Context-aware Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine

Alexander Smirnov a, Nikolay Shilov b and Andrew Ponomarev c SPIIRAS, 14th Line, 39, St.Petersburg, Russian Federation

Keywords: Socio-Cyber-Physical Systems, Collective Intelligence, Hybrid Systems, Context-aware , Ontology-based Systems, Multi-aspect Ontology, Role-based , Dynamic Motivation.

Abstract: The competitiveness of companies and heavily depends on how they maintain and access highly decentralized up-to-date & knowledge coming from various resources located in their Socio-Cyber-Physical Systems. Such systems tightly integrate heterogeneous resources of the physical world and IT (cyber) world together with social networking concepts. Context-Aware Knowledge Management is becoming de facto one of the required business strategies in these systems. Its goal is to facilitate knowledge transfer and in the context of business structures and activities bound together with the cultural norms. This keynote presents new trends (including role-based organization, dynamic motivation mechanisms and multi-aspect ontology) in knowledge management for socio-cyber-physical systems. Such trends can facilitate creation of innovative IT & HR environments based on human-machine collective intelligence, where information & knowledge are shared between participants and across of participants, who can be both people (collective intelligence as the methods used by humans to act collectively for ) and software services (based on models). The keynote considers examples of trends and their implementation experience in a global production company.

1 INTRODUCTION SCPSs examples can be found in modern production environments, especially those adopting the Industry The concept of Socio-Cyber-Physical System 4.0 concept. (SCPS) integrating in real-time physical systems Advances in the mobility, cloud computing, (e.g., physical production equipment, vehicles, , and big data analytics increase the devices), IT components (e.g., enterprise resource number and kinds of networked connections in planning, manufacturing execution systems or other business environments, as well as the opportunities information systems), and human actors for people and machines to derive unpredictable (organizational roles and stakeholders) at individual value from these connections (Pew Research Center, and social network level is becoming more and more 2014). important in modern IT landscape. Knowledge management (KM) allowing to Currently, more and more systems (including locate knowledge/skill for a task at hand is a crucial “system of systems”) in many areas are recognized for successful , in particularly in the to be socio-cyber-physical, and this spurs on the systems with heterogeneous entities (as in SCPSs). research in the area of SCPSs, aimed at creating Distributed work in product design, manufacturing, coherent tools and for the SCPSs and supply management projects requires decision development and evolution. Quite a few good support for the involved parties tailored to the actual context of these parties (depending on their nature). a https://orcid.org/0000-0001-8364-073X b https://orcid.org/0000-0002-9264-9127 c https://orcid.org/0000-0002-9380-5064

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Smirnov, A., Shilov, N. and Ponomarev, A. Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence. DOI: 10.5220/0010171800050017 In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS, pages 5-17 ISBN: 978-989-758-474-9 Copyright c 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

