Query: Fire evacuation artificial intelligence

title: Planning and learning under uncertainty
abstract: Automated Planning is the component of Artificial Intelligence that studies the computational process of synthesizing sets of actions whose execution achieves some given objectives. Research on Automated Planning has traditionally focused on solving theoretical problems in controlled environments. In such environments both, the current state of the environment and the outcome of actions, are completely known. The development of real planning applications during the last decade (planning fire extinction operations (Castillo et al. 2006) planning spacecraft activities (Nayak et al. 1999) planning emergency evacuation actions (Munoz Avila et al. 1999) has evidenced that these two assumptions are not true in many real world problems. The planning research community is aware of this issue and during the last years, it has multiplied its efforts to find new planning systems able to address these kinds of problems. All these efforts have created a new field in Automated Planning called planning under uncertainty. Nevertheless, the new systems suffer from two limitations. (1) They precise accurate action models, though the definition by hand of accurate action models is frequently very complex. (2) They present scalability problems due to the combinatorial explosion implied by the expressiveness of its action models. This thesis defines a new planning paradigm for building, in an efficient and scalable way, robust plans in domains with uncertainty though the action model is incomplete. The thesis is that, the integration of relational machine learning techniques with the planning and execution processes, allows to develop planning systems that automatically enrich their initial knowledge about the environment and therefore find more robust plans. An empirical evaluation illustrates these benefits in comparison with state of the art probabilistic planners which use static actions models.

title: Army Medical Robotics Research
abstract: Buddy treatment, first responder combat casualty care, and patient evacuation under hostile fire have compounded combat losses throughout history. Force protection of military first responders is complicated by current troop deployments for peacekeeping operations, counter terrorism, and humanitarian assistance missions that involve highly visible, politically sensitive low intensity combat in urban terrain. Research progress has been made in the areas of robotics; artificial intelligence; sensors; computer vision; mechanical, electrical and biological engineering; non invasive diagnostics; and wireless digital communications. Academic institutions have demonstrated intelligent robots that execute functions ranging from performing mechanical repairs to playing soccer. The military has significantly invested in autonomous vehicles, and other robots to support its Objective Force. By leveraging several Department of Defense funding sources the Army Telemedicine and Advanced Technology Research Center has established a growing portfolio of projects aimed at adapting, integrating, or developing new robotic technologies to locate, identify, assess, treat, and rescue battlefield casualties under hostile conditions.

title: FIRE 2019 AILA Track: Artificial Intelligence for Legal Assistance
abstract: The FIRE 2019 AILA track focused on creating a framework for evaluating different methods of retrieving relevant prior/precedent cases and statutes given a factual scenario. There were two tasks for this track: (i) Identifying relevant prior cases for a given situation (Precedent Retrieval) and (ii) Identifying most relevant statutes for a given situation (Statute Retrieval) Given a situation that can lead to filing a case, the precedent retrieval task aims at finding case documents where similar legal situations were addressed. The statute retrieval task aims at finding relevant statutes that are applicable to the situation. The factual scenarios, statutes and prior case documents used in the tasks were from the Indian Supreme Court judiciary.

title: Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework
abstract: A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule based algorithms or image based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95% The end to end delay, which comprises the transfer and decision delays, is reduced by an average of 72%

title: Early Forest Fire Detection Using Drones and Artificial Intelligence
abstract: Forest and urban fires have been and still are serious problem for many countries in the world. Currently, there are many different solutions to fight forest fires. These solutions mainly aim to mitigate the damage caused by the fires, using methods for their early detection. In this paper, we discuss a new approach for fire detection and control, in which modern technologies are used. In particular, we propose a platform that uses Unmanned Aerial Vehicles (UAVs) which constantly patrol over potentially threatened by fire areas. The UAVs also utilize the benefits from Artificial Intelligence (AI) and are equipped with on board processing capabilities. This allows them to use computer vision methods for recognition and detection of smoke or fire, based on the still images or the video input from the drone cameras. Several different scenarios for the possible use of the UAVs for forest fire detection are presented and analyse in the paper, including a solution with the use of a combination between a fixed and rotary wing drones.

title: Fire resistance evaluation through artificial intelligence A case for timber structures
abstract: With the ever growing surge of new technologies, there seems to be an ongoing inertia towards integrating automation and cognition into various engineering applications. Despite a number of initiatives, and oddly enough of all civil engineering sub disciplines, the structural fire engineering and fire safety community continues to embrace a classical stance to tackle the problem of fire. In support of growing demands to adopt performance based solutions, this paper showcases the potential of integrating Artificial Intelligence (AI) as a unique technology to assess performance and fire resistance of structures. More specifically, this study sheds light on the proper use of AI to derive temperature dependent material models for wood, together with simple expressions that can be used to trace thermo structural response of timber elements/components (i.e. floor assemblies, beams, columns, and connections) These expressions comprehend the naturally complex temperature induced physio chemical changes to timber properties, including creep and charring, and hence do not require input of such properties nor special computing software. The outcome of this study clearly shows the merit of utilizing AI to modernize fire resistance evaluation given that the developed AI models have high degree of perception (i.e. learn from past behaviors) and ability to improve their prediction capability through independent and unsupervised learning.

title: Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System
abstract: Every minute counts in an event of fire evacuation where evacuees need to make immediate routing decisions in a condition of low visibility, low environmental familiarity, and high anxiety. However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. This study aims to develop a fire evacuation routing model "Bee Fire" using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. Bee Fire is able to find the optimal fire evacuation routing solutions; thus not only the clearance time but also the total evacuation time can be reduced. Simulation shows that Bee Fire could save 10.12% clearance time and 15.41% total evacuation time; thus the congestion during the evacuation process could be effectively avoided and thus the evacuation becomes more systematic and efficient.

title: Application of Artificial Intelligence in UAV platforms for Early Forest Fire Detection
abstract: In this paper we propose a platform for early forest fire detection that uses unmanned aerial vehicle for observing given territory. The UAV platforms are equipped with specialized cameras that are either optical or thermal. The focus of the paper falls on a method for using optical camera for fire detection. We pay attention to the powerful machine learning technique that is used for the implementation of object detection in images. We have trained an object detector that can recognize smoke and fire in the images and at the end we have evaluated the performance and accuracy of the model.

title: Using Big data and artificial intelligence to solve the fire safety problem of potentially dangerous objects
abstract: In this paper, we consider a hardware software system for evaluating the level of fire safety in real time, which allows us to proactively monitor the dynamics of changes in the level of fire safety and provide the supervision authorities with remote access to the information about the level of fire safety

title: An artificial intelligence platform for design optimization and data analysis: application for fire and ventilation problems
abstract: This thesis focuses on the development of novel multi objective software platforms to assist engineering design and investigation, especially for simulation based indoor environment problems, which always involve multiple evaluation criteria. In addition,