diff --git a/content/program/workshops/hplan.md b/content/program/workshops/hplan.md
index 25a5b35..aeb2779 100644
--- a/content/program/workshops/hplan.md
+++ b/content/program/workshops/hplan.md
@@ -186,14 +186,34 @@ Our workshop is scheduled for Monday, June 3, 2024. It takes place in room KC 30
 - 10:50 -- 12:00        poster session 2 (6 posters)
 - 12:00 -- 13:30        *BREAK*
 - 13:30 -- 14:30        Invited talk (including discussion)
-- 14:30 -- 15:00        tba
+- 14:30 -- 15:00        A discussion on an novel HTN formalism in the presence of empty decomposition methods, by Roman Bartak (~10 min talk + open discussion)
+
+Poster session 1:
+
+- An ILP Heuristic for Total-Order HTN Planning
+- On Semantics of Hierarchical Planning Domain Models with Decomposition Constraints and Empty Methods
+- Laying the Foundations for Solving FOND HTN problems: Grounding, Search, Heuristics (and Benchmark Problems)
+- Properties under Progression
+- Redundant Decompositions in PO HTN Domains: Goto Considered Harmful
+- Towards Search Node-Specific Special-Case Heuristics for HTN Planning – An Empirical Analysis of Search Space 
+
+Poster Session 2:
+
+- A Comparative Analysis of Plan Repair in HTN Planning
+- Barely Decidable Fragments of HTN Planning
+- Correcting Totally-Ordered Hierarchical Plans by Action Deletion and Action Insertion
+- HTN Model repair by completing incomplete methods (not an HPlan paper)
+- Toward Planning with Hierarchical Decompositions and Time-frames
+- Weighted Randomized Anytime Planning in Pyhop
+
+
 
 <!--
 - &nbsp;&nbsp;8:30 -- &nbsp;&nbsp;8:40 &nbsp; &nbsp; &nbsp;  Welcome
 - &nbsp;&nbsp;8:40 -- &nbsp;&nbsp;9:40 &nbsp; &nbsp; &nbsp;  
 - &nbsp;&nbsp;9:40 -- 10:00 &nbsp; &nbsp; &nbsp;  tba
 - 10:00 -- 10:30 &nbsp; &nbsp; &nbsp;  *BREAK*
-- 10:30 -- 10:50 &nbsp; &nbsp; &nbsp;  poster teaser talks for session 1 (6 talks)
+- 10:30 -- 10:50 &nbsp; &nbsp;  &nbsp;  poster teaser talks for session 1 (6 talks)
 - 10:50 -- 12:00 &nbsp; &nbsp; &nbsp;  poster session 1 (6 posters)
 - 13:30 -- 13:50 &nbsp; &nbsp; &nbsp;  poster teaser talks for session 2 (5 talks)
 - 13:50 -- 15:00 &nbsp; &nbsp; &nbsp;  poster session 2 (5 posters)
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diff --git a/content/program/workshops/hsdip.md b/content/program/workshops/hsdip.md
index 95a0362..2861308 100644
--- a/content/program/workshops/hsdip.md
+++ b/content/program/workshops/hsdip.md
@@ -8,7 +8,7 @@ draft: false
 
 ICAPS'24 Workshop \
 Banff, Canada \
-June 2-3, 2024
+June 2, 2024
 
 ## Aim and Scope of the Workshop
 
@@ -18,39 +18,66 @@ The HSDIP workshop has always been welcoming of multidisciplinary work, for exam
 
 The workshop is meant to be an open and inclusive forum, and we encourage papers that report on work in progress or that do not fit the mold of a typical conference paper. Non-trivial negative results are welcome to the workshop, but we expect the authors to argue for the significance of the presented results.
 