IC3K 2020 - 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Network-wise modern SCPSs are based on purpose of ontologies is to represent knowledge integration of a number of networks supported by about a certain domain in a machine-readable way. the following information technologies (A. Smirnov Ontologies allow to describe, share and process & Sandkuhl, 2015): knowledge considering its syntax along with its  Social networks: who knows whom => Virtual semantics. They are formal conceptualizations of Communities; certain domain of interest that are shared between  Knowledge networks: who knows what => different applications (Gruber, 1993; Staab & Human & Knowledge Management; Studer, 2009). The ontology describes concepts,  Information networks: who informs what => their relationships and axioms to exist in the /Intranet/Extranet/Cloud; given domain. They are considered an efficient  Work networks: who works where => mean to solve the interoperability problem. In Decision Support based on Crowdsourcing particular, ontologies turn out to be effective in and Recommendation Systems; encoding context.  Competency networks: what is where => Context model serves to represent the knowledge Knowledge Map; about a current situation (the environment  Inter-organizational network: organizational properties, the current problem, as well as states of linkages => Semantic-Driven Interoperability. the stakeholders). In general, SCPSs are reconfigurable dynamic These models, for instance, are used to reveal systems; their elements may have variety of possible user preferences based on the analysis of the context states and arrange in dynamically arrange in representations in conjunction with the implemented problem-centric compositions. This provides an decisions. additional requirement for successful KM in SCPSs. Namely context-awareness. The context is usually 2.1 Role-based Organization defined as any information that can be used to characterize the situation of an entity, where an Personalized support is important for modern entity is a person, place, or object that is considered business applications. As a rule, it is based on relevant to the interaction between a user and an application of the profiling technology. Each user (a application, including the user and applications human or an information system) works on a themselves (Dey, Abowd, & Salber, 2001). particular problem or scenario represented via a This paper describes some trends in context that may be characterized by a particular implementing context-aware KM in SCPSs. customer order, its time, requirements, etc. The rest of the paper is organized as follows. Research efforts in the area of information Section 2 describes some important trends in KM in logistics show information and knowledge needs of SCPSs. Section 3 discusses practical application of a particular employee depend on his/her tasks and these trends in solving KM problems in a production responsibilities (Lundqvist, 2007). Therefore, in company. Finally, section 4 presents a design of a business applications the idea of personalization human-machine collective intelligent environment, (identification of implicit context of the request) can which follows these trends and can be used in a be extended with the knowledge of the user’s role. variety of problem domains to effectively solve KM Besides, it is also the case that representatives of problems at decision support by human-machine adjacent (in terms of business process) roles can collectives. have slightly different goals and use different terminology (even referring to the same concepts). The idea of the role-based approach is to consider the workflows and information models 2 MODERN TRENDS IN CAKM from perspectives of different roles that deal with FOR SCPS them. Role-based organization for ontology-based KM This section describes some modern trends in the assumes the following steps: context-aware knowledge management (CAKM) for 1. Structural information about workflows and socio-cyber-physical systems and shows how the the problem domain is collected and described respective emerging technologies can facilitate the in the common ontology. creation of innovative IT&HR environments. 2. User roles are identified and their relevant Ontology-based knowledge representation is in parts of the common ontology are defined. the core of these trends; it is their enabler. The

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Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence

3. Tasks assigned to the identified roles are The first question is extensively studied in defined. human resources management area. 4. Knowledge required for performing identified The second one is more relevant to IT. There are tasks is defined. two classes of solutions: specialized solutions 5. Based on the identified roles, tasks and (tailored for the particular problem) and generic knowledge new knowledge-based workflows solutions. are defined. An example of using specialized solution for 6. Corresponding role-based knowledge support implementing the dynamic motivation approach to of the workflows is provided based on the increase the project management efficiency based on usage of the common ontology and the competency management system is described in knowledge / information storages. (Smirnov, Kashevnik, et al., 2019). The solution includes reference and mathematical models of This process repeats for each particular role, with language expert network, which are used for the some knowledge being reused by several roles. automated assignment of organization’s personnel to The implementation of the approach is described projects. They allow formalizing not only the in Section 3.1. individual employees’ skills, but also their achievements and strengths. 2.2 Dynamic Motivation Mechanisms A prominent example of generic solution is PRINGL language (Scekic, Truong, & Dustdar, Despite the prevalence of the KM systems aimed at 2015) allowing to define motivation policies in an improving within the application independent way and connect to some organization sometimes these KM mechanisms add information system via an application programming responsibilities and activities that have to be done by interface. employees and that are not seen as important as the primary (productive) activities. Therefore, 2.3 Multi-aspect Ontology employees may evade using the organized KM solutions or even feel threatened by organizing their The purpose of ontologies is to represent knowledge knowledge in an accessible manner as it might make about a certain domain in a machine-readable way. them ‘replaceable’. An important task of However, in some complex domains, like Product management is to establish open and fair corporate Lifecycle Management (PLM), the application of culture that values KM. One of the most important ontologies is complicated since it has to deal with aspects that have to be considered is aligning interdisciplinary information and knowledge related organization and employees goals via employee to different phases (Shilov, Smirnov, & Ansari, motivation (Friedrich, Becker, Kramer, Wirth, & 2020). The terminology and notations used in Schneider, 2020). Especially, dynamic motivation various processes are different since they are aimed (the type of motivation that changes within a short at solving tasks of different nature that require period of time). A good example of dynamic different techniques (Asmae, Souhail, Moukhtar, & motivation used by the retailer companies are: the Hussein, 2017; Palmer, Urwin, Young, & best sellers boards, scores in the corporate systems Marilungo, 2017). To a certain extent, this problem and etc. is similar to that of role-based information The empirical study has proven that dynamic representation, where the information and motivation seems to yield high levels of knowledge have to be presented to different roles in engagement, , and of performance and different views and terminologies. effectiveness in organizational implementation Some research efforts were aimed for enriching processes. In addition, dynamic motivation also ontologies with additional information that could seems to positively contribute to collaborative work represent additional facts originally described in a and team performance (Ferreira, Araújo, Fernandes, different notation (e.g., semantic annotations (Liao, & Miguel, 2017). Lezoche, Panetto, & Boudjlida, 2016), DAML+OIL The use of dynamic motivation in some SCPS extensions for configuration problem descriptions relies on answering two questions: (Felfernig, Friedrich, Jannach, Stumptner, & Zanker, 1) How should the participants be motivated 2003), and others). However, this still cannot be an (what rewards are effective)? efficient solution for problems of integrating 2) What software solutions can be used to information and knowledge from multiple different define dynamic motivation mechanisms?

