Collective intelligence helps to address many problems that are intractable by efforts of a single person. One of the current challenges in building collective intelligence systems is to seamlessly integrate AI agents in them, transforming traditional collective intelligence to human-machine collective intelligence. The paper describes an approach to address two cornerstone problems of human-machine collective intelligence – interoperability in hybrid teams and sustaining the degree of self-organization required to address complex problems. To solve the first problem, it is proposed to leverage the expressiveness of ontologies and the technology of ontology-based smart spaces. To address the second one, a concept of assisted self-organization is proposed, including a method to assist in team-formation and a method to detect and resolve non-productive situations in the collective work.

Tourism industry has been actively developing during recent years. Tourists actively produce user-generated content such as photos and videos, use various mobile devices to support, and share their attractions review trips in social networks. This content is a basis for tourist behaviour models construction which allow to predict future desires and intentions. This work presents a tourist behaviour analysis system based on digital pattern of life concept. This concept represents tourist in IT environment and connects them with behaviour analysis instruments. The solution is based on ontological approach, which allows to use context information for the analysis and prediction. The usage of digital pattern of life allows to extract tourists behaviour components in a convenient form for analysis. The paper introduces three behaviour analysis use cases, which can be implemented by using classification, clustering, and time-series prediction machine learning techniques.

The paper describes a human-computer cloud environment supporting the deployment and functioning of human-based applications and allowing to decouple computing resource management issues (for this kind of applications) from application software. The paper focuses on two specific contributions lying in the heart of the proposed human-computer cloud environment: a) application platform, allowing to deploy human-based applications and using digital contracts to regulate the interactions between an application and its contributors, and b) the principles of ontology-based decision support service that is implemented on top of the human-computer cloud and uses task decomposition in order to deal with ad hoc tasks, algorithms for which are not described in advance.

A collective intelligence system could significantly help to improve decision making. Its advantage is that often collective decisions can be more efficient than individual ones. The paper considers the human-machine collective intelligence as shared intelligence, which is a product of the collaboration between humans and software services, their joint efforts and conformed decisions. Usually, multiple collaborators do not share a common view on the domain or problem they are working on. The paper assumes usage of multi-aspect ontologies to overcome the problem of different views thus enabling humans and intelligent software services to self-organize into a collaborative community for decision support. A methodology for development of the above multi-aspect ontologies is proposed. The major ideas behind the approach are demonstrated by an example from the smart city domain.

The concept of human-computer cloud is an adaptation of a traditional cloud computing paradigm to applications that require human expertise for performing some of the information processing operations. In these environments, it is important to maintain rich contributor profile that would allow to automatically route task requests to the contributors who will most likely complete them with high quality. On the other hand, the burden of filling and updating the profile shouldn't be entirely on the shoulders of contributors. This paper describes the profile structure of human-computer cloud environment and several mechanisms for automatically filling it and keeping up-to-date.

Tourist mobility, high risk and uncertainty in unfamiliar environments cause the importance of information and decision support for tourists. On the other side, complex nature of tourism economic sector, intertwined with other sectors, demand for decision support methodologies and tools helping destination management organizations to plan the activities for promoting and rational development of tourist destinations. Decision support systems in tourism today leverage a variety of technologies both machine-driven and human-driven. This paper applies a novel concept of human-computer cloud as a conceptual and architectural approach to building decision support systems in tourism (both from the tourist's perspective, and from destination management organization's perspective). The main role of human-computer cloud here is to provide a convenient abstraction for computational resources, not only "ordinary" (electronic/software) ones but also human-based. In the paper, we identify the list of typical decision support tasks in tourism domain, outline possible human-based extensions of traditional kinds of decision support systems, and finally discuss how some popular decision support functions in this domain can be mapped to a multi-tiered conceptual architecture of human-computer cloud services. The proposed approach is illustrated by two usage scenarios - itinerary planning and destination visitors' survey.

Recent developments in the areas of decision support, data and decision mining, on-board infotainment systems have produced valuable results that can be used to support people in different aspects of their lives. Infomobile driver support is one of the possible applications of these, what can significant increase the quality of the user experience. The paper presents a developed approach and enabling technologies for implementation of an intelligent driver support system that takes advantages provided by such modern developing technologies as context-based collaborative recommendation systems, proactive information support, smart space, and V2V communication. The developed concept is illustrated via a parking assistance scenario.

Semantic interoperability of the agents connected to smart spaces is the key requirement for proper functioning of the environment. Consequently it requires model that enables agents (hereafter knowledge processors) to perform on-the-fly translation and interpretation between the internal ontology and smart space ontology. The paper provides state of the art overview and discusses a new solution for ontology matching, translation and interpretation. In particular the study is focus on practical study of the capabilities of the delivered ontology matching solution on top of the Smart-M3 Open Source platform.

Semantic interoperability of the agents connected to smart spaces is the key requirement for proper functioning of the environment. It requires model enabling agents (hereafter knowledge processors) to perform on-the-fly translation and interpretation between the internal ontology and smart space ontology. The paper discusses our work on development of a new solution for ontology matching. In particular the study is focused on practical study of the capabilities of the delivered ontology matching solution on top of the Smart-M3 Open Source platform.

The paper addresses the problem of exploitation of smart space's facilities for organization of the resources of the smart space into a community aimed at emergency response. A smart framework is proposed that serves to integrate services provided by the resources of the smart space in context-aware manner and to organize these resources into a community aimed at joint actions on emergency response. The resources are represented by sets of Web-services. Thus, the service-based resource community comprising Web-services representing resources that provide emergency response services is organized. To coordinate Web-service interactions a service-oriented architecture is designed. An applicability of the framework is demonstrated by an example of organization of a service-based resource community for fire response actions.

An approach aimed at supporting decisions of actors of a smart environment is proposed. Resources of the smart environment are required to be self-organised to assist the actors in taking joint actions in an accident situation. The accident situation is represented by a context model that is a result of self-organisation of human, organisational, information and problem-solving resources of the smart environment. The resources are represented by Web-services. This representation allows self-organisation of the resources to be replaced with that of the Web-services. A scenario and principles of efficient self-organisation of the Web-services are discussed. The proposed approach is illustrated by an example of planning and taking joint resource actions in an operation on a traffic accident response.

The paper presents research on context-aware operational decision support for emergency management. Ontology-based context model is proposed for organization of context-aware decision support Two types of context are used: abstract context that is an ontology-based model integrating information and knowledge relevant to a decision situation, and operational context that is an instantiated abstract context within the personalization of decision support systems multi-role decision support is offered proposed ideas are illustrated through a case study of disaster relief and evacuation.

An integrated technology for intelligent support for distributed operational decision making is proposed. The conceptual framework implementing the technology consists of building an ontology-based model of the problem to be solved by the decision maker, formalization of the problem with a set of constraints, instantiation with the data values provided by environmental information sources, and interpretation it as constraint satisfaction problem. The problem is modelled by two types of contexts: abstract and operational. The integrated technology embodies advanced technologies for ontology & context management, and constraint satisfaction

The interest in the virtual supply chain decision-making problem is growing fast. The purpose of this type of enterprise is to transform incomplete information about customer orders and available resources into coordinated plans for production and replenishment of goods and services in the temporal network formed by collaborating units. The paper discusses a framework that integrates intelligent information support group-decision making and agreement modeling for a supply chain network. It offers the platform for design and modeling of diverse implementation strategies related to the type of agreement, optimization policies, decision-making strategies, organization structures, information sharing strategies and mechanisms, and business policies for the virtual supply chain.

Major trends in the knowledge-dominated economy are: (i) the change a "capital-intensive business environment" to an "intelligence-intensive business environment" and (ii) the change from from "product push" strategies to "consumer pull" management. Interest in global businesses and new forms of co-operation such as knowledge supply networks (KSNet) is growing along with increasing use of Internet-based engineering and management technologies. A profile-based approach for KSNet configuration is a method of configuring knowledge sources in order to improve knowledge efficiency for decision making over the total KSNet lifetime. This paper discusses a generic methodology of KSNet configuration in the global business information environment (BIE) based on knowledge management and multiagent/intelligent agent technologies.