Multiagent Hierarchical Cognition Difference Policy for Multiagent Cooperation

Multiagent cooperation is one of the most attractive research fields in multiagent systems. There are many attempts made by researchers in this field to promote cooperation behavior. However, several issues still exist, such as complex interactions among different groups of agents, redundant communication contents of irrelevant agents, which prevents the learning and convergence of agent cooperation behaviors. To address the limitations above, a novel method called multiagent hierarchical cognition difference policy (MA-HCDP) is proposed in this paper. It includes a hierarchical group network (HGN), a cognition difference network (CDN), and a soft communication network (SCN). HGN is designed to distinguish different underlying information of diverse groups’ observations (including friendly group, enemy group, and object group) and extract different high-dimensional state representations of different groups. CDN is designed based on a variational auto-encoder to allow each agent to choose its neighbors (communication targets) adaptively with its environment cognition difference. SCN is designed to handle the complex interactions among the agents with a soft attention mechanism. The results of simulations demonstrate the superior effectiveness of our method compared with existing methods.

Unity in Variety: Developing a collection description standard by consensus

Digitisation and publication of museum specimen data is happening worldwide, but far from complete. Museums can start by sharing what they know about their holdings at a higher level, long before each object has its own record. Information about what is held in collections worldwide is needed by many stakeholders including collections managers, funders, researchers, policy-makers, industry, and educators. To aggregate this information from collections, the data need to be standardised (Johnston and Robinson 2002). So, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Task Group is developing a data standard for describing collections, which gives the ability to provide: automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). Outputs will include a data model to underpin the new standard, and guidance and reference implementations for the practical use of the standard in institutional and collaborative data infrastructures. The Task Group employs a community-driven approach to standard development. With international participation, workshops at the Natural History Museum (London 2019) and the MOBILISE workshop (Warsaw 2020) allowed over 50 people to contribute this work. Our group organized online "barbecues" (BBQs) so that many more could contribute to standard definitions and address data model design challenges. Cloud-based tools (e.g., GitHub, Google Sheets) are used to organise and publish the group's work and make it easy to participate. A Wikibase instance is also used to test and demonstrate the model using real data. There are a range of global, regional, and national initiatives interested in the standard (see Task Group charter). Some, like GRSciColl (now at the Global Biodiversity Information Facility (GBIF)), Index Herbariorum (IH), and the iDigBio US Collections List are existing catalogues. Others, including the Consortium of European Taxonomic Facilities (CETAF) and the Distributed System of Scientific Collections (DiSSCo), include collection descriptions as a key part of their near-term development plans. As part of the EU-funded SYNTHESYS+ project, GBIF organized a virtual workshop: Advancing the Catalogue of the World's Natural History Collections to get international input for such a resource that would use this CD standard. Some major complexities present themselves in designing a standardised approach to represent collection descriptions data. It is not the first time that the natural science collections community has tried to address them (see the TDWG Natural Collections Description standard). Beyond natural sciences, the library community in particular gave thought to this (Heaney 2001, Johnston and Robinson 2002), noting significant difficulties. One hurdle is that collections may be broken down into different degrees of granularity according to different criteria, and may also overlap so that a single object can be represented in more than one collection description. Managing statistics such as numbers of objects is complex due to data gaps and variable degrees of certainty about collection contents. It also takes considerable effort from collections staff to generate structured data about their undigitised holdings. We need to support simple, high-level collection summaries as well as detailed quantitative data, and to be able to update as needed. We need a simple approach, but one that can also handle the complexities of data, scope, and social needs, for digitised and undigitised collections. The data standard itself is a defined set of classes and properties that can be used to represent groups of collection objects and their associated information. These incorporate common characteristics ('dimensions') by which we want to describe, group and break down our collections, metrics for quantifying those collections, and properties such as persistent identifiers for tracking collections and managing their digital counterparts. Existing terms from other standards (e.g. Darwin Core, ABCD) are re-used if possible. The data model (Fig. 1) underpinning the standard defines the relationships between those different classes, and ensures that the structure as well as the content are comparable across different datasets. It centres around the core concept of an 'object group', representing a set of physical objects that is defined by one or more dimensions (e.g., taxonomy and geographic origin), and linked to other entities such as the holding institution. To the object group, quantitative data about its contents are attached (e.g. counts of objects or taxa), along with more qualitative information describing the contents of the group as a whole. In this presentation, we will describe the draft standard and data model with examples of early adoption for real-world and example data. We will also discuss the vision of how the new standard may be adopted and its potential impact on collection discoverability across the collections community.