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IC3K 2020 - 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

notations and terminologies, which is the case for MVP-OWL extends OWL-DL (the complete PLM. description is presented in (Hemam & Boufaïda, One of the common solutions for multi-domain 2011)). Firstly, it supports viewpoints that describe systems is having a common ontology at the top and information and knowledge related to a certain task its extension for specific sub-domains (e.g., or process. Secondly, classes and properties are configuration problem solving). However, it is not divided into two groups: local – observed only from efficient for dynamic domains with large number of one viewpoint, and global – observed from two or sub-domains, since this would require a continuous more viewpoints. The instances can only be local, ontology matching and modifications of the however since MVP-OWL supports multi- common ontology. instantiation, the instances can exist in several Ontology matching can also be used separately viewpoints at the same time. Thirdly, the authors for establishing links between multiple domain- introduce “bridge rules” of four types, which enable specific ontologies. However, manual ontology relating concepts from different viewpoints. matching would require too much time and efforts in This approach is the most suitable for the dynamically changing domains and automatic problem since it supports resolving ontology matching is still not a reliable instrument terminological issues, and also makes it possible to since the existing methods deliver high level of preserve original formalisms used in existing precision only in narrow domains. ontologies. The authors of (Lafleur et al., 2016) propose a model-driven interoperability framework aimed at supporting relationships between products and 3 CAKM IMPLEMENTATION manufacturing equipment. They form a “connection framework” describing relationships between This section describes several particular different product ontologies maintained in the PLM organizational KM problems and how they are system and different ontologies of manufacturing successfully approached by modern solutions capabilities managed in the Manufacturing Process described in Section 2. Management system. However, having multiple The problem at hand is product and knowledge ontologies for different tasks is not an efficient management in a large automation manufacturing solution for the problem identified either. Since company. This section integrates results of several translating information from one specific ontology projects carried out by the research team, the paper to another assumes a translation between the source authors belong to, together with representatives of ontology and the common ontology and then the company. between the target ontology, what eventually will cause information losses. 3.1 Role-based Organization Another approach is to preserve the original domain ontologies and build an additional layer at This approach was implemented in the frame of the the top of them. The authors of (Hagedorn, Smith, project reported in (Smirnov, Levashova, & Shilov, Krishnamurty, & Grosse, 2019) propose to use a 2015). Basic Formal Ontology (BFO) as a top-level The first step of the approach implementation ontology for describing various engineering was the ontology creation. The resulting ontology domains, and to re-engineer the existing ontologies consists of over 1000 classes organized into a four so that they would be compliant to it. level taxonomy based on the VDMA (Verband Viewing a problem domain from a number of Deutscher Maschinen – und Anlagenbau, German viewpoints has resulted in appearance of Multi- Engineering Federation) classification (VDMA. Viewpoints Ontology (MVpOnt). In MVpOnt each German Engineering Federation, 2018). Later it was viewpoint corresponds to the knowledge extended with descriptions of complex products, representation model useful for a particular task, their components and compatibility rules. process, or a group of people co-existing in a At the second step, the major roles, whose common information environment and sharing some workflows were addressed by KM implementation, information and knowledge. These viewpoints are have been identified. They included product described in a specialized language for the multi- manager, product engineer, production manager, and viewpoint ontologies called MVP-OWL) (Hemam & production engineer. Boufaïda, 2011). In 2018 MVP-OWL was extended Then, at steps 3 and 4, their tasks and with probabilistic reasoning support (Hemam, 2018). knowledge/information needs were analysed. For