-## List of Accepted Papers
-* **The Bench Transition System and Stochastic Exploration**  
-  Dawson Tomasz, Richard A. Valenzano
-* **Crafting a Pogo Stick in Minecraft with Heuristic Search**  
-  Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern
-* **A Lifted Backward Computation of hAdd**  
-  Pascal Lauer, Alvaro Torralba, Daniel Höller, Jörg Hoffmann
-* **Consolidating LAMA with Best-First Width Search**  
-  Augusto B. Corrêa, Jendrik Seipp
-* **Some Orders Are Important: Partially Preserving Orders in Top-Quality Planning**  
-  Michael Katz, Junkyu Lee, Jungkoo Kang, Shirin Sohrabi
-* **Separate Generation and Evaluation for Parallel Greedy Best-First Search**  
-  Takumi Shimoda, Alex Fukunaga
-* **Cost Partitioning For Multiple Sequence Alignment**  
-  Mika Skjelnes, Daniel Gnad, Jendrik Seipp
-* **Hitting Set Heuristics for Overlapping Landmarks in Satisficing Planning**  
+## Workshop Schedule
+
+HSDIP is scheduled on the June 2.
+
+---
+[08:50 - 10:00] - **Opening & Invited Talk**
+* **Opening Remarks**
+* **Forest Agostinelli -- TBD**
+
+---
+[10:00 - 10:30] - Coffee Break
+
+---
+[10:30 - 12:00] - Session 1
+* **Hitting Set Heuristics for Overlapping Landmarks in Satisficing Planning** ([PDF](../hsdip-papers/paper_14.pdf))  
   Clemens Büchner, Remo Christen, Salomé Eriksson, Thomas Keller
-* **Metareasoning Needs Distributions: Put Up or Shut Up Games**  
-  Sean Mariasin, Andrew I. Coles, Erez Karpas, Wheeler Ruml, Eyal S. Shimony, Shahaf S. Shperberg
-* **Improving the Solvability of the Rubik’s Cube with Domain-Independent Planners Using Standard Planning Representations**  
-  Bharath C. Muppasani, Vishal Pallagani, Biplav Srivastava, Forest Agostinelli
-* **Reasonable Landmark Orderings for Lifted Classical Planning**  
+* **Consolidating LAMA with Best-First Width Search** ([PDF](../hsdip-papers/paper_3.pdf))  
+  Augusto B. Corrêa, Jendrik Seipp
+* **Reasonable Landmark Orderings for Lifted Classical Planning** ([PDF](../hsdip-papers/paper_7.pdf))  
   Marcel Schubert, Julia Wichlacz, Daniel Höller
-* **PDBs Go Numeric: Pattern-Database Heuristics for Simple Numeric Planning**  
+* **A Lifted Backward Computation of hAdd** ([PDF](../hsdip-papers/paper_9.pdf))  
+  Pascal Lauer, Alvaro Torralba, Daniel Höller, Jörg Hoffmann
+* **Gotta Catch 'Em All! Sequence Flaws in CEGAR for Classical Planning** ([PDF](../hsdip-papers/paper_17.pdf))  
+  Martín Pozo, Carlos Linares Lopez, Alvaro Torralba
+
+---
+[12:00 - 13:30] - Lunch
+
+---
+[13:30 - 15:00] - Session 2
+* **PDBs Go Numeric: Pattern-Database Heuristics for Simple Numeric Planning** ([PDF](../hsdip-papers/paper_13.pdf))  
   Alexander Shleyfman, Daniel Gnad, Eyal Weiss, Lee-or Alon
-* **Merge-and-Shrink Heuristics for Stochastic Shortest-Path Problems with Prune Transformations**  
+* **Merge-and-Shrink Heuristics for Stochastic Shortest-Path Problems with Prune Transformations**   ([PDF](../hsdip-papers/paper_16.pdf))  
   Thorsten Klößner, Alvaro Torralba, Marcel Steinmetz, Silvan Sievers
-* **Choosing a Classical Planner with Graph Neural Networks**  
-  Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, Michael Katz
-* **Expected Runtime Comparisons Between Breadth-First Search and Constant-Depth Restarting Random Walks**  
+* **Crafting a Pogo Stick in Minecraft with Heuristic Search** ([PDF](../hsdip-papers/paper_15.pdf))  
+  Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern
+* **Cost Partitioning For Multiple Sequence Alignment** ([PDF](../hsdip-papers/paper_2.pdf))  
+  Mika Skjelnes, Daniel Gnad, Jendrik Seipp
+* **Improving the Solvability of the Rubik’s Cube with Domain-Independent Planners Using Standard Planning Representations** ([PDF](../hsdip-papers/paper_10.pdf))  
+  Bharath C. Muppasani, Vishal Pallagani, Biplav Srivastava, Forest Agostinelli
+* **Some Orders Are Important: Partially Preserving Orders in Top-Quality Planning** ([PDF](../hsdip-papers/paper_4.pdf))  
+  Michael Katz, Junkyu Lee, Jungkoo Kang, Shirin Sohrabi
+
+---
+[15:00 - 15:30] - Coffee Break
+
+---
+[15:30 - 17:00] - Session 3
+* **The Bench Transition System and Stochastic Exploration** ([PDF](../hsdip-papers/paper_11.pdf))  
+  Dawson Tomasz, Richard A. Valenzano
+* **Separate Generation and Evaluation for Parallel Greedy Best-First Search** ([PDF](../hsdip-papers/paper_8.pdf))  
+  Takumi Shimoda, Alex Fukunaga
+* **Expected Runtime Comparisons Between Breadth-First Search and Constant-Depth Restarting Random Walks** ([PDF](../hsdip-papers/paper_12.pdf))  
   Daniel Platnick, Richard A. Valenzano
-* **Gotta Catch 'Em All! Sequence Flaws in CEGAR for Classical Planning**  
-  Martín Pozo, Carlos Linares Lopez, Alvaro Torralba
+* **Choosing a Classical Planner with Graph Neural Networks** ([PDF](../hsdip-papers/paper_6.pdf))  
+  Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, Michael Katz
+* **Metareasoning Needs Distributions: Put Up or Shut Up Games** ([PDF](../hsdip-papers/paper_5.pdf))  
+  Sean Mariasin, Andrew I. Coles, Erez Karpas, Wheeler Ruml, Eyal S. Shimony, Shahaf S. Shperberg
+
+
 
