IJCAI2021 Tutorial on AI Planning: Theory and Practice

Aug 19 10:00 — 14:30 Montreal Time (UTC-4)

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. Planning applications can be found in dialog systems, cybersecurity, transportation and logistics, as well as IT. Yet, applying AI Planning research in practice faces several challenges, preventing its widespread use. 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 IJCAI attendees the necessary means for using AI planning tools in their applications.

Tutorial length: 1/2 day

Tutorial Schedule (all times are in EST)

Tutorial outline

1. Introduction [slides]
   • What is AI Planning
   • Why? Motivation of this Tutorial — applications
   • Why AI Planning is important
   • How to Spot a Planning Problem — why these applications are planning problems
   • AI Planning, how it works
   • Plan for this Tutorial (Theory, Modeling, Tools)
2. Theory [slides]
   • What is AI Planning: Models and Languages
   • Models and Languages
   • Computational Problems
   • Planning and RL
   • Solving Classical Planning
   • History
   • Planning as Heuristic Search
   • Heuristics for Classical Planning
   • Search Pruning Techniques
   • Planners and Planning Competitions
   • International Planning Competition
   • Major Planning Toolkits/Systems/Families
   • Non-IPC Planners and Tools
3. Modeling [slides]
   • Modeling Challenges
   • Relationship to Planning
   • Overview of our solutions
     • Automating machine learning pipeline generation
     • Hypothesis generation problem
     • Scenario Planning for Enterprise Risk Management
     • Planning for dialog
   • Overview of other approaches
   • Modeling Summary
4. Tools and Applications [slides]
   • Challenges in Using AI Planning in Applications
   • Pre/Post Processing
   • Hands on: Automating ML pipeline generation/exploration (instructions here and more details)
     1. Problem Definition
     2. Modeling
     3. Transformation (HTN to classical planning transformation)
     4. Additional constraints
     5. Computation of plans (top-k planning)
     6. Postprocessing of the plans to pipelines
     7. Pipeline accuracy and its translation to action costs
   • Quick overview of other applications:
     1. Automated Analytic Composition
     2. Data Science Automation
     3. Hypothesis generation
     4. Scenario Planning
5. Summary and Q&A [slides]
   • Lessons learned
   • Improvements/future work

ML pipeline generation/exploration hands-on

• Instructions for the hands-on part and the code/notebooks you need are on Github
• Some planning tools we will be using in this tutorial:
  • HTN2PDDL translation
  • K* planner
  • PlanUtils
  • PDDLEditor plugin for VSCode

Target Audience

Potential target audience are researchers from outside the field of planning, most of the typical IJCAI attendees. No prior knowledge is necessary, since we plan 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 attendees the necessary knowledge and experience so that they can start using AI Planning tools in their applications. We plan to 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.

The following objectives fit out tutorial:

1. Educate experts or non experts to established but specialized AI methodologies
2. Introduce expert non-specialists to an AI area

Useful links

Major Planning Toolkits/Systems/Families

Non-IPC Planners and Tools

Other Major Community Efforts