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Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence

example, the product manager works with customers segment, and technical area that describes the and their needs. Since the terminology used by mentioned problem domains. customers differs from that used by product Every expert is described by a competence engineers, a mapping between the customer needs profile. The expert profile contains: information and internal product requirements had to be about the expert, list of competencies, and established. professional assessment (global skill level, GSL). At steps 5 and 6 the knowledge-based workflows Global skill level is calculated based on a number of were defined, and corresponding supporting tools successfully completed tasks this expert performed, were built. his/her availability estimation for task performing, The project showed that such approach enabled estimation of how long the expert works in the implementing KM incrementally, with initiative company, qualification of the expert, and rewards coming from employees. E.g., an experimental the expert received from the manager. knowledge-based support of one workflow could be For the definition of the proofreading task it is implemented for one user role letting the users proposed to use the following structure (see Table I). estimate its efficiency and convenience. Then, The task form accessible to the expert includes the workflows reusing some knowledge of the task structure presented in the table as well as the experimental workflow can be added, etc. task discussion interface that allows proofreaders to Representatives of other roles seeing the exchange their knowledge about it. improvements of the implemented knowledge-based workflows also wish to join and actively participate Table 1: Proofreading task description. in the identification of the knowledge needed for Name Description their workflows and further turning their workflows Due Date Date when the task should be performed into the knowledge-based ones. Source Language Source language of the term Target Language Target language of the term 3.2 Dynamic Motivation Term Term to be translated Translation Translation made by translation agency Context that helps an expert to perform the An example of successfully leveraging dynamic translation. It includes the project where the motivation is automating and facilitating a Task Context translation will be used, “in sentence translation process involving company employees context”, technical area, industry segment, and etc. from different countries (A. Smirnov, Kashevnik, et al., 2019). The translation process was implemented The list of possible motivations used in the as a distributed network of language experts, and system includes two main groups of motivations: dynamic motivation was leveraged to incentivize material and non-material. Every motivation is experts (found by maximization of the global fitness specified by budget, value, and monetary benefit as function) to take part in the translation. well as it can be supported globally or only by one An example of the generated skill tree that is or several local companies. used to describe expert’s competence profile as well For example, an expert can be motivated by a as task requirements is presented in Fig. 1. The skill shopping voucher (20 EUR). In this case spent tree for the developed language experts network budget will be 20 EUR as well as monetary benefit consists of three main parts: dictionary, industry that determines the value of this present for the expert. At the same time the value of a positive recommendation to the expert’s boss could be evaluated as 10 (maximum value) but budget and monetary benefit is 0, since the company does not spend money on it. The system also provides a number of forms for managing rewards. First, a form to display the list of rewards assigned to each expert (including date/time of the assignment) and define new rewards (Fig. 2). Using this form, a manager can select an expert(s) and reward. The system shows the left monetary benefit for each expert in the current year. Figure 1: Example of skill tree.