 ## Topics of Interest
 
@@ -68,16 +95,16 @@ Examples of typical topics for submissions to this workshop are:
 ## Important Dates
 
 **Submission deadline:**  
-~~March 28~~ April 4, 2024 (UTC-12 timezone)
+~~April 4, 2024~~ (UTC-12 timezone)
 
 **Author feedback period:**  
-~~April 15-21~~ April 22-24, 2024
+~~April 22-24, 2024~~
 
 **Notification:**  
-April 26 2024
+~~April 26 2024~~
 
 **Workshop:**  
-June 2-3 2024
+June 2 2024
 
 ## Submission Details
 
diff --git a/content/program/workshops/planrob.md b/content/program/workshops/planrob.md
index dbb356a..559141b 100644
--- a/content/program/workshops/planrob.md
+++ b/content/program/workshops/planrob.md
@@ -40,13 +40,11 @@ Topics of interest include (but are not limited to):
 
 ![Tiago Stegun Vaquero](https://icaps24.icaps-conference.org/program/workshops/planrob-invitedSpeaker-Tiago-Vaquero.jpg)
 
-Tiago is a Technical Group Supervisor of the Artificial Intelligence Group, Planning and Execution Section, of the Jet Propulsion Laboratory, California Institute of Technology.
+*Bio:* Tiago is a Technical Group Supervisor of the Artificial Intelligence Group, Planning and Execution Section, of the NASA Jet Propulsion Laboratory, California Institute of Technology. He holds a B.Sc., M.Sc., and Ph.D. in Mechatronics Engineering from the University of Sao Paulo, Brazil. Tiago previously held a MIT research scientist position and a joint Caltech/MIT research position where he worked on Resilient Spacecraft Systems and Risk Sensitive Planning/Scheduling algorithms. At MIT, Tiago also worked on Risk-aware Planners and Executives for Autonomous Underwater Vehicles and Autonomous Cars. Tiago also previously held a research position at the University of Toronto where he worked on Multi-Robot Planning and Coordination. His research interest includes knowledge engineering for autonomous vehicles, and automated planning and scheduling for single and multi-robot missions. 
 
