Development of Autonomous Multi Agent System for Multi-Hazard Risk Assessment

Developing autonomous multi agent systems are to be considered anadvancement of multi agent systems can be applied in both the physical and the logicalworld. Constructions of multi hazard risk assessment using spatial data for disastermanagement have a problem of effective communication because of implicitknowledge. Risk assessment is the determination of quantitative or qualitative value ofrisk related to a concrete situation and a recognized hazard. Multi hazard riskassessment requires commonsense knowledge related with the hazard. This complicatesthe effective communication of data to the user in real-time machine processing insupport of disaster management. The aim of the approach is to identify the influences ofdeveloping autonomous multi agent systems for risk assesmnet in disaster management.The objectives should a) contribute to a better understanding of the transformationprocesses in commonsense knowledge related with a hazard and b) provide effectivecommunication of data to the user in real-time machine processing in support of disastermanagement.In this paper we present a metodology to modeling commonsenseknowledge in Multi hazard risk assessment using Autonomous multi agent system. Thisgives three-phase knowledge modeling approach for modeling commonsenseknowledge in, which enables holistic approach for disaster management. At the initialstage autonomous agents are initialized to convert commonsense knowledge based onmulti hazards into a questionnaire. Removing dependencies among the questions aremodeled using principal component analysis. Classification of the knowledge isprocessed through fuzzy logic agent, which is constructed on the basis of principalcomponents. Further explanations for classified knowledge are derived by agent basedon expert system technology. We have implemented the system using FLEX expertsystem shell, SPSS, XML and VB. This paper describes one such approach usingclassification of human constituents in Ayurvedic medicine. Evaluation of the systemhas shown 77% accuracy.Key words: Autonomous multi agent systems, Multi hazards, risk assessment,commonsense knowledge, Fuzzy logi

HAC-ER: a disaster response system based on human-agent collectives

This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emergency responders by enabling humans and agents, using state-of-the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC-ER utilises crowdsourcing combined with machine learning to extract situational awareness information from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations

Social Network Model for Accessing and Sharing Expertise During Disasters

Whether human-made or natural disasters, both could typically followed by chaos, this often results from an inadequate overall response. Preparedness is the best response to emergencies, and a multi-agent-based approach to coordination decision support systems often plays a significant role in disaster management and response. Using an integrated approach to facilitate coordination is considered important in dealing with disasters. Often, there are different kinds of coordination, such as physical coordination between emergent agencies or local agencies, and administrative coordination. While traditional disaster management studies have focused on coordination of managerial or government policy approaches, this study focuses on the direct local-based advice network used by emergency personnel (such as managers and volunteers) to understand how the properties of such human networks affect the ability to access and share expertise during a disaster incident in order to ensure prompt and accurate decisions. The key motivating question guiding this research is: how can the multi-level study of properties of social networks at network, actor and tie level help us understand the coordination that enables expertise access and sharing during disasters? Moreover, this study also asks: To what degree is this relationship associated with expertise coordination in a negative or positive manner? How are centralisation and efficiency in an individual’s social network associated with coordination? Do network constraints and tie strength in an individual’s social network negatively or positively affect coordination? This study uses novel theoretical approaches to suggest an empirical-based framework and methodology for exploring the relationship between the properties of social networks and coordination of expertise during disasters

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

Research Directions in Information Systems for Humanitarian Logistics

This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the article’s research methodology and research contribution. Finally, we identify potential future research directions

OpenKnowledge at work: exploring centralized and decentralized information gathering in emergency contexts

Real-world experience teaches us that to manage emergencies, efficient crisis response coordination is crucial; ICT infrastructures are effective in supporting the people involved in such contexts, by supporting effective ways of interaction. They also should provide innovative means of communication and information management. At present, centralized architectures are mostly used for this purpose; however, alternative infrastructures based on the use of distributed information sources, are currently being explored, studied and analyzed. This paper aims at investigating the capability of a novel approach (developed within the European project OpenKnowledge1) to support centralized as well as decentralized architectures for information gathering. For this purpose we developed an agent-based e-Response simulation environment fully integrated with the OpenKnowledge infrastructure and through which existing emergency plans are modelled and simulated. Preliminary results show the OpenKnowledge capability of supporting the two afore-mentioned architectures and, under ideal assumptions, a comparable performance in both cases