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IC3K 2020 - 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

purposes the aspects with different formalisms have been selected. Below, each of them is described with corresponding references. The first considered aspect Product Engineering describes the task of definition of a new product and its features (Oroszi, Jung, Smirnov, Shilov, & Kashevnik, 2009), which is currently done in the NOC tool. This aspect is defined in OWL. The goal of this task is definition of new products and product families with their possible characteristics by a product engineer. During this process, the product engineer has to make sure that the defined products and characteristics are consistent (the Pellet reasoner Figure 2: Example of the new reward definition. is used for this purpose). The sample classes presented in the figure include “Product Family” 3.3 Multi-aspect Ontology (high level generalization of products), “Product Group” (lower level generalization of products, a Several projects carried out for the same production subclass of Product Family), “Product” (simple or company have led to a necessity to share modular product, a subclass of Product Group), and information and knowledge between several “Feature” (product characteristics, associated with workflows and departments that do not share the the class Product). same terminology. Besides, different tasks of The second considered aspect is Sales. It different workflows required application of different describes the task of defining and using constraints formalisms what resulted in a necessity of between product characteristics and product developing a multi-aspect ontology (A. Smirnov, combinations in an assembly. Definition of the Shilov, & Parfenov, 2019). These different views constraints is done via the CONSys tool by product can be successfully synchronized and matched with manager, and their usage is done in the CONFig tool a help of multi-aspect ontology, being a formalized by a customer or product/solution managers (A. V. instrument supporting various processes of the Smirnov, Shilov, Oroszi, Sinko, & Krebs, 2018). For considered company. For this , the ontology the purpose of constraint satisfaction technology had to cover processes, that were addressed during support, the formalism of object-oriented constraint development of the information and knowledge networks was used. The example classes from this management systems. As an illustrative example for aspect are “Product” (can be a product or a product this paper the aspects of “Product Engineering”, combination), “Parameter” (parameter of a product, “Sales”, and “Strategic Planning and Production” e.g., “mass”, “power”, that can match product that correspond to different PLM phases have been characteristic but it is not always the case), and selected. “Constraint” (mathematical constraints limiting or Development of the aspect ontologies can be calculating values of product characteristics done on the basis of any existing of depending on other characteristics). ontology development, e.g., METHONTOLOGY The third presented aspect is Strategic Planning (Fernández-López & Gómez-Pérez, 2002). Aspect and Production. The task solved in this aspect is ontology can be also built using a different definition of strategy regarding production classes. methodology since the aspects are independent. Three classes are considered: “ETO” (engineered to When developing an aspect ontology, a reuse of order, longest lead time), “ATO” (assemble to order, existing ontologies is beneficial (e.g., typical medium lead time), and “PTO” (pick to order, subproblems usually already have established shortest lead time) (A. V. Smirnov et al., 2018). ontologies) though not obligatory. Solving this task is based on pre-defined rules, and, The illustration of the developed multi-aspect hence it is defined as a set of classes and production ontology is given in Fig. 3. The ontology was built rules (“if … then …”). Based on these rules the lead based on the top-level ontology presented in times and production plants for the products are (Borsato, Estorilio, Cziulik, Ugaya, & Rozenfeld, defined. Example classes of this aspect are 2010). Earlier developed ontologies for different “Production Class” (the superclass for the above tasks have been used as the aspects (one task mentioned “ETO”, “ATO”, and “PTO” classes), corresponds to one aspect). For the illustration “Product”, and “Plant”.

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Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence

To sum up the following elements of the multi- (symbol ↔ ), meaning that two concepts from aspect PLM ontology have been defined: different aspects are equal, for the class Product are Aspects: Product Engineering, Sales, Strategic presented: Planning and Production. Product ↔ ProductProductEngineering; Local Classes (by aspect): Product ↔ ProductSales; Product Engineering aspect: Product Family, Product Group, Product, Feature. Product ↔ ProductStrategicPlanningAndProduction Sales aspect: Product, Parameter, Constraint. i.e., the concept product in different aspects has the same meaning. Strategic Planning and Production aspect: Product, Production Class, Plant, Rule. The resulting ontology made it possible to establish links between heterogeneous information Global level has the following classes: Thing, models, and, for example, changes made in the Attribute, Product, Dependency, Group, Resource. Product Engineering task can be easily reflected in To establish connections between aspects and the the Sales. global level, bridge rules have been defined. As an example, the bridge rules of bidirectional inclusion

Figure 3: Multi-aspect ontology for three aspects.

