Biblio

Blockchain has emerged as a leading technological innovation because of its indisputable safety and services in a distributed setup. Applications of blockchain are rising covering varied fields such as financial transactions, supply chains, maintenance of land records, etc. Supply chain management is a potential area that can immensely benefit from blockchain technology (BCT) along with smart contracts, making supply chain operations more reliable, safer, and trustworthy for all its stakeholders. However, there are numerous challenges such as scalability, coordination, and safety-related issues which are yet to be resolved. Multi-agent systems (MAS) offer a completely new dimension for scalability, cooperation, and coordination in distributed culture. MAS consists of a collection of automated agents who can perform a specific task intelligently in a distributed environment. In this work, an attempt has been made to develop a framework for implementing a multi-agent system for a large-scale product manufacturing supply chain with blockchain technology wherein the agents communicate with each other to monitor and organize supply chain operations. This framework eliminates many of the weaknesses of supply chain management systems. The overall goal is to enhance the performance of SCM in terms of transparency, traceability, trustworthiness, and resilience by using MAS and BCT.

Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.

This paper proposes a basic strategy for Botnet Defense System (BDS). BDS is a cybersecurity system that utilizes white-hat botnets to defend IoT systems against malicious botnets. Once a BDS detects a malicious botnet, it launches white-hat worms in order to drive out the malicious botnet. The proposed strategy aims at the proper use of the worms based on the worms' capability such as lifespan and secondary infectivity. If the worms have high secondary infectivity or a long lifespan, the BDS only has to launch a few worms. Otherwise, it should launch as many worms as possible. The effectiveness of the strategy was confirmed through the simulation evaluation using agent-oriented Petri nets.

In this paper, an agent-based cross-layer secure service discovery scheme has been presented. Service discovery in MANET is a critical task and it presents numerous security challenges. These threats can compromise the availability, privacy and integrity of service discovery process and infrastructure. This paper highlights various security challenges prevalent to service discovery in MANET. Then, in order to address these security challenges, the paper proposes a cross-layer, agent based secure service discovery scheme for MANET based on deep neural network. The software agents will monitor the intrusive activities in the network based on an Intrusion Detection System (IDS). The service discovery operation is performed based on periodic dissemination of service, routing and security information. The QoS provisioning is achieved by encapsulating QoS information in the periodic advertisements done by service providers. The proposed approach has been implemented in JIST/ SWANS simulator. The results show that proposed approach provides improved security, scalability, latency, packet delivery ratio and service discovery success ratio, for various simulation scenarios.

We present Diana, an embodied agent who is aware of her own virtual space and the physical space around her. Using video and depth sensors, Diana attends to the user's gestures, body language, gaze and (soon) facial expressions as well as their words. Diana also gestures and emotes in addition to speaking, and exists in a 3D virtual world that the user can see. This produces symmetric and shared perception, in the sense that Diana can see the user, the user can see Diana