Updated publications

content/paper/2406.01.md

@@ -0,0 +1,21 @@
+---
+draft: false
+title: "Guiding Hiearchical Reinforcement Learning in Partially Observable Environments with AI Planning"
+authors: [
+    "Brandon Rozek",
+    "Junkyu Lee",
+    "Harsha Kokel",
+    "Michael Katz",
+    "Shirin Sohrabi"
+]
+date: 2024-06-02
+publish_date: "2024/06/02"
+conference: "International Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)"
+
+isbn: ""
+doi: ""
+language: "English"
+
+pdf_url: "https://prl-theworkshop.github.io/prl2024-icaps/papers/12.pdf"
+abstract: "Partially observable Markov decision processes challenge reinforcement learning agents since observations provide an limited view of the environment. This often requires an agent to explore collecting observations to form the necessary state information to complete the task. Even assuming knowledge is monotonic, it is difficult to know when to stop exploration. We integrate AI planning within hierarchical reinforcement learning to aide in the exploration of partially observable environments. Given a set of unknown state variables, their potential valuations, along with which abstract operators may discover them, we create an abstract fully-observable non-deterministic planning problem which captures the agent’s abstract belief state. This decomposes the POMDP into a tree of semi-POMDPs based on sensing outcomes. We evaluate our agent’s performance on a MiniGrid domain and show how guided exploration may improve agent performance."
+---

content/paper/2406.02.md

@@ -0,0 +1,18 @@
+---
+draft: false
+title: "Initial Steps in Planning under Qualitative Uncertainty"
+authors: [
+    "Brandon Rozek",
+    "Selmer Bringsjord"
+]
+date: 2024-06-02
+publish_date: "2024/06/02"
+conference: "International Workshop on Human-Aware and Explainable Planning (HAXP)"
+
+isbn: ""
+doi: ""
+language: "English"
+
+pdf_url: "https://openreview.net/pdf?id=soH9BIp0pL"
+abstract: "Techniques in automated planning under uncertainty capture whether an agent believes that a ground atomic formula is true, false, or uncertain; and, in some cases, the exact probability that it’s true at a given state. Sometimes, however, an agent does not have access to exact probabilistic information, but is instead able to judge the uncertainty qualitatively. We take initial but substantial steps towards characterizing a variant of conformant planning based on qualitative uncertainty. Our framework, QU-STRIPS, introduces levels of belief about ground atomic formulae which stratify uncertainty ranging on the negative side from certainly not, to agnostic, and then on the positive side up to certainly. In order to efficiently find plans, we present a sound compilation into classical STRIPS. We provide preliminary results on a new escape domain and show that state-of-the-art planners can effectively find plans that achieve the goal at a high positive belief level, while considering the trade-off between the strength of a plan and its cost."
+---

content/publications.md

@@ -8,6 +8,11 @@ aliases:
 
 ## Publications
 
+A Modal Logic of Optimality (Student Abstract)
+- Authors: James T. Oswald, *Brandon Rozek*, Thomas M. Ferguson, Selmer Bringsjord
+- Venue: AAAI Conference on Artificial Intelligence, 2025.
+- Paper to appear
+
 [Modeling C0 Family Logics for Artificial Intelligence: Doxastic-Temporal Logics for Reasoning About Goals](/paper/2405.02)
 - Authors: James T. Oswald, *Brandon Rozek*, and Thomas M. Ferguson
 - Venue: Künstliche Intelligenz (KI), 2024
@@ -53,15 +58,15 @@ Verification of Automatically Synthesized Cryptosystems ](/paper/2109.01/)
 
 ## Papers
 
-Initial Steps in Planning under Qualitative Uncertainty
+[Initial Steps in Planning under Qualitative Uncertainty](/paper/2406.02)
 - Authors: *Brandon Rozek* and Selmer Bringsjord
 - Venue: International Workshop on Human-Aware and Explainable Planning (HAXP), 2024
-- Paper to appear mid 2024
+- [Paper](https://openreview.net/pdf?id=soH9BIp0pL)
 
-Guiding Hiearchical Reinforcement Learning in Partially Observable Environments with AI Planning
+[Guiding Hiearchical Reinforcement Learning in Partially Observable Environments with AI Planning](/paper/2406.01)
 - Authors: *Brandon Rozek*, Junkyu Lee, Harsha Kokel, Michael Katz, Shirin Sohrabi
 - Venue: International Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), 2024
-- Paper to appear mid 2024
+- [Paper](https://prl-theworkshop.github.io/prl2024-icaps/papers/12.pdf)
  
 [CryptoSolve: Towards a Tool for the Symbolic Analysis of Cryptographic Algorithms](/paper/2203.01/)
 - Authors: D Chichester, W Du, R Kauffman, H Lin, C Lynch, A M. Marshall, C Meadows, P Narendran, V Ravishankar, L Rovira, *B Rozek*