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4 CONCEPT OF The primary goal is to support cooperation of relatively short-lived (hours to several days) ad hoc HUMAN-MACHINE teams. Another limitation is that the environment is COLLECTIVE INTELLIGENCE inherently dedicated to decision support problems. ENVIRONMENT Therefore, the design is influenced by decision- making methodologies and the workflow The experience of implementing the above novel implemented by a team mostly corresponds to a techniques for CAKM in a production environment typical decision-making process. and benefits they brought have led to an idea that There are following principal actors they could be applied in a more general way to differentiated by the environment design: end-user create an environment supporting human-machine (decision-maker), participant, and service provider collective intelligence. (Fig. 4). End-user (decision-maker) uses the The problem of human-machine collaboration environment to get help in making a decision. and collective intelligence in particular have He/she describes the problem and posts it so that the attracted attention of researchers in several problem description is visible to a specified perspectives and have posed a number of important community. Participant is an active entity (human or questions (Jennings et al., 2014). a software service) working on a problem given by The proposed human-machine collective the end-user. Finally, a service provider develops, intelligence environment is based on the following integrates to the environment, and supports software foundations: services that can act as participants working on some . One of the established facts about problem given by the end user. Service provider is collaborative work on complex problems is also responsible for the deployed services, assuaging that it requires certain agent autonomy and the problem of service accountability. self-organization (Retelny, Bernstein, & Core entities involved in most of the Valentine, 2017). environment processes are the problem and the . To achieve interoperability between human team. Problem is introduced by an end-user and then and software participants, the environment is addressed by the participants’ team. The problem should support some structured representation description has a complex structure and of the discourse contents and/or task representation. First of all, it contains information, distribution. A good example is the Dicode specified by the end-user (initial statement), and also project implemented within the framework of includes all information produced by the team. So, the European FP7-ICT program (Karacapilidis during team’s activity the problem becomes more & Tampakas, 2019), proposing an ontological and more detailed. Second, to enable (at least, presentation of the argumentation process and partially) an effective interpretation by software a number of visual tools for working with a agents the problem description is represented in a formalized set of interrelated arguments. semi-structured way. In particular, machine In this research, however, an environment is built readability is achieved via using ontologies. To where heterogeneous agents (human and software) facilitate the use of ontologies for people the would be able to collectively decide on the details of environment makes it as implicit as possible by the workflow. Its distinguishing features are: relying on three techniques: . The support for self-organization (in contrast . Implicit ontological representation of the to pre-defined workflows); structure of problem information. . Flexible role-based distribution of . Natural language processing. Using advances responsibilities; in this area it is possible to infer the role of . The use of ontologies (and, in particular, some information pieces, its relationship with multi-aspect ontologies) to support human- the goal and/or some line of argumentation machine interoperability and knowledge and so on. management. . GUI-based nudging participants to encode The purpose of this environment is to implement problem structure in an ontology-compatible basic discovery, information exchange and way. organization routines to allow agents of different The environment defines two basic ontologies, nature (human and software) to collectively tackle representing different aspects of the collaborative organizational decision-making problems. decision support (Figure 5):