-Tiago holds a bachelor's, master's, and Ph.D. in Mechatronics Engineering from the University of Sao Paulo, Brazil. Tiago previously held a MIT research scientist position and a joint Caltech/MIT research position where he worked on Resilient Spacecraft Systems and Risk Sensitive Planning/Scheduling algorithms with Professor Brian Williams. At MIT, Tiago also worked on Risk Sensitive Planners and Executives for Autonomous Underwater Vehicles and Autonomous Cars. Tiago also held a research position at the University of Toronto where he worked on Multi-Robot Planning and Coordination with Prof. Christopher Beck and Goldie Nejat.
-
-Tiago works on multi-agent coordination methods for multi-rover cave exploration and for surface site characterization.
-
-*Title & Abstract coming soon!*
+*Title:* Autonomous Task and Motion Planning for Ice Worlds Exploration
+ 
+*Abstract:* Icy moons of the gas giant planets, such as Europa, Titan, and Enceladus, are at the forefront of astrobiological interest due to the evidence of subsurface oceans. Enceladus is unique among the icy moons because there are known vent systems that are likely connected to a subsurface ocean, through which the ocean water is ejected to space. An existing study has shown that sending small robots into the vents and directly sampling the ocean water is likely possible. To enable such a mission, JPL is developing a novel, snake-like robot called Exobiology Extant Life Surveyor (EELS) that can navigate Enceladus' extreme surface and descend an erupting vent to capture unaltered liquid samples and potentially reach the ocean. However, navigating to and through Enceladus' environment is challenging: there is substantial uncertainty with respect to its geometry and physical properties of the surface/vents due to the limitations of existing orbital reconnaissance; and communication is limited which requires highly autonomous robots to execute the mission with limited human supervision. In this talk we provide an overview of the EELS project and its development effort to create a risk-aware autonomous robot to navigate these extreme ice terrains/environments. We describe the robot�s architecture and the technical challenges to navigate and sense the icy environment safely and effectively. We focus on the challenges related to surface mobility, and task and motion planning under uncertainty. We provide initial results on mobility and risk-aware task and motion planning from field tests and simulated scenarios.
 
 
 ## List of Accepted Papers
@@ -78,12 +76,12 @@ Ronen Brafman, Or Wertheim and Dan Suissa. **Plug'n Play Task-Level Autonomy for
 
 ## Workshop Schedule
 
-Our workshop is scheduled for Monday, June 3, 2024. It takes place in room KC 105. 
+Our workshop is scheduled for Monday, June 3, 2024. It takes place in room KC 105. We look forward to seeing you there!
 
 The preliminary schedule is as follows:
 
 - 08:30 - 08:40   Welcome
-- 08:40 - 09:40   Invited talk
+- 08:40 - 09:40   Invited talk: Autonomous Task and Motion Planning for Ice Worlds Exploration
 - 09:40 - 10:00   Search Applications for Integrated Planning and Execution of Satellite Observations using Dynamic Targeting
 - 10:00 - 10:30   *Coffee Break*
 - 10:30 - 10:50   PWTO: A Heuristic Approach for Trajectory Optimization in Complex Terrains
diff --git a/content/program/workshops/prl.md b/content/program/workshops/prl.md
index 31fa3b1..08f7422 100644
--- a/content/program/workshops/prl.md
+++ b/content/program/workshops/prl.md
@@ -8,7 +8,7 @@ draft: false
 
 ICAPS'24 Workshop \
 Banff, Alberta, Canada  \
-Date: TBA
+Date: June 2, 2024 
 
 
 ## Aim and Scope of the Workshop
@@ -49,6 +49,90 @@ Please refer to the [PRL workshop website](https://prl-theworkshop.github.io/prl
 
 ICAPS will be **in-person** this year. Authors of accepted workshop papers are expected to physically attend the conference and present in person.
 
