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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.

Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure – including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.

Hemophilia A is an X-linked inherited blood coagulation disorder caused by the production and circulation of defective coagulation factor VIII protein. People living with this condition receive either prophylaxis or on-demand treatment, and approximately 30% of patients develop inhibitor antibodies, a serious complication that limits treatment options. Although previous studies performed targeted mutations to identify important residues of FVIII, a detailed understanding of the role of each amino acid and their neighboring residues is still lacking. Here, we addressed this issue by creating a residue interaction network (RIN) where the nodes are the FVIII residues, and two nodes are connected if their corresponding residues are in close proximity in the FVIII protein structure. We studied the characteristics of all residues in this network and found important properties related to disease severity, interaction to other proteins and structural stability. Importantly, we found that the RIN-derived properties were in close agreement with in vitro and clinical reports, corroborating the observation that the patterns derived from this detailed map of the FVIII protein architecture accurately capture the biological properties of FVIII.