From email, over social networks, to virtual worlds, the way people work and enjoy their free time is changing dramatically. The resulting networks are usually large in scale, involving huge numbers of interactions, and are open for the interacting entities to join or leave at will. People are often supported by software components of different complexity to which some of the corresponding tasks can be delegated. In practice, such systems cannot be built and managed based on rigid, centralised client-server architectures, but call for more flexible and decentralised means of interaction. The field of Agreement Technologies (AT) [1] envisions next-generation open distributed systems, where interactions between software components are based on the concept of agreement, and which enact two key mechanisms: a means to specify the "space" of agreements that the agents can possibly reach, and an interaction model by means of which agreements can be effectively reached. Autonomy, interaction, mobility and openness are key characteristics that are tackled from a theoretical and practical perspective. Coordination in Distributed Systems is often seen as governing the interaction among distributed processes, with the aim of "gluing together" their behaviour, so that the resulting ensemble shows desired characteristics or functionalities [2]. This notion has also been applied to Distributed Systems made up of software agents. Initially, the main purpose of such multiagent systems was to efficiently perform problem-solving in a distributed manner: both the agents and their rules of interaction were designed together, often in a top-down manner and applying a divide-and-Appl.

NASA-funded researchers applied artificial intelligence to Facebook user location data captured as two fires wrecked northern California in 2018 and gained new insight into people's evacuation movements and behaviors when disaster strikes, which could strengthen future response. The Defense Innovation Unit and Carnegie Mellon University's Software Engineering Institute are collectively crafting datasets to teach AI tools to assess buildings and structures after natural crises occur, and ultimately augment and increase the accuracy of damage estimates. These are two of many examples detailed in a new report from the Partnership for Public Service and Microsoft that explores how the maturing technology can improve disaster resilience and response, and considerations and actions governments should pursue when adopting AI to boost preparedness, recovery and relief. The report suggests agencies improve data collection and access, make proactive instead of reactive moves, collaborate with other organizations--and more. "While some governments, companies and universities have already used AI in this field, most are still in the early stages of use," officials wrote in the report.

In countries around the world, natural disasters have been much in the news. If you had a hunch such calamities were increasing, you're right. In 2017, hurricanes, earthquakes, and wildfires cost $306 billion worldwide, nearly double 2016's losses of $188 billion. Natural disasters caused by climate change, extreme weather, and aging and poorly designed infrastructure, among other risks, represent a significant risk to human life and communities. Globally, $94 trillion in new investment is needed to keep pace with population growth, with a large portion of that going toward repair of the built environment.

Evacuation planning is a critical aspect of disaster preparedness and response to minimize the number of people exposed to a threat. Controlled evacuations aim at managing the flow of evacuees as efficiently as possible and have been shown to produce significant benefits compared to self-evacuations. However, existing approaches do not capture the delays introduced by diverging and crossing evacuation routes, although evidence from actual evacuations highlights that these can lead to significant congestion. This paper introduces the concept of convergent evacuation plans to tackle this issue. It presents a MIP model to obtain optimal convergent evacuation plans which, unfortunately, does not scale to realistic instances. The paper then proposes a two-stage approach that separates the route design and the evacuation scheduling. Experimental results on a real case study show that the two-stage approach produces better primal bounds than the MIP model and is two orders of magnitude faster; It also produces dual bounds stronger than the linear relaxation of the MIP model. Finally, simulations of the evacuation demonstrate that convergent evacuation plans outperform existing approaches for realistic driver behaviors.

The creation of high fidelity scenarios for disaster simulation is a major challenge for a number of reasons. First, the maps supplied by existing map providers tend to provide only road or building shapes and do not accurately model open spaces which people use to evacuate buildings, homes, or industrial facilities. Secondly, even if some of the data about evacuation routes is available, the real-world connection points between these spaces and roads and buildings is usually not well defined unless data from buildings’ owners can be obtained. Finally, in order to augment current maps with accurate spatial data, it would require either a good set of training data for a computer vision algorithm to define evacuation routes using pictures or a significant amount of manpower to directly survey a vast area. Against this background, we develop a novel model of geospatial data creation, called CollabMap, that relies on human computation. CollabMap is a crowdsourcing tool to get users contracted via Amazon Mechanical Turk or a similar service to perform micro-tasks that involve augmenting existing maps by drawing evacuation routes, using satellite imagery from Google Maps and panoramic views from Google Street-View. We use human computation to complete tasks that are hard for a computer vision algorithm to perform or to generate training data that could be used by a computer vision algorithm to automatically define evacuation routes.