Situation aware intelligent reasoning during disaster situation in smart cities.

Investigating prior methodologies, it has come to our knowledge that in smart cities, a disaster management system needs an autonomous reasoning mechanism to efficiently enhance the situation awareness of disaster sites and reduce its after-effects. Disasters are unavoidable events that occur at anytime and anywhere. Timely response to hazardous situations can save countless lives. Therefore, this paper introduces a multi-agent system (MAS) with a situation-awareness method utilizing NB-IoT, cyan industrial Internet of things (IIOT), and edge intelligence to have efficient energy, optimistic planning, range flexibility, and handle the situation promptly. We introduce the belief-desire-intention (BDI) reasoning mechanism in a MAS to enhance the ability to have disaster information when an event occurs and perform an intelligent reasoning mechanism to act efficiently in a dynamic environment. Moreover, we illustrate the framework using a case study to determine the working of the proposed system. We develop ontology and a prototype model to demonstrate the scalability of our proposed system.

A Multi-Agent Formalism Based on Contextual Defeasible Logic for Healthcare Systems.

In the last decade, smart computing has garnered much attention, particularly in ubiquitous environments, thus increasing the ease of everyday human life. Users can dynamically interact with the systems using different modalities in a smart computing environment. The literature discussed multiple mechanisms to enhance the modalities for communication using different knowledge sources. Among others, Multi-context System (MCS) has been proven quite significant to interlink various context domains dynamically to a distributed environment. MCS is a collection of different contexts (independent knowledge sources), and every context contains its own set of defined rules and facts and inference systems. These contexts are interlinked via bridge rules. However, the interaction among knowledge sources could have the consequences such as bringing out inconsistent results. These issues may report situations such as the system being unable to reach a conclusion or communication in different contexts becoming asynchronous. There is a need for a suitable framework to resolve inconsistencies. In this article, we provide a framework based on contextual defeasible reasoning and a formalism of multi-agent environment is to handle the issue of inconsistent information in MCS. Additionally, in this work, a prototypal simulation is designed using a simulation tool called NetLogo, and a formalism about a Parkinson's disease patient's case study is also developed. Both of these show the validity of the framework.