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Multi-Agent Systems (MASs) have been used to solve complex problems which demand intelligent agents working together to reach the desired goals. These Agents should effectively synchronize their individual behaviors so that they can act as a team in a coordinated manner to achieve the common goal of the whole system. One of the main issues in MASs is the agents' coordination, being common domain experts observing MASs execution disapprove agents' decisions. Even if the MAS was designed using the best methods and tools for agents' coordination, this difference of decisions between experts and MAS is confirmed. Therefore, this paper proposes a new dataset schema to support learning the coordinated behavior in MASs from demonstration. The results of the proposed solution are validated in a Multi-Robot System (MRS) organizing a collection of new cooperative plans recommendations from the demonstration by domain experts. Keywords Multi-Agent System · Learning from Demonstration · Dataset · Coordination · Multi-robot plan · clustering PACS 07.05.Mh Dataset Schema for Cooperative Learning in a MRS 3Learning from Demonstration (LfD) has been used to learn robots basic skills or even very simple setplays in a MRS [11]. Although, there is no register of using LfD to learn complex setplays. Therefore, this work proposes using LfD to offer domain experts a chance to watch robotic soccer matches and suggest new setplays for each situation for which they think the MRS has made a bad decision.Section 3 presents the state-of-the-art for learning coordinated plans in MAS. One of the main issues in LfD for setplays is the nature of the dataset generated from the domain experts recommendation. Some features in this dataset are not of primitive types as scalars or strings, but some complex types, such as objects, structures, trees, etc. Thus, we also define a strategy presented in Section 2.4, to handle this kind of complex data.The proposed solution has a two-level dataset, detailed in Section 4. To assess the feasibility of using this dataset to support setplays learning, the Fuzzy C-Means (FCM) algorithm is used to organize setplays recommendations into clusters. The choice of the FCM algorithm is due to the imprecision inherent in the friendly interface proposed for use by experts to generate the recommendations of setplays. The suggestions from experts are organized in clusters to solve the semantic equivalence issue presented in Section 2. Section 5 describes the assessment process and its results. Section 6 has conclusion and future work descriptions.

Coordination is an important requirement for most Multiagent Systems. A setplay is a particular instance of a coordinated plan for multi-robot systems in collective sports. Setplays are usually designed by robotics specialists using some existing tools, like the SPlanner, or by hand-coding. This work presents recent improvements to the Strategy Planner (SPlanner) and its corresponding FCPortugal Setplays Framework (FSF) to provide sophisticated setplays. This toolkit is useful to design strategic plans for robotic soccer teams as a particular case of Multi-Agent Systems (MASs). The new enhancements enable more realistic setplays, including, but not limited to, the definition of better pass strategies and defensive setplays. The enhanced tool is used to populate a dataset with demonstrations made by soccer experts and used in a Learning from Demonstration (LfD) approach to allow robotic soccer teams to learn new setplays. A new demonstration mode in the RoboCup Soccer Simulation 3D (SSIM3D) viewer RoboViz was also introduced to integrate this tool with SPlanner. Domain experts can use this set of tools to capture a specific scene in a game in RoboViz and use it as an initial step for a new setplay recommendation in SPlanner. The resulting dataset is organized into fuzzy clusters to be used in a reinforcement learning strategy. This paper describes the whole process. Article Highlights This paper’s main contribution is generating a dataset of setplays to support learning from demonstration in robotic soccer. A set of new features were added to the Strategic Planner(SPlanner) to enable the design of more realistic setplays. The official RoboCup viewer (Roboviz) was integrated with SPlanner using a new demonstration mode.