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Tutorial 04 - Planning for Storytelling Abstract In this tutorial we will demonstrate the role that planning, or planning-based representations, can play in narrative generation methods. The plan becomes the representation of the story and also that of the story world, and thus we use Planning to create logical, believable, and coherent stories (narratives) in a variety of domains. We will cover several techniques, including modern approaches that make use of Large Language Models (LLMs) and provide the opportunity for attendees to play with the technology themselves live.
ICAPS 2024 Awards Outstanding Reviewer Award Miquel Ramirez Outstanding Senior Program Committee Award Arthur Bit-Monnot Fabio Patrizi Best Paper Award Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning David Speck, Daniel Gnad
Best Paper Award (Honorable Mention) Expressiveness of Graph Neural Networks in Planning Domains Rostislav Horcik, Gustav Šír
Best Student Paper Award Accelerating Search-Based Planning for Multi-Robot Manipulation by Leveraging Online-Generated Experiences Yorai Shaoul, Itamar Mishani, Maxim Likhachev, Jiaoyang Li
Tutorial 01 - AI Techniques for Solving Scheduling Problems Abstract Scheduling problems arise in various areas, including business, engineering, healthcare, and others. In this tutorial, we will first present several scheduling problems and case studies from various application domains, such as project scheduling, production planning and scheduling, employee scheduling, and timetabling. We will then provide an overview of different AI methods for solving such problems. The topics covered will include solver-independent modeling, constraint programming, metaheuristic methods, and hybrid techniques.
Tutorial 02 - Finding Multiple Plans for Classical Planning Problems Abstract The goal of the tutorial is to familiarise the audience with the theoretical and practical aspects of devising multiple plans for classical planning problems. We will motivate the need for such planners from application perspective, formally define the respective computational problems, as well as describe the existing approaches to solving these problems. We will finish with a hands-on session on using the existing planners.
Tutorial 03 - A Hands-On Tutorial on scikit-decide, the Open-Source C++ and Python Library for Planning, Scheduling and Reinforcement Learning Abstract Scikit-decide is an open-source library for modeling and solving planning, scheduling and reinforcement learning problems within a common API which helps break technical silos between different decision-making communities and enables seamless benchmarking of different approaches. For instance, one can solve PDDL problems with both classical planning (via a bridge to Unified Planning) and reinforcement learning (via a bridge to RLlib) solvers with very few lines of code, and compare the different solutions.
Tutorial 05 - Orchestrating Autonomous Agents: Reinforcement Learning for Hierarchical Planning with COACH Abstract This tutorial will provide an introduction to COACH - a suite of Python tools for recasting Gymnasium and PettingZoo-compatible Multi-Agent Reinforcement Learning (MARL) problems as orchestration-style planning problems. Traditional Reinforcement Learning (RL) focuses on training low level agents to interact with an environment in a high frequency feedback loop. Once policies have been trained, human direction becomes an orchestration problem, especially with large numbers of agents.
Knowledge Engineering for Planning and Scheduling (KEPS 2024) 2024 Workshop on Knowledge Engineering for Planning and Scheduling Banff, Canada June 2-3, 2024
Aim and Scope of the Workshop Despite the progress in automated planning and scheduling systems, these systems still need to be fed by carefully engineered domain and problem descriptions and they need to be fine-tuned for particular domains and problems. Knowledge engineering for AI planning and scheduling deals with the acquisition, design, validation and maintenance of domain models, and the selection and optimization of appropriate machinery to work on them.
Bridging the Gap Between AI Planning and Reinforcement Learning (PRL) ICAPS'24 Workshop Banff, Alberta, Canada Date: June 2, 2024
Aim and Scope of the Workshop While AI Planning and Reinforcement Learning communities focus on similar sequential decision-making problems, these communities remain somewhat unaware of each other on specific problems, techniques, methodologies, and evaluations.
This workshop aims to encourage discussion and collaboration between researchers in the fields of AI planning and reinforcement learning.
Workshop on Reliable Data-Driven Planning and Scheduling ICAPS'24 Workshop on Reliable Data-Driven Planning and Scheduling (RDDPS) Banff, Alberta, Canada June 3, 2024
Aim and Scope of the Workshop Data-driven AI is the dominating trend in AI at this time. From a planning and scheduling perspective – and for sequential decision making in general – this is manifested in two major kinds of technical artifacts that are rapidly gaining importance. The first are planning models that are (partially) learned from data (e.