+
+## Schedule
+
+| Time (Banff) | Title |
+|:------------:|:-----------|
+|     8:30     | Opening Remarks          |
+|     8:35     | **Keynote Felipe Trevizan:<br>The Next-Generation of Planning Heuristics: GNNs and Beyond**       |
+|     9:35     | **Poster Session I**   |
+|    10:00     | Coffee break |
+|    10:30     | **Talk Session I**    |
+|  | The Case for Developing a Foundation Model for Planning-like Tasks from Scratch. Biplav Srivastava, Vishal Pallagani. |
+|  | Equivalence-Based Abstractions for Learning General Policies. Dominik Drexler, Simon Ståhlberg, Blai Bonet, Hector Geffner. |
+|  | Comparing State-of-the-art Graph Neural Networks and Transformers for General Policy Learning. Nicola J. Müller, Pablo Sanchez Martin, Jörg Hoffmann, Verena Wolf, Timo P. Gros. |
+|  | Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning. Katharina Stein, Daniel Fišer, Jörg Hoffmann, Alexander Koller. |
+|  | Planning with Language Models Through The Lens of Efficiency. Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi. |
+|    12:00     | Lunch    |
+|    13:30     | **Keynote Forest Agostinelli:<br>Deep Reinforcement Learning and Heuristic Search Algorithms** |
+|    14:30 | **Poster Session II**  |
+|    15:00     | Coffee break |
+|    15:30     | **Talk Session II** |
+| | Exploring Simultaneity: Learning Earliest-time Semantics for Automated Planning. Ángel Aso-Mollar, Óscar Sapena, Eva Onaindia.|
+| | ModelDiff: Leveraging Models for Policy Transfer with Value Lower Bounds. Xiaotian Liu, Jihwan Jeong, Ayal Taitler, Michael Gimelfarb, Scott Sanner.|
+| | Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling. Constantin Waubert de Puiseau, Christian Dörpelkus, Jannik Peters, Hasan Tercan, Tobias Meisen.|
+| |Online Planning in MDPs with Stochastic Durative Actions. Tal Berman, Ronen Brafman, Erez Karpas.|
+| | A New View on Planning in Online Reinforcement Learning. Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White.|
+|    17:00     | **Closing Remarks**    |
+|    17:05     | End      |
+
+
+# Program
+
+
+
+## Keynotes 
+
+#### I. Felipe Trevizan: The Next-Generation of Planning Heuristics: GNNs and Beyond
+
+##### Abstract
+Deep learning has been responsible for multiple recent breakthroughs, particularly in image recognition and natural language processing. In this talk, I will focus on a particular deep learning model, Graph Neural Networks (GNNs), and how they have the potential to change heuristic search in automated planning from the heuristics used to search methods. I will introduce novel graph representations designed to optimize the application of GNNs to learning both domain-specific and domain-independent heuristics. Additionally, I will present other targets that can be learnt using GNNs, such as rankings between states, and how they can be used during search. Lastly, based on theoretical insights, we present an alternative approach to GNNs using classical machine learning methods such as SVMs and Gaussian Processes for heuristic learning, offering simplicity and reduced training times.
+
+##### Biography
+Dr. Felipe Trevizan is a Senior Lecturer at the School of Computing, the Australian National University. He previously served as a Senior Research Scientist at NICTA (now Data61/CSIRO). He earned his PhD in Machine Learning from Carnegie Mellon University in 2013. His research interests lie at the intersection of Artificial Intelligence, Operations Research and Machine Learning including automated planning and scheduling, reasoning under uncertainty, heuristic search, and learning for planning. Along with colleagues and students, he is the co-recipient of the 2016 best paper award from the Transport Research Board and the best paper award at the International Conference on Automated Planning and Scheduling (ICAPS) in 2016 and 2017.
+
+
+#### II. Forest Agostinelli: Deep Reinforcement Learning and Heuristic Search Algorithms
+
+##### Abstract
+ Deep reinforcement learning has been shown to be able to learn domain-specific heuristic functions in a largely domain-independent fashion. As a result, novel variations of A* search, such as batch A* search and Q* search, have been proposed to accommodate the deep neural networks that represent these heuristic functions. In this talk, I will describe how approximate value iteration can be used to learn heuristic functions to guide batch A* search, which can exploit parallelization provided by graphics processing units. Next, I will describe how Q-learning can be used to learn heuristic functions represented by deep Q-networks to guide Q* search, which exploits the structure of deep Q-networks to significantly increase speed and reduce memory during search. Finally, I will describe how model-based reinforcement learning and hindsight experience replay can be used to extend these methods to domains with unknown transition functions. I will give several examples of application domains, including the Rubik’s cube, quantum computing, and reaction mechanism pathway prediction. The code for many of these algorithms is publicly available at https://github.com/forestagostinelli/deepxube.
