AAAI 2022 Tutorial on AI Planning: Theory and Practice

Shirin Sohrabi Michael Katz Octavian Udrea
Shirin Sohrabi Michael Katz Octavian Udrea


Brief description

The tutorial provides a theoretical background on AI Planning and introduces some of the existing tools, as well as existing applications that were tackled with these tools. We show how to use the tools on an example application. The participants will get the knowledge and hands-on experience necessary to start using AI Planning tools in their applications.

Overview

AI Planning is a long standing sub-area of AI, dealing with sequential decision making, a sister field to Reinforcement Learning. Mature industrial applications of planning technology can be seen in various fields, such as dialog systems, cybersecurity, transportation and logistics, as well as IT. While model-based planning tools allow to solve problems of practical size, applying AI Planning research in practice faces several challenges, preventing its widespread use. The alternative of using model-free methods, however, often proves infeasible for real life size problems. The aim of this tutorial is to provide the audience with the necessary theoretical background knowledge, as well as hands-on experience to allow for using planning tools for solving everyday challenges.

In this tutorial, we will provide an overview of the field of planning, including recent advances in the field. We will then dive into three challenges: (1) modeling — how to represent, extract, and learn the knowledge; (2) theory — formal definition of the computational problems; (3) tooling — how to solve the computational problems. We will have a hands-on session to exemplify the use of planning tools for solving an example application. Our aim is to give AAAI attendees the necessary means for using AI planning tools in their applications.

Target Audience

Potential target audience are researchers in AI that want to know more about planning, typical AAAI attendees. No prior knowledge is necessary, since we plan (pun intended) to cover all the necessary background. In particular, we hope that researchers with no prior exposure to AI Planning will find our tutorial interesting and motivating for their work.

Why Should I Attend

Our main goal is to give the researchers in AI that are interested in sequential decision making a basic knowledge about the field of AI planning. We will provide you with the tools to solve various real life size sequential decision making problems. We will cover several applications of AI Planning, challenges, and solutions and give the attendees a hands-on experience of using planning tools in one of these applications.

Previous Tutorials

Tutorial outline

  1. Introduction
  2. Theory
  3. Modeling
  4. Tools and Applications
  5. Summary and Q&A

ML pipeline generation/exploration hands-on


Useful links

Major Planning Toolkits/Systems/Families

Fast-Forward (better known as FF)
Fast Downward
Lightweight Automated Planning ToolKiT (LAPKT)
LPG
SHOP2
OPTIC.

Non-IPC Planners and Tools

Planning service for cost-optimal, agile, satisficing, top-k, top-quality, diverse planning, also available as docker image
Planner in the cloud and a collection of tools
Forbid-Iterative Collection of planners for top-k, top-quality, diverse planning
Top-k planners K* and SymK
OSP planners A*-based and symbolic- search-based
FOND planner PRP
Pyperplan Lightweight python-based planner developed for educational purposes

Other Major Community Efforts

Slack workspace
ICAPS web site
Community GitHub
PlanUtils
Planning Wiki (initial effort), including a list of planners.