjord](https://kryten.mm.rpi.edu/selmerbringsjord.html) (Paper to be released soon) +- Additionally with Selmer Bringsjord in the [RAIR Lab](https://rair.cogsci.rpi.edu/), I created a framework that captures +situations where agents are able to bucket the likelihood of facts within their environment. I then provide an effective +techinque for using classical planners to find plans which maximize the agent's likelihood of success. ([Paper](/paper/2406.02)) +- In the RAIR Lab, I also further developed [Spectra](https://github.com/rairlab/spectra) -- +an automated planner built on automated theorem proving. I showed how a class of problems +under uncertainty can be easily encoded and wrote a question-answer algorithm +for ShadowProver so that Spectra can find plans under epistemic uncertainty. ([Paper](/paper/2405.01/)) - 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), [Harsha Kokel](https://harshakokel.com/), and [Shirin Sohrabi](https://researcher.watson.ibm.com/researcher/view.php?person=us-ssohrab) at IBM I developed an algorithm -for guiding hiearchical reinforcement agents under partial observability when domain knowledge -can be encoded for characterizing discovery of unknown predicates. - +for guiding hierarchical reinforcement agents under partial observability. Specifically, +I focused on situations where the agent knows what they don't know and compiled that knowledge +so that a fully-observable non-deterministic planner can decompose the overall problem. ([Paper](/paper/2406.01)) ## Logic @@ -46,7 +57,7 @@ Related Notes: - [Automated Theorem Proving](atp/) - [Term Reasoning](termreasoning/) - + ## Symbolic Methods for Cryptography Worked with [Andrew Marshall](https://www.marshallandrew.net/) and others in applying term reasoning within computational logic @@ -73,6 +84,11 @@ Collaborators: Group Website: [https://cryptosolvers.github.io](https://cryptosolvers.github.io) +--- + +**Note:** From this point on, the projects listed happened over 5 years ago. + +--- ## Reinforcement Learning