Application of Modern Information Technologies in the Formation of Collective Design Competencies on the Example of Group Management of Heterogeneous Robots

The subject of these studies is to propose an approach to the formation of collective design competencies in the new digitalization environment. The relevance of the task in various fields of activity is shown. It is noted that while the latest information technologies, which appeared in the era of universal digitalization, are not yet sufficiently applied to build the competencies of designers as part of collective activities, the training of specialists is mainly carried out as part of individual projects. The studies were conducted on the basis of the actual applied task of collective design of group control of heterogeneous robots. The unique relationship of the object (group control) and the method of its research (group parallel design) is noted. A connection is established between the features of the ongoing project of group control of robots and the formation of in-depth competencies in the training of specialists in robotics and mechatronics. New representations of functional models are formed taking into account the needs of new information technologies. Within the framework of parallel and compositional design technologies based on CDP4 ™- and System Composer ™, models have been obtained that implement functional schemes for group control of heterogeneous robots in two versions, suitable for use both for educational purposes to form team design competencies and for real projects.

Novel Geometric Morphometric (GMM) Application to the Study of Bronze Age Tutuli

In this paper, we examine the morphological diversity of the tutuli object group from the earlier Nordic Bronze Age (henceforth NBA) – an often over-looked object group despite their abundance specially, temporally and contextually. Currently, only a few studies of the morphological diversity of tutuli have been published, and these consist primarily of decade-old-typologies. The objective of this of this paper is first and foremost methodological, as we examine two research questions – concerning classification and periodisation – through a novel two-dimensional geometric morphometric (henceforth GMM) framework and subsequent multivariate analysis. Inherently we examine whether specific shapes conform to the classificatory of the Montelius typology, and whether a temporal relationship exists between types and shapes.

Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification

Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specification: when one object group (i.e. a group of records in a data table) is specified, Duet recommends object groups that are similar to or different from the specified one; when two object groups are specified, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specification is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.

ColScript a New Scripting Language for Collaborative Learning

The collaboration script is defined as a formal way of describing the flow of activities within a collaborative learning session. Using collaboration script would encourage the production of effective and productive interactions between learners. Nevertheless, developing such script is not a trivial task. Standardization has played a major role in the expansion of instructional designs, but at the same time it limited down the flexibility of describing collaboration sessions that have complex structures. Representing a collaboration script in XML-tags format works well when scripting simple scenarios, but to describe extended scenarios it would make this scripting style very challenging and complicated. This approach causes users to avoid designing heavy weight scenarios and limits down their creativity. Relying on tools to implicitly generate such script would also limit down designers’ creativity since designers can only choose from a limited set of tools and design components. In this research, we have defined the bases of a new collaboration scripting language, CoScript, that is able to describe collaboration learning sessions in a simple, flexible, and formal way. This scripting language has been derived based on a theoretical framework that was proposed in an earlier research. The proposed scripting language notation is close to the notation of traditional software scripting languages. This makes it easier to be learnt by instructors with basic programming skills. It has the ability to describe design’s structure elements, such as sequencing, conditions, repetition, activities, activity’s input /output, group formation, etc. ColScript is basically composed of a limited set of objects and commands. The first part contains six objects (role object, group object, feedback object, collaboration tools object, time/date object and resources object), where the second part contains five essential structuring commands (input, output, loop, doactivity, groupformation).