Multi-Agent Planning deals with the task of generating a plan for/by a set of agents that jointly solve a planning problem. One of the biggest challenges is how to handle interactions arising from agents' actions. The first contribution of the paper is Plan Merging by Reuse, PMR, an algorithm that automatically adjusts its behaviour to the level of interaction. Given a multi-agent planning task, PMR assigns goals to specific agents. The chosen agents solve their individual planning tasks and the resulting plans are merged. Since merged plans are not always valid, PMR performs planning by reuse to generate a valid plan. The second contribution of the paper is RRPT-PLAN, a stochastic plan-reuse planner that combines plan reuse, standard search and sampling. We have performed extensive sets of experiments in order to analyze the performance of PMR in relation to state of the art multi-agent planning techniques.
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach has to find a solution inside a search space that contains every possible combination of robots and goals. This leads to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a new way to solve this kind of tasks efficiently. Previous works on MAP have proposed to factorize the problem to decrease the planning effort i.e. dividing the goals among the agents (robots). However, these techniques do not scale when the number of agents and goals grow. Also, in most real world scenarios with big maps, goals might not be reached by every robot so it has a computational cost associated. In this paper we propose a combination of robotics and planning techniques to alleviate and boost the computation of the goal assignment process. We use Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. Thus, specific information can be extracted to know which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to a multi-agent planning algorithm, the goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.
David; por muchos años más votando charlas en navidad y dando regalos de Reyes que hagan más diverso y accesible el sector tecnológico. Ali, Diego, Marina, Sara, María, Álvaro, Lisardo...sois tantos que no tengo espacio para nombraros uno a uno, pero ole ese equipazo de voluntarios y lo que os echen. Con T3chFest llegaron los eventos y las comunidades, y así mucha gente con un gran potencial y corazón. Carlos,
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