Periodic Reporting for period 1 - LAW-GAME (An Interactive, Collaborative Digital Gamification Approach to Effective Experiential Training and Prediction of Criminal Actions)
Período documentado: 2021-09-01 hasta 2023-02-28
Organized crime and terrorist organisations are often at the forefront of technological innovation in planning, executing and concealing their criminal activities and the revenues stemming from them. There is a growing need to focus on technology opportunities provided by new and emerging technologies in advancing Law Enforcement Agencies (LEAs) in tackling criminal activities supported by advanced technologies, but also to support professional development and build LEAs capabilities and skills, allowing them to perform their daily operations effectively.
As new technologies are introduced to the law enforcement field, departments must ensure their officers are well trained and well versed on how to use these new tools effectively. New automated technologies such as artificial intelligence and predictive analytics are being used by law enforcement to both improve efficiency and enhance safety.
It is a fact that many training and educational programs are out of date and insufficient in preparing officers for the real-world challenges they will face. Not to mention the lack of focus on core skills related to leadership such as communication competencies, conflict resolution and management.
LAW-GAME aims to develop to the law enforcement domain a new form of training system combining Virtual and Constructive simulation technologies for criminal actions, terrorism-attack prevention, police interview and car accident analysis training exercises. Realism, engagement, interaction, immersion and active participation are the core principles of this proposed virtual-reality gaming approach. LAW-GAME aims at improving the overall training process in terms of effectiveness, time, budget, location and disruption. LAW-GAME artificial Intelligence technologies will reproduce actual terrorist incidents, and analyze crime and car accident scenes improving police operations through better preparation of the officers in the realistic scenarios and work as new tools in police arsenals.The vision of the LAW-GAME team is that the proposed system will be adopted as the pan-European training platform for a large number of users in the security domain.
As new technologies are introduced to the law enforcement field, departments must ensure their officers are well trained and well versed on how to use these new tools effectively. New automated technologies such as artificial intelligence and predictive analytics are being used by law enforcement to both improve efficiency and enhance safety.
It is a fact that many training and educational programs are out of date and insufficient in preparing officers for the real-world challenges they will face. Not to mention the lack of focus on core skills related to leadership such as communication competencies, conflict resolution and management.
LAW-GAME aims to develop to the law enforcement domain a new form of training system combining Virtual and Constructive simulation technologies for criminal actions, terrorism-attack prevention, police interview and car accident analysis training exercises. Realism, engagement, interaction, immersion and active participation are the core principles of this proposed virtual-reality gaming approach. LAW-GAME aims at improving the overall training process in terms of effectiveness, time, budget, location and disruption. LAW-GAME artificial Intelligence technologies will reproduce actual terrorist incidents, and analyze crime and car accident scenes improving police operations through better preparation of the officers in the realistic scenarios and work as new tools in police arsenals.The vision of the LAW-GAME team is that the proposed system will be adopted as the pan-European training platform for a large number of users in the security domain.
During this period of the LAW-GAME project (M1-M18), the consortium has managed to extract the use case requirements and the system specifications and to design, and follow up, a common design methodology in order to ensure the requirement elicitation process that has been used, and will be used, along the project, considering the limitations for the organisation of physical workshops with LAW-GAME end-users, due to COVID pandemic, mainly in the first 12 months .
Use cases initial designs, each one with each own context and goals, have been completed and important elements for the future LAW-GAME work (such as user roles, data exchange and data flows needs, functional and non-functional requirements, technical components, processes, etc.) have been identified. All these elements have been taken into account to design the system architecture and its main technical components, including the human emotion, stance modelling modules, facial expressions. Also the first prototype of the dialogue engine able to enable smooth interactions between human players and non-player characters (NPCs), and the AI narrator which provides context-aware hints and instructions to guide players through the game have been developed. Finally, the first version of the scenario configurator which generates a wide range of mini-game scenarios, each designed to challenge players is available and documented.
In addition to the mini-game scenarios, the crime scene reconstruction, object and human detection, and ballistics analysis modules form a standalone tool for crime scene analysis are available.
Regarding the pilots and use cases during this period different tasks have started to define the LAW-GAME knowledge base, how to represent data (inside a common model) for the future big data engine, several datasets have been considered (data and data types) and the initial preliminary work around the data categorisation has been done.
The early-stage design of the LAW-GAME infrastructure will support the fast development, during the next periods, the back-end, the front-end and the different services needed. These designs will model these elements inside the LAW-GAME infrastructure.
In parallel with the aforementioned activities, LAW-GAME planned a number of raising awareness actions and exploitation roadmap area activities, and is participating in CC-Driver Cluster.
