The Spatial Knowledge Graphs (SKG) are experiencing growing adoption as a means to model real-world entities, proving especially invaluable in domains like crisis management and urban planning. Considering that RDF specifications offer limited support for effectively managing spatial information, it's common practice to include text-based serializations of geometrical features, such as polygons and lines, as string literals in knowledge graphs. Consequently, Spatial Knowledge Graphs (SKGs) often rely on geo-enabled RDF Stores capable of parsing, interpreting, and indexing such serializations. In this paper, we leverage grid cells as the foundational element of SKGs and demonstrate how efficiently the spatial characteristics of real-world entities and their attributes can be encoded within knowledge graphs. Furthermore, we introduce a novel methodology for representing street networks in knowledge graphs, diverging from the conventional practice of individually capturing each street segment. Instead, our approach is based on tessellating the street network using grid cells and creating a simplified representation that could be utilized for various routing and navigation tasks, solely relying on RDF specifications.

Emergencies and critical incidents often unfold rapidly, necessitating a swift and effective response. In this research, we introduce a novel approach to identify and classify emergency situations from social media posts and direct emergency messages using an open source Large Language Model, LLAMA2. The goal is to harness the power of natural language processing and machine learning to assist public safety telecommunicators and huge crowds during countrywide emergencies. Our research focuses on developing a language model that can understand users describe their situation in the 911 call, enabling LLAMA2 to analyze the content and offer relevant instructions to the telecommunicator, while also creating workflows to notify government agencies with the caller's information when necessary. Another benefit this language model provides is its ability to assist people during a significant emergency incident when the 911 system is overwhelmed, by assisting the users with simple instructions and informing authorities with their location and emergency information.

The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime, people must repurpose the materials and processes available to make shelter, distribute food, etc. Repurposing is equally important in professional life (e.g. clinicians often repurpose medicines off-license) and in addressing societal challenges (e.g. finding new roles for waste products,). Despite the importance of repurposing, the topic has received little academic attention. By considering examples from a variety of domains such as every-day activities, drug repurposing and natural disasters, we identify some principle characteristics of the process and describe some technical challenges that would be involved in modelling and simulating it. We consider cases of both substitution, i.e. finding an alternative for a missing resource, and exploitation, i.e. identifying a new role for an existing resource. We argue that these ideas could be developed into general formal theory of repurposing, and that this could then lead to the development of AI methods based on commonsense reasoning, argumentation, ontological reasoning, and various machine learning methods, to develop tools to support repurposing in practice.

Both the private and public sectors need to embrace data science as part of developing policies and procedures to better manage a crisis.

Today is the era of intelligence in machines. With the advances in Artificial Intelligence, machines have started to impersonate different human traits, a chatbot is the next big thing in the domain of conversational services. A chatbot is a virtual person who is capable to carry out a natural conversation with people. They can include skills that enable them to converse with the humans in audio, visual, or textual formats. Artificial intelligence conversational entities, also called chatbots, conversational agents, or dialogue system, are an excellent example of such machines.

The coordination and cooperation of all the stakeholders involved is a decisive point for the control and the resolution of problems. In the insecurity events, the resolution should refer to a plan that defines a general framework of the procedures to be undertaken and the instructions to be complied with; also, a more precise process must be defined by the actors to deal with the case represented by the particular problem of the current situation. Indeed, this process has to cope with a dynamic, unstable and unpredictable environment, due to the heterogeneity and multiplicity of stakeholders, and finally due to their possible geographical distribution. In this article, we will present the first steps of validation of a collaborative decision-making approach in the context of crisis situations such as road accidents. This approach is based on ontologies and multi-agent systems.

The key question on the mind of policymakers now is whether Artificial Intelligence would be able to deliver on its promises instead of entering another season of scepticism and stagnation. The quest for Artificial Intelligence (AI) has travelled through multiple "seasons of hope and despair" since the 1950s. The introduction of neural networks and deep learning in late 1990s has generated a new wave of interest in AI and growing optimism in the possibility of applying it to a wide range of activities, including diplomacy. The key question on the mind of policymakers now is whether AI would be able to deliver on its promises instead of entering another season of scepticism and stagnation. This paper evaluates the potential of IA to provide reliable assistance in areas of diplomatic interest such as in consular services, crisis management, public diplomacy and international negotiations, as well as the ratio between costs and contributions of AI applications to diplomatic work.

Note from the CPD Blog Manager: A previous version of this piece was originally published by the Elcano Royal Institute. Riding the waves of growing interest about artificial intelligence (AI) in international relations (IR) and security studies, the debate about the role of AI in diplomacy is also gaining momentum, although academic discussions are progressing rather slowly, without a clear analytical focus. The key question on the mind of policymakers at the moment is whether AI would be able to deliver on its promises instead of entering another season of skepticism and stagnation. If AI would be able to demonstrate value in a consistent manner by providing reliable assistance in areas of diplomatic interest such as in consular services, crisis management, public diplomacy and international negotiations, as suggested above, then the future of AI in diplomacy should look bright. If, on the other hand, the ratio between costs and contributions of AI applications to diplomatic work would stay high, then the appetite for AI integration would likely decline.

The key question on the mind of policymakers now is whether Artificial Intelligence would be able to deliver on its promises instead of entering another season of scepticism and stagnation. The quest for Artificial Intelligence (AI) has travelled through multiple "seasons of hope and despair" since the 1950s. The introduction of neural networks and deep learning in late 1990s has generated a new wave of interest in AI and growing optimism in the possibility of applying it to a wide range of activities, including diplomacy. The key question on the mind of policymakers now is whether AI would be able to deliver on its promises instead of entering another season of scepticism and stagnation. This paper evaluates the potential of IA to provide reliable assistance in areas of diplomatic interest such as in consular services, crisis management, public diplomacy and international negotiations, as well as the ratio between costs and contributions of AI applications to diplomatic work.

This paper presents a training game intended to train crisis management teams to negotiate collaboratively in order to reach the group goal in the best way possible. The importance of the group goal in comparison to their individual goals is touched upon as well, as are various conflicts that can occur during such a negotiation. The game, which is implemented in the Blocks World 4 Teams environment, gives a team a specific scenario and allows them to negotiate a plan of action. This plan of action is then performed by agents, after which the team members will be debriefed on their performance. An experiment, containing multiple rounds to test the effect the game has on participants, is planned in the near future.