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Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence

problem has a team dedicated to it. Obviously, a participant may be a member of several teams, or not be a member of any team. Initial team formation is based on the same principles used in most of the crowdsourcing platforms and knowledge networks (Ahmad, Battle, Malkani, & Kamvar, 2011): each participant has a profile describing key specializations, problem- solving history, as well as the history of previous (with mutual evaluations). There is a massive list of publications why each of this components of the profile is necessary and how it affects the efficiency of teaming. The initiative in this process is mixed in the sense that a contributor should send a proposal to the end-user, consisting of one or more team members (proposal may include several participants that already have some positive experience of working together), and end-user has to collect the initial team. However, decisions of the both parties – participants and end-users – are assisted by environment. The participants may choose to receive recommendations if some problem Figure 4: Principal actors. touching his/her area of competences is posted. On the other hand end-users may explore the description . Decision-making ontology. This ontology and history of all the participants mentioned in the defines main concepts that are used during proposals. decision-making (criterion, alternative, Due to much uncertainty typically associated evaluation etc.) and interaction between them. with decision-making, it is often the case that during The ontology is based on the analysis of the work on the problem, the team understands that existing decision-making methodologies and it lacks some competencies or resources. Therefore, has been built to support majority of them. the team may create a new resource requirements, . Collaboration and coordination ontology. It that are registered in the environment and resolved defines the concepts used in distributing work in a manner, similar to the initial team formation among team members (role, responsibility, process (participants have to actively apply for the dependency etc.). positions in the team, however, both sides are The use of above ontologies allows artificial assisted by the environment mechanisms). agents to ‘understand’ the processes taking place in It should be noted, that it does not fully apply to the team and contribute to them. However, for the the software participants (services). As the ontology-based decision-support agents, there also throughput of software services is not as limited as exists a possibility to define an application ontology the throughput of humans, and the execution is and to map it to the decision-making ontology. By relatively cheap, software services are passively this process some parts of the problem situation connected to any team and by the mechanisms of the become connected to the general decision-making environment (ontology-based publish-subscribe) are terminology. watching the processes taking place with the The way problem information becomes richer problem. There are two states a software service can and grows via interaction of agents, to some extent be in w.r.t. the team: dormant and active. Initially, resemblances to (Heylighen, 2016) and all services are in the dormant state and are waiting intelligent systems based on the blackboard for specific conditions during the problem-solving. interaction principle. If these conditions defined by a particular service are Another already mentioned core entity is the met, the service tries to activate, describing its team. The team in the context of the environment is purpose and terms of use. If the team agrees that the defined as a heterogeneous group (consisting of service is useful for the problem, the service is human participants and software services) working allowed to activate (change state to active) and towards solution of a particular problem. Each become a member of the team. Otherwise, the

13 IC3K 2020 - 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Figure 5: Conceptual model of the environment. service remains dormant. Active service may also be Simultaneously two processes take place when transferred to the dormant state by a decision of the team works on a problem: solution preparation and team. Besides, the services can be accessed via a decision support (re)organization. Both of these service catalogue and activated manually by team processes are supported by mechanisms provided by members. the environment. Solution preparation is main Active services can be used by the team productive process, during which problem is members. The mechanics of their usage depends on enriched with new information and artefacts created the service’s kind. There are two main types of by team members. The result of this process is fully services: detailed description of a problem situation, weighted . Problem-solving service; alternatives and their estimated consequences – . External tool and access service. accepted by the end-user. Decision support Problem-solving service accesses the problem (re)organization process represents all the activities information described in the form of ontology and aimed at planning and organization of team work natural text, and can actively add information pieces (e.g., deciding whether additional resources are to it. An example of such service is a statistics-based required, assigning team member responsibilities, question answering service – if it detects a question setting task deadlines and identifying new tasks to about some facts (e.g., “How many people die from be solved in order to reach the goals of the whole tuberculosis in the World in one year?”) and can process). answer it in some form, it adds an answer to the Several ontologies used to describe the current question. Another example is a service that derives problem state are connected by the multi-aspect from the problem information a current set of ontology approach. Two main aspects used in alternatives and their evaluations, builds a Pareto describing the problem are decision support aspect optimal set and adds it to the problem information. and domain aspect. For example, if the environment External tool and database access services in is used in a smart tourism scenario to select a tourist their activated form only provide an access to a itinerary, then possible alternatives to consider and specified resource. For example, if the team needs evaluate are tourist itineraries. Therefore, class an epidemic database, it can activate the service that Alternative of the decision-making aspect in this grants access to this database and use it for queries. problem setting is connected with the Itinerary