During this period, the Advisory Board (AB) has been created, and it has set up everything that has to do with the technological, innovation and ethics requirements inside the project. Further to this, DPOs from the different partners have been identified. The AB is working from this first period on to ensure compliance of every requirement and need according to the procedures and criteria defined.
Use cases initial designs, each one with each own context and goals, have been completed and important elements for the future LAW-GAME work (such as user roles, data exchange and data flows needs, functional and non-functional requirements, technical components, processes, etc.) have been identified. All these elements have been taken into account to design the system architecture and its main technical components, including the human emotion, stance modelling modules, facial expressions. Also the first prototype of the dialogue engine able to enable smooth interactions between human players and non-player characters (NPCs), and the AI narrator which provides context-aware hints and instructions to guide players through the game have been developed. Finally, the first version of the scenario configurator which generates a wide range of mini-game scenarios, each designed to challenge players is available and documented.
In addition to the mini-game scenarios, the crime scene reconstruction, object and human detection, and ballistics analysis modules form a standalone tool for crime scene analysis are available.
Regarding the pilots and use cases during this period different tasks have started to define the LAW-GAME knowledge base, how to represent data (inside a common model) for the future big data engine, several datasets have been considered (data and data types) and the initial preliminary work around the data categorisation has been done.
The early-stage design of the LAW-GAME infrastructure will support the fast development, during the next periods, the back-end, the front-end and the different services needed. These designs will model these elements inside the LAW-GAME infrastructure.
In parallel with the aforementioned activities, LAW-GAME planned a number of raising awareness actions and exploitation roadmap area activities, and is participating in CC-Driver Cluster.
During this period, the Advisory Board (AB) has been created, and it has set up everything that has to do with the technological, innovation and ethics requirements inside the project. Further to this, DPOs from the different partners have been identified. The AB is working from this first period on to ensure compliance of every requirement and need according to the procedures and criteria defined.
LAW-GAME integrates a series of state-of-the-art technological achievements.
The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations that police officers’ face.
LAW-GAME project is an innovative training platform that combines state-of-the-art technology with immersive gameplay to provide a comprehensive training experience for professionals in the field of law enforcement.
The project is divided into various work packages, each tasked with developing specific modules that will be integrated into the final platform. These modules include human emotion modelling, stance modelling, dialogue engine, AI narrator, scenario configurator, crime scene reconstruction, object and human detection, and ballistics analysis.
The human emotion and stance modelling modules utilise physiological signals, facial expressions, and game video, audio and player video feeds to accurately predict the players’ emotional state and stance, respectively. The dialogue engine enables smooth interactions between human players and non-player characters (NPCs), while the AI narrator provides context-aware hints and instructions to guide players through the game. Finally, the scenario configurator generates a wide range of mini-game scenarios, each designed to challenge players.
Building upon an in-depth analysis of police officers’ learning needs and inspired by a multitude of disciplines, LAW-GAME will develop an advanced learning experience, embedded into three comprehensive “mini games” dedicated to train police officers.
The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations that police officers’ face.
LAW-GAME maximises the returns from EC’s investment in science and technology, addresses and provide solutions for border control and management planning and optimization thanks and will allow creating new job opportunities that will have an economic impact on the citizens’ life.
The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations that police officers’ face.
LAW-GAME project is an innovative training platform that combines state-of-the-art technology with immersive gameplay to provide a comprehensive training experience for professionals in the field of law enforcement.
The project is divided into various work packages, each tasked with developing specific modules that will be integrated into the final platform. These modules include human emotion modelling, stance modelling, dialogue engine, AI narrator, scenario configurator, crime scene reconstruction, object and human detection, and ballistics analysis.
The human emotion and stance modelling modules utilise physiological signals, facial expressions, and game video, audio and player video feeds to accurately predict the players’ emotional state and stance, respectively. The dialogue engine enables smooth interactions between human players and non-player characters (NPCs), while the AI narrator provides context-aware hints and instructions to guide players through the game. Finally, the scenario configurator generates a wide range of mini-game scenarios, each designed to challenge players.
Building upon an in-depth analysis of police officers’ learning needs and inspired by a multitude of disciplines, LAW-GAME will develop an advanced learning experience, embedded into three comprehensive “mini games” dedicated to train police officers.
The proposed learning experience focuses on the development of the key competences needed for successfully operating in diverse and distributed teams, as required by several cross-organisational and international cooperation situations that police officers’ face.
LAW-GAME maximises the returns from EC’s investment in science and technology, addresses and provide solutions for border control and management planning and optimization thanks and will allow creating new job opportunities that will have an economic impact on the citizens’ life.