Automated Task Planning
towards Multi-Agent, Flexible, Temporal, Epistemic and Contingent Models
Tutorial at ECAI 2024, Santiago de Compostela, Spain, October 2024

Description

Automated task planning is the domain of symbolic Artificial Intelligence concerned with computing plans or policies of actions for reaching given goals from initial states. In its most basic formulation, so-called “classical planning” is the problem of computing a sequence of actions leading from a given initial state to one of a given set of goal states, with deterministic actions, and fully observable states at execution time. This simple setting already captures a number of actual problems: controlled environments (games like Sokoban, industrial tasks) and more open environments (like high-level, centralized decisions for logistics).

This tutorial aims at presenting the domain of automated planning to a broad audience, with a focus on “rich” objectives, beyond the classical setting of single-agent, fully observable planning; specifically: multi-agent aspects, flexibility (with respect to execution uncertainty), temporal aspects (duration of actions, concurrent actions), epistemic setting (planning towards mental states), and contingency (in partially observable environments). Accounting for such aspects makes a much wider access to real-life problems than classical planning: taking into account the state of knowledge of other agents or humans in robot-robot or human-robot interactions, acting in highly uncertain (exploration of unknown areas) or highly unobservable environments (ineffective sensors), etc.. For each topic, we present the main modelling frameworks, typical scenarios and their modelling, and the main algorithmic approaches.

Target audience: PhD candidates and more experienced researchers having the feeling that some of their research may benefit, or is linked to, the domain of automated planning.

Prerequisites: Basics of probabilities, logic, and algorithms (undergraduate level).

Format: Half-day (2 × 90 min).

Speakers

Tiago de Lima is an associate professor at the Artois University, France. He has been an active researcher on the field of reasoning about actions and information change for more than 10 years. He coordinates, together with Bruno Zanuttini, the French network MAFTEC (Multi-Agent, Flexible, Temporal, Epistemic, and Contingent Planning), a network of the French National Center for Scientific Research (CNRS).

Frédéric Maris is an associate professor at the University of Toulouse 3, France. He has been an active researcher on the field of automated planning, knowledge representation and reasoning, and satisfiability and constraint programming for more than 10 years. He founded and then co-ordinated from 2016 to 2022 the French network MAFTEC.

Thierry Vidal is an associate professor at UTTOP (Tarbes, France). He has been an active researcher for nearly 30 years in temporal uncertainty management for planning and scheduling, dynamic robust architectures interleaving planning and execution, and more recently shared control of multi-agent interdependent plans. He is also member of the committee of MAFTEC.

Bruno Zanuttini s a full professor at the University of Caen, France. He has been an active researcher on the field of knowledge compilation and reasoning, planning, and sequential decision making for more than 10 years. He coordinates, together with Tiago de Lima, the French network MAFTEC.