Numerical and Temporal Planning for a Multi-agent Team Acting in the Real World

Introduction. Automated planning is a central area of Artificial Intelligence which aims to design a powerful deliberation layer for autonomous intelligent systems. Autonomy of intelligent systems doesn’t concern only planning and deliberation but also acting. These two aspects are not completely disjoint: actors may deliberate or plan both before and during acting in order to perform intelligent executions, and deliberation may be strictly influenced by acting details ([1]). The gap between planning and execution is one of the main problems to face in building an autonomous system and in order to successfully do it, planning should capture important features of real world domains ([2]). In particular, when problems involve teams of (possibly heterogeneous) agents which must cooperate, relevant features to take into account are cooperation, consumable resources, continuous numeric change, as well as concurrency, time and temporal constraints. In recent years, automatic planning languages have been extended with primitives allowing to express numerical and temporal aspects of problems (e.g. PDDL 2.1 and PDDL 2.2 ([3] and [4])). In this way, relevant aspects for execution can be taken into account already at planning time. Many alternative approaches to action-based planning have been developed. In particular timeline-based approaches (e.g. [5], [6] or the mission planning frameworks [7], [8]) or MILP approaches (e.g. [9], [10]). Defining the problem. In this work we aim at showing how complex realworld multi-agent problems involving consumable resources, continuous numeric change, time and multi-agent coordination can be faced with action-based approaches such as numerical and temporal planning by employing state-of-art general purpose planners. We developed a complex software architecture oriented to a centralized offline planning system. This approach to multi-agent planning is justified by the many interactions required among agents, by the expensive coordination activities in heterogeneous teams and by optimization reasons (i.e. maximizing the number of tasks assigned to the agents and minimizing the number of agents employed). These factors make more appropriate a centralized approach than a distributed one. However, we left the low-level controls (e.g. sensor pointing,