+
+##### Biography
+ Forest Agostinelli is an assistant professor at the University of South Carolina. His research aims to use artificial intelligence to automate the discovery of new knowledge. He looks to apply his research to fields such as puzzle solving, chemical synthesis, robotics, quantum computing, theorem proving, program synthesis, and education. He led the creation of DeepCubeA, an artificial intelligence algorithm capable of solving puzzles such as the Rubik’s cube without human guidance. DeepCubeA has since been applied to problems in quantum computing, chemical reactions, cryptography, and parking lot optimization. He earned his Ph.D. from the University of California, Irvine under the supervision of Professor Pierre Baldi. His homepage is located at https://cse.sc.edu/~foresta/.
+
+## Talks 
+Select accepted papers are given a slot in the program: 15 minutes for content + 3 minutes for questions.
+
+## Poster Sessions
+The program includes two poster sessions in order to have enough time for discussions. All authors are expected to participate in the poster session.
+
+
+## List of Accepted Papers
+
+
+* [poster] **Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning**	*Guy Azran, Mohamad Hosein Danesh, Stefano V Albrecht, Sarah Keren*
+* [talk] **Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling**	*Constantin Waubert de Puiseau, Christian Dörpelkus, Jannik Peters, Hasan Tercan, Tobias Meisen*
+* [talk] **Online Planning in MDPs with Stochastic Durative Actions**	*Tal Berman, Ronen Brafman, Erez Karpas*
+* [talk] **ModelDiff: Leveraging Models for Policy Transfer with Value Lower Bounds**	*Xiaotian Liu, Jihwan Jeong, Ayal Taitler, Michael Gimelfarb, Scott Sanner*
+* [poster] **Solving Minecraft Tasks via Model Learning**	*Yarin Benyamin, Argaman Mordoch, Shahaf S. Shperberg, Roni Stern*
+* [talk] **A New View on Planning in Online Reinforcement Learning**	*Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White*
+* [poster] **Conviction-Based Planning for Sparse Reward Reinforcement Learning Problems**	*Simon Ouellette, Eric Beaudry, Mohamed Bouguessa*
+* [poster] **Q\* Search: Heuristic Search with Deep Q-Networks**	*Forest Agostinelli, Shahaf S. Shperberg, Alexander Shmakov, Stephen Marcus McAleer, Roy Fox, Pierre Baldi*
+* [poster] **Finding Reaction Mechanism Pathways with Deep Reinforcement Learning and Heuristic Search**	*Rojina Panta, Mohammadamin Tavakoli, Christian Geils, Pierre Baldi, Forest Agostinelli*
+* [talk] **Planning with Language Models Through The Lens of Efficiency**	*Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi*
+* [poster] **Guiding Hiearchical Reinforcement Learning in Partially Observable Environments with AI Planning**	*Brandon Rozek, Junkyu Lee, Harsha Kokel, Michael Katz, Shirin Sohrabi*
+* [poster] **Monte Carlo Tree Search for Integrated Planning, Learning, and Execution in Nondeterministic Python**	*Rich Levinson*
+* [talk] **Exploring Simultaneity: Learning Earliest-time Semantics for Automated Planning**	*Ángel Aso-Mollar, Óscar Sapena, Eva Onaindia*
+* [poster] **Numeric Reward Machines**	*Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis, Aneta Vulgarakis Feljan, Jendrik Seipp*
+* [poster] **POSGGym: A Library for Decision-Theoretic Planning and Learning in Partially Observable, Multi-Agent Environments**	*Jonathon Schwartz, Rhys Newbury, Dana Kulic, Hanna Kurniawati*
+* [talk] **The Case for Developing a Foundation Model for Planning-like Tasks from Scratch**	*Biplav Srivastava, Vishal Pallagani*
+* [talk] **Equivalence-Based Abstractions for Learning General Policies**	*Dominik Drexler, Simon Ståhlberg, Blai Bonet, Hector Geffner*
+* [talk] **Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning**	*Katharina Stein, Daniel Fišer, Jörg Hoffmann, Alexander Koller*
+* [talk] **Comparing State-of-the-art Graph Neural Networks and Transformers for General Policy Learning**	*Nicola J. Müller, Pablo Sanchez Martin, Jörg Hoffmann, Verena Wolf, Timo P. Gros*
+* [poster] **Towards Neurosymbolic RL via Inductive Learning of Answer Set Programs**	*Celeste Veronese, Daniele Meli, Alessandro Farinelli*
+* [poster] **SLOPE: Search with Learned Optimal Pruning-based Expansion**	*Davor Bokan, Zlatan Ajanović, Bakir Lacevic*
+
 ## Submission Details
 
 
diff --git a/content/summerschool.md b/content/summerschool.md
index c4feb76..4fd8457 100644
--- a/content/summerschool.md
+++ b/content/summerschool.md
@@ -36,7 +36,7 @@ The following is a preliminary list of speakers (ordered alphabetically) and top
   In this introductory tutorial, we explore the basics of HTN planning, designed to enable anybody without prior knowledge of this field to easily follow current research papers. Starting with a simplistic formalization of the problem -- as used in scientific papers -- we show how this relates to non-hierarchical (STRIPS) planning both in terms of computational complexity and a more fine-grained measure of expressivity, before providing a broad overview of useful extensions of the core formalism. We explain the most commonly known solution technique -- progression search -- but also provide pointers to others such as compilation techniques.
 - [Jeremy Frank](https://www.linkedin.com/in/jeremy-frank-62141bb3), NASA,  
   **The Distributed Spacecraft Autonomy (DSA) Project**  
-  Autonomous decision-making significantly increases mission effectiveness by mitigating the effects of communication constraints, like latency and bandwidth, and mission complexity on multi-spacecraft operations. To advance the state of the art in autonomous Distributed Space Systems (DSS), the Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is developing within five relevant technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human- swarm interaction, and ad hoc network communications. DSA is maturing these technologies - critical for future large autonomous DSS - from concept to launch via simulation studies and orbital deployments. A 100-node heterogenous Processor-in-the-Loop (PiL) testbed aids distributed autonomy capability development and verification of multi-spacecraft missions. The DSA software payload deployed to the D-Orbit SCV-004 spacecraft demonstrates multi-agent reconfigurability and reliability as part of an ESA-sponsored in-orbit technology demonstration. Finally, DSA’s primary flight mission showcases collaborative resource allocation for multipoint science data collection with four small spacecraft as a payload on NASA’s Starling 1.0 satellites.
+  Autonomous decision-making significantly increases mission effectiveness by mitigating the effects of communication constraints, like latency and bandwidth, and mission complexity on multi-spacecraft operations. To advance the state of the art in autonomous Distributed Space Systems (DSS), the Distributed Spacecraft Autonomy (DSA) team at NASA is maturing technology in the following technical areas: distributed resource and task management, reactive operations, human-swarm interaction, and ad hoc network communications. DSA is divided into two parts. DSA’s primary flight mission showcases collaborative resource allocation for multipoint science data collection with four small spacecraft as a payload on NASA’s Starling 1.0 satellites. A scalability demonstration of Lunar Position, Navigation and Timing services (PNT) services was performed in simulation, and on a 100-node heterogenous Processor-in-the-Loop (PiL) testbed. In this talk, I will describe the overarching goals of DSA, discuss progress in both areas, then sum up with lessons learned.
 - [Sarah Keren](https://sarahk.cs.technion.ac.il), Technion,  
   **Introduction to integrated task and motion planning**  
   Integrated Task and Motion Planning (ITMP) is the problem of planning for robots that operate in complex environments and need to combine low-level motion planning in a continuous space with a high-level search in a discrete space for a sequence of actions that is predicted to accomplish their assigned task. With the increasing complexity of the tasks autonomous agents and robots are required to achieve in applications such as packing, personal assistance, and cooking, this dichotomous view becomes inefficient. Instead, there is a need for new ways for integrating task-level considerations when planning the robot’s movement, and for propagating motion-planning considerations into the task-level process. This is even more important in settings in which agents are required to share their environment and collaborate with other autonomous agents. I will introduce the rich and active field of integrated task and motion planning, present the key technical challenges that TMP entails, and highlight some of the state-of-the-art approaches towards addressing them. I will end my talk by overviewing some of the major open problems in the field and potential ways of addressing them. 
@@ -167,7 +167,7 @@ Also, in the schedule below, all labs are organized and executed by Sarah Keren
         <td style="background-color: #ff0">Lab: continue...<br>(16:00-17:30)</td>
         <td style="background-color: #add8e6">ICKEPS<br>(16:00-17:30)</td>
         <td style="background-color: #ff0">Lab: continue...<br>(16:00-17:30)</td>
-        <td>Summer school wrap-up<br>(16:00-17:30)</td>
+        <td>Summer school wrap-up and Quiz<br>(16:00-17:30)</td>
     </tr>
     <tr>
         <td><b>Evening (time)</b></td>
@@ -229,6 +229,11 @@ The organizing team, sorted alphabetically by last name, consists of:
 - Sarah Keren from Technion, Israel -- sarahk at technion.ac.il, [webpage](https://sarahk.cs.technion.ac.il)
 - Jane Jean Kiam from University of the Bundeswehr Munich, Germany -- jane.kiam at unibw.de [webpage](https://www.unibw.de/home-en/appointment-of-professors/prof-jane-jean-kiam)
 
+In putting together the program, Sarah has been supported by our two teaching assistants:
+
+- Guy Azran from Technion, Israel
+- Yuval Goshen from Technion, Israel
+
 
 ## Sponsors