Nowadays, many important real-world problems require efficient constraint techniques for AI planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Some example of these problems are synchronizing the on-board activities of a space mission while performing navigation tasks, coordinating the movement of personnel and supplies to support disaster relief efforts, and managing the flow of materials through an automated manufacturing facility. Such problems are typically known as highly coupled planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives (e.g., maximizing scientific return of space missions, initiating relief efforts as soon as possible, maximizing product throughput). At the same time, since the executability of a given goal activity in such problems also depends on conditions of the predicted world state other than resource availability (e.g., spacecraft vibration level, the locations of transport or material handling vehicles), solution feasibility can only be guaranteed by dynamically generating and synchronizing the auxiliary activities necessary to bring about and preserve enabling state conditions. Thus, solutions to these problems must integrate resource allocation and plan synthesis capabilities. In short, we need to manage complex problems where planning, scheduling and constraint satisfaction must be interrelated, which entail a great potential.

The aim of this workshop is to join researchers of the field of Artificial Intelligence working on planning, scheduling, constraint satisfaction problems (CSPs), and many other common areas that exist among them. In fact, most of the works are based on combined approaches of constraints and planning, scheduling and planning, planning and scheduling, etc.

Accepted Papers