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Context-aware Knowledge Management for Socio-Cyber-Physical Systems: New Trends towards Human-machine Collective Intelligence

concept of the domain aspect. Further, evaluation of in process planning and team recruiting, because the itineraries done by the team can be interpreted as reward sharing is an important aspect of process evaluations of the alternatives which is an essential definition. step in making a decision. It should be noted, that mutual mapping of the aspects is realized in the context of the problem (in location problems 5 CONCLUSIONS Alternative is mapped to geographic point, etc.). By providing a set of mechanisms (maintaining The paper discusses three modern trends in context- the problem state, discourse logics, team formation aware knowledge management for socio-cyber- processes etc.) the environment supports the physical systems. In particular: activities of the human-machine team. One . role-based organization, important specific mechanism provided by the . dynamic motivation mechanisms, and environment is soft guidance by offering situation- . multi-aspect ontology. specific cooperation patterns to the team. In this Each of these trends (or their combination) can sense, the environment plays the role similar to the be implemented in a variety of systems, improving facilitator in group decision support systems. These the effectiveness of knowledge eliciting, storing, and recommendations are based on the number of utilization, which can have a major positive impact identified collaboration patterns: generate, reduce, on the effectiveness of the whole system clarify, organize, evaluate, build consensus. These (organization). patterns form basic activities performed by members The paper also introduces a concept of human- of the team. Sometimes, current activity is defined machine collective intelligence environment, making explicitly during decision support (re)organization use of all these trends. process (e.g., there might be an alternative generation activity). In other cases, pattern can be recognized by certain structure in the ontological description of the problem state (e.g., if two or more ACKNOWLEDGEMENTS participants offer different estimations for the same alternative, then these estimations should be The research is partially funded by the Russian State reconciled during consensus building). An important Research, project 0073-2019-0005. The research on role of these patterns is that they allow to structure the human-machine collective intelligence for the team activities (w.r.t. the goal) and tie existing decision support is funded by the Russian Science methods to these activities. For example, if it has Foundation, project 19-11-00126. been recognized, that the team needs to build consensus on the set of the alternatives estimation, a number of methods for building consensus could be REFERENCES recommended. It can be seen, that the design of the environment Ahmad, S., Battle, A., Malkani, Z., & Kamvar, S. (2011). is heavily affected by the discussed trends. First of The jabberwocky programming environment for all, ontology-based context modeling and structured . Proceedings of the 24th specialization are at the core of the problem Annual ACM Symposium on User Interface Software representation. The problem situation and all the and Technology - UIST ’11, 53–64. artefacts are represented in the form of ontology, https://doi.org/10.1145/2047196.2047203 which allows to achieve interoperability between Asmae, A., Souhail, S., Moukhtar, Z. El, & Hussein, B. (2017). Using ontologies for the integration of human and software participants. Role-based information systems dedicated to product (CFAO, organization and multi-aspect ontologies are used to PLM…) and those of systems monitoring (ERP, help to reconcile different aspects of the decision MES ). 2017 International Colloquium on Logistics support (e.g., domain structure vs. process structure), and Supply Chain Management (LOGISTIQUA), 59– besides, role-based organization is used also in the 64. foundational layer, because every decision-making https://doi.org/10.1109/LOGISTIQUA.2017.7962874 process in a coarse decomposition can be viewed as Borsato, M., Estorilio, C. C. A., Cziulik, C., Ugaya, C. M. an interaction of different roles (project leader, data L., & Rozenfeld, H. (2010). An ontology building analyst, domain expert, etc.), and the environment approach for knowledge sharing in product lifecycle management. International Journal of Business and supports the definition of the team via set of roles. Systems Research, 4(3), 278. Finally, dynamic motivation mechanisms play a role https://doi.org/10.1504/IJBSR.2010.032951

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IC3K 2020 - 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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