Awesome Probabilistic Planning

A curated list of resources for people starting to research in Probabilistic Planning in Artificial Intelligence.

Table of Contents

• Books
• Conferences and Competitions
• Courses
• Papers
• Research Groups
• Software
• Tutorials

Books

• Automated Planning and Acting
• Automated Planning: Theory & Practice
• A Concise Introduction to Models and Methods for Automated Planning
• Planning with Markov Decision Processes: An AI Perspective
• Heuristic Search: Theory and Applications

Courses

• AI Planning MOOC, School of Informatics, University of Edinburgh
• CMSC 722, AI Planning

Conferences and Competitions

• International Conference on Automated Planning and Scheduling (ICAPS)
• International Planning Competition (IPC)

Papers

• Boutilier, Craig, Thomas Dean, and Steve Hanks. Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research 11, no. 1 (1999): 94.

• Dean, Thomas, and Keiji Kanazawa. A model for reasoning about persistence and causation. Computational intelligence 5, no. 2 (1989): 142-150.

• Howard, Ronald A. Comments on the origin and application of Markov decision processes. Operations Research 50, no. 1 (2002): 100-102.

• Browne, Cameron B., Edward Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games 4, no. 1 (2012): 1-43.

• Guestrin, Carlos, Daphne Koller, Ronald Parr, and Shobha Venkataraman. Efficient solution algorithms for factored MDPs. Journal of Artificial Intelligence Research 19 (2003): 399-468.

• Trevizan, Felipe W., Fabio Gagliardi Cozman, and Leliane Nunes de Barros. Planning under Risk and Knightian Uncertainty. In IJCAI, vol. 2007, pp. 2023-2028. 2007.

• Edelkamp, Stefan. Heuristic Search Planning with BDDs. In PuK. 2000.

• Hansen, Eric A., and Shlomo Zilberstein. LAO*: A heuristic search algorithm that finds solutions with loops. Artificial Intelligence 129, no. 1-2 (2001): 35-62.

• Bonet, Blai, and Hector Geffner. Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming. In ICAPS, vol. 3, pp. 12-21. 2003.

• Younes, Håkan LS, and Michael L. Littman. PPDDL1.0: An extension to PDDL for expressing planning domains with probabilistic effects. Techn. Rep. CMU-CS-04-162 (2004).

• Sanner, Scott. Relational dynamic influence diagram language (RDDL): Language description. Unpublished ms. Australian National University (2010): 32.

• Hoffmann, Jörg. Everything you always wanted to know about planning. In Annual Conference on Artificial Intelligence, pp. 1-13. Springer Berlin Heidelberg, 2011.

• Sanner, Scott. How to spice up your planning under uncertainty research life. In Workshop on a Reality Check for Planning and Scheduling Under Uncertainty (ICAPS-08). 2008.

Research Groups

• Automated Planning and Control Group at the University of Washington
• Decision-theoretic Planning and Learning in The Artificial Intelligence Group at the University of Waterloo
• Foundations of Artificial Intelligence (FAI) Group
• Foundations of Artificial Intelligence University of Freiburg, Germany
• Planning and Activity Management Group within the Artificial Intelligence Applications Institute (AIAI) in the School of Informatics at the University of Edinburgh
• Planning Research Group in the Department of Informatics at King's College London
• Planning, Cognitive Robotics and Applications Research Group in LIAMF Group at the University of São Paulo, Brazil
• Robotics and InteractionS (RIS) in Laboratory for Analysis and Architecture of Systems at CNRS, France
• The Artificial Intelligence and Machine Learning group at the Universitat Pompeu Fabra (UPF) in Barcelona, Spain

Software

Online Tools

• planning.domains A collection of tools for working with planning domains. Tutorial available at https://www.youtube.com/watch?v=hPoWf-cKSww. Source code available at https://bitbucket.org/planning-researchers/cloud-solver.

Parsers

• Universal PDDL-parser
• PDDL-pp

Planners

Classical

• Lightweight Automated Planning Toolkit LAPKT stands for the Lightweight Automated Planning ToolKiT. It aims to make your life easier if your purpose is to create, use or extend basic to advanced Automated Planners. It's an open-source Toolkit written in C++ and Python with simple interfaces that give you complete flexibility by decoupling parsers from problem representations and algorithms.

• Automated Programming Framework This is the main software repository of the Artificial Intelligence and Machine Learning Research Group at Universitat Pompeu Fabra, Barcelona, Spain. The Automated Programming Framework includes the code necessary to configure and execute different compilations related to the formalisms of Planning Programs and Hierarchical Finite State Controllers.

• FastDownward (FD) Helmert, Malte. The Fast Downward Planning System. J. Artif. Intell. Res.(JAIR) 26 (2006): 191-246.

• FastForward (FF) Hoffmann, Jörg. FF: The fast-forward planning system. AI magazine 22, no. 3 (2001): 57.

• SATPLAN Henry Kautz, Bart Selman, and Joerg Hoffmann (2006). SatPlan: Planning as Satisfiability. Working Notes of the 5th International Planning Competition, Cumbria, UK, 2006.

• blackbox Kautz, Henry, and Bart Selman. Unifying SAT-based and graph-based planning. In IJCAI, vol. 99, pp. 318-325. 1999.

Probabilistic

• MDP-engine Implementation of different algorithms and policies for solving MDPs.

• G-Pack G-pack is a software package for planning under uncertainty that currently implements: LR^2TDP (Reverse Iterative Deepening LRTDP), Glutton, the runner-up at the 2011 International Probabilistic Planning Competition (IPPC-2011), essentially a beefed-up offline version of LR^2TDP; and Gourmand, an online version of Glutton. Because planning offline isn't feasible for large MDPs, Gourmand tends to outperform Glutton on such problems.

• PROST Keller, Thomas, and Patrick Eyerich. PROST: Probabilistic Planning Based on UCT. In ICAPS. 2012.

• SPUDD Hoey, Jesse, Robert St-Aubin, Alan Hu, and Craig Boutilier. SPUDD: Stochastic planning using decision diagrams. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp. 279-288. Morgan Kaufmann Publishers Inc., 1999.

• FPG Buffet, Olivier, and Douglas Aberdeen. The factored policy-gradient planner. Artificial Intelligence 173, no. 5-6 (2009): 722-747.

Simulators

• RDDLSim Implements a parser, simulator, and client/server evaluation architecture for the relational dynamic influence diagram language (RDDL) -- pronounced "riddle".

Libraries

• Colorado University Decision Diagrams (CUDD)

• List of BDD packages

Tutorials

• ICAPS 2016: Planning under Uncertainty (part 1), Scott Sanner

• Probabilistic Planning with Markov Decision Processes, Andrey Kolobov and Mausam

• Advanced Introduction to Planning: Models and Methods, Hector Geffner

• Probabilistic Planning, Blai Bonet

License

Copyright (c) 2017 Thiago Pereira Bueno All Rights Reserved.

Planning-Resources-Roadmap is free software documentation: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Planning-Resources-Roadmap is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public License along with Planning-Resources-Roadmap. If not, see http://www.gnu.org/licenses/.