Intelligent Agents for Disaster Management

ALADDIN [1] is a multi-disciplinary project that is developing novel techniques, architectures, and mechanisms for multi-agent systems in uncertain and dynamic environments. The application focus of the project is disaster management. Research within a number of themes is being pursued and this is considering different aspects of the interaction between autonomous agents and the decentralised system architectures that support those interactions. The aim of the research is to contribute to building more robust multi-agent systems for future applications in disaster management and other similar domains

System Issues in Multi-agent Simulation of Large Crowds

Crowd simulation is a complex and challenging domain. Crowds demonstrate many complex behaviours and are consequently difficult to model for realistic simulation systems. Analyzing crowd dynamics has been an active area of research and efforts have been made to develop models to explain crowd behaviour. In this paper we describe an agent based simulation of crowds, based on a continuous field force model. Our simulation can handle movement of crowds over complex terrains and we have been able to simulate scenarios like clogging of exits during emergency evacuation situations. The focus of this paper, however, is on the scalability issues for such a multi-agent based crowd simulation system. We believe that scalability is an important criterion for rescue simulation systems. To realistically model a disaster scenario for a large city, the system should ideally scale up to accommodate hundreds of thousands of agents. We discuss the attempts made so far to meet this challenge, and try to identify the architectural and system constraints that limit scalability. Thereafter we propose a novel technique which could be used to richly simulate huge crowds

RoboCup Rescue Simulation Machine Learning Workshop

The Rescue Simulation League is the challenge to learn an optimal response for a team of robots that have to mitigate the effects of a disaster. To operate optimal as teams, several aspects have to be solved at once, such as team formation, task allocation and route planning. This is a hard problem, which could be quite overwhelming for newcomer teams. The proposal is to create a workshop, which demonstrates how each of the aspects could be solved with standard machine learning algorithms, as available in MathWorks' Statistics and Machine Learning Toolbox™

Using the <I-N-C-A> Constraint Model as a Shared Representation of Intentions for Emergency Response

The aim of this paper is to describe the I-X system with its underlying representation: . The latter can be seen as a description of an agent’s intentions, which can be shared and communicated amongst multiple I-X agents to coordinate activities in an emergency response scenario. In general, an object describes the product of a synthesis task. In the multi-agent context it can be used to describe the intentions of an agent, although it also includes elements of beliefs about the world and goals to be achieved, thus showing a close relationship with the BDI agent model which we will explore in this paper. From a user’s perspective, I-X Process Panels can be used as an intelligent to-do list that assists emergency responders in applying pre-defined standard operating procedures in different types of emergencies. In particular, multiple instances of the I-X Process Panels can be used as a distributed system to coordinate the efforts of independent emergency responders as well as responders within the same organization. Furthermore, it can be used as an agent wrapper for other software systems such as webservices to integrate these into the emergency response team as virtual members. At the heart of I-X is a Hierarchical Task Network (HTN) planner that can be used to synthesize courses of action automatically or explore alternative options manually

Using the &lt;I-N-C-A&gt; Constraint Model as a Shared Representation of Intentions for Emergency Response

The aim of this paper is to describe the I-X system with its underlying representation: &lt;I-N-C-A&gt;. The latter can be seen as a description of an agent’s intentions, which can be shared and communicated amongst multiple I-X agents to coordinate activities in an emergency response scenario. In general, an &lt;I-N-C-A&gt; object describes the product of a synthesis task. In the multi-agent context it can be used to describe the intentions of an agent, although it also includes elements of beliefs about the world and goals to be achieved, thus showing a close relationship with the BDI agent model which we will explore in this paper. From a user’s perspective, I-X Process Panels can be used as an intelligent to-do list that assists emergency responders in applying pre-defined standard operating procedures in different types of emergencies. In particular, multiple instances of the I-X Process Panels can be used as a distributed system to coordinate the efforts of independent emergency responders as well as responders within the same organization. Furthermore, it can be used as an agent wrapper for other software systems such as web-services to integrate these into the emergency response team as virtual members. At the heart of I-X is a Hierarchical Task Network (HTN) planner that can be used to synthesize courses of action automatically or explore alternative options manually

The robot programming network

The Robot Programming Network (RPN) is an initiative for creating a network of robotics education laboratories with remote programming capabilities. It consists of both online open course materials and online servers that are ready to execute and test the programs written by remote students. Online materials include introductory course modules on robot programming, mobile robotics and humanoids, aimed to learn from basic concepts in science, technology, engineering, and mathematics (STEM) to more advanced programming skills. The students have access to the online server hosts, where they submit and run their programming code on the fly. The hosts run a variety of robot simulation environments, and access to real robots can also be granted, upon successful achievement of the course modules. The learning materials provide step-by-step guidance for solving problems with increasing level of dif- ficulty. Skill tests and challenges are given for checking the success, and online competitions are scheduled for additional motivation and fun. Use of standard robotics middleware (ROS) allows the system to be extended to a large number of robot platforms, and connected to other existing tele-laboratories for building a large social network for online teaching of robotics.Support of IEEE RAS through the CEMRA program (Creation of Educational Material for Robotics and Automation) is gratefully acknowledged. This paper describes research done at the Robotic Intelligence Laboratory. Support for this laboratory is provided in part by Ministerio de Economia y Competitividad (DPI2011-27846), by Generalitat Valenciana (PROMETEOII/2014/028) and by Universitat Jaume I (P1-1B2011-54)

Intelligent control interfaces developed on versatile portable intelligent platform in order to improving autonomous navigation robots performances

The paper presents Intelligent Control Interfaces (ICIs) for real-time control for terrestrial mobile robots or unmanned aerial robots in order to improve the navigation performances. Intelligent control interfaces using advanced control strategies adapted to robot environment are presented, implemented through IT & C techniques with fast processing and real-time communications in order to develop a versatile, intelligent and portable VIPRO Platform with behavior of e-learning platform, which allows achievement inter-academic research networks and building new intelligent vectors robots. Implementation of ICIs laws in the intelligent real time control interfaces depends on the particular circumstances of the characteristics model used and the exact definition of optimization problem. The results led to the development of the ICI interfaces through image analysis using Images Operation Sampling & Quantization (IOSQ)