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Semantically rich depiction of the concepts for context-aware indoor routing brings appealing benefits for the safety of occupants of smart spaces in emergency evacuation. In this paper, we propose Smart Building Evacuation Ontology (SBEO3), a reusable ontology for indoor spaces, based on three different data models: user, building, and context. We provide a common representation of indoor routing and navigation, describe users? characteristics and preferences, grouping of individuals and their role in a specific context, hazards, and emergency evacuation. Among other characteristics, we consider abilities of individuals, safety and accessibility of spaces related to each person, intensity, impact, and severity of an emergency event or activity. SBEO is flexible and compatible with other ontologies of its domain, including SEAS, SSN/SOSA, SEMA4A, and empathi. We evaluate SBEO based on several metrics demonstrating that it addresses the information needs for the context-aware route recommendation system for emergency evacuation in indoor spaces. In the end, a simulation-based application example exploits SBEO using Context-Aware Emergency Evacuation Software (CAREE)4.

The Special Issue of AI Communications presents innovative and novel solutions related to modeling of pedestrian and vehicle behavior, coordination of transportation using machine learning and data-driven approaches, along with traffic problems. It includes selected, revised, and extended articles that were presented at the 11th International Workshop on Agents in Traffic and Transportation (ATT 2020), held along with the 24th European Conference on Artificial Intelligence (ECAI 2020) on September 4, 2020. This special issue may be of interest to AI researchers who are active or interested in a variety of research areas such as path planning, optimization, data analytics, machine learning, multi-agent systems, and their coordination. In all these areas, traffic and transportation form interesting and challenging application areas, especially when using mobile intelligent technologies.

International audience; To date, the research on agriculture vehicles in general and Agriculture Mobile Robots (AMRs) in particular has focused on a single vehicle (robot) and its agriculture-specific capabilities. Very little work has explored the coordination of fleets of such vehicles in the daily execution of farming tasks. This is especially the case when considering overall fleet performance, its efficiency and scalability in the context of highly automated agriculture vehicles that perform tasks throughout multiple fields potentially owned by different farmers and/or enterprises. The potential impact of automating AMR fleet coordination on commercial agriculture is immense. Major conglomerates with large and heterogeneous fleets of agriculture vehicles could operate on huge land areas without human operators to effect precision farming. In this paper, we propose the Agriculture Fleet Vehicle Routing Problem (AF-VRP) which, to the best of our knowledge, differs from any other version of the Vehicle Routing Problem studied so far. We focus on the dynamic and decentralised version of this problem applicable in environments involving multiple agriculture machinery and farm owners where concepts of fairness and equity must be considered. Such a problem combines three related problems: the dynamic assignment problem, the dynamic 3-index assignment problem and the capacitated arc routing problem. We review the state-of-the-art and categorise solution approaches as centralised, distributed and decentralised, based on the underlining decision-making context. Finally, we discuss open challenges in applying distributed and decentralised coordination approaches to this problem.

In this paper, we study the agility of evacuation routes in relation to dynamically changing unpredictable hazardous conditions in smart space networks. Infrastructure safety conditions may unpredictably change through time. Due to unpredictability, evacuees’ safety can get jeopardized at any point of the evacuation route. Thus, it is not sufficient only to find the shortest evacuation routes considering present safety conditions and evacuation flow, but we should also consider other relevant characteristics that make the evacuation routes sufficiently safe through time. With this aim, we propose two new node importance metrics: evacuation betweenness centrality and evacuation centrality, both inspired by betweenness centrality. The first metric represents the fraction of k efficient evacuation routes between all origin–destination pairs different from the given node that pass through that node, while the second represents the importance of the given node for evacuation considering the availability of alternative efficient evacuation paths (routes) from that node towards safe exits. Moreover, given a set of evacuees’ positions and safe exits, we find shortest agile evacuation routes, where by agile route we mean the ability to efficiently and safely reroute from intermediate nodes in case of unpredictable safety drops through maximizing the value of the evacuation centrality of the route’s intermediate nodes. In addition, we propose an algorithm for that problem and discuss its capability to react to the changes in safety circumstances along recommended routes.

[EN] Open and shared manufacturing factories typically dispose of a limited number of industrial robots and/or other production resources that should be properly allocated to tasks in time for an effective and efficient system performance. In particular, we deal with the dynamic capacitated production planning problem with sequence independent setup costs where quantities of products to manufacture need to be determined at consecutive periods within a given time horizon and products can be anticipated or back-ordered related to the demand period. We consider a decentralised multi-agent variant of this problem in an open factory setting with multiple owners of robots as well as different owners of the items to be produced, both considered self-interested and individually rational. Existing solution approaches to the classic constrained lot-sizing problem are centralised exact methods that require sharing of global knowledge of all the participants' private and sensitive information and are not applicable in the described multi-agent context. Therefore, we propose a computationally efficient decentralised approach based on the spillover effect that solves this NP-hard problem by distributing decisions in an intrinsically decentralised multi-agent system environment while protecting private and sensitive information. To the best of our knowledge, this is the first decentralised algorithm for the solution of the studied problem in intrinsically decentralised environments where production resources and/or products are owned by multiple stakeholders with possibly conflicting objectives. To show its efficiency, the performance of the Spillover Algorithm is benchmarked against state-of-the-art commercial solver CPLEX 12.8., This work has been partially supported by the "E-Logistics'' project funded by the French Agency for Environment and Energy Management (ADEME) and by the "AGRIFLEETS'' project ANR-20-CE10-0001 funded by the French National Research Agency (ANR) and by the STSM Grant funded by the European ICT COST Action IC1406, ``cHiPSeT'', and by the Spanish MINECO projects RTI2018-095390-BC33 (MCIU/AEI/FEDER, UE) and TIN2017-88476-C2-1-R.

Part 6: Smart Environments; International audience; Mass evacuation of people in buildings during emergencies is still a burning question regarding safety and reliability. Due to lack of information, crowd prefers to evacuate in the form of random groups. This random grouping increases the possibility of panic among evacuees due to human behavioral factors like herding and stampeding. As a result, congestion may occur resulting in unnecessary casualties. For this purpose, we propose a multi-agent evacuation architecture, that not only collects all the information from evacuees and events occurring in the building on a real-time basis but also provides the evacuation routes to evacuees. In this paper, we discuss a group formation module of our proposed architecture and use an example as a test case to check the functionality of our proposed group formation approach.

Evacuation in buildings during emergency situations is crucial to the safety of people, therefore a pragmatic response plan is desirable. Due to the lack of awareness in buildings, either occupants have to wait for instructions from the administration or to start following each other to find the best evacuation route for them on the basis of hit and trial method. In this regard, we present a context-aware smart architecture for evacuation that provides real-time evacuation routes to occupants with respect to their characteristics. We also put forward a two-phase group evacuation technique in which people evacuate in the form of groups under the supervision of experts so-called group leaders. The first phase handles the assembly of evacuees at their allotted collection points and in the second phase evacuees follow their group leaders to safe points. Group leaders are equipped with hand-held devices having live information of building, routes and their group members. A use case is also discussed in the paper as an application of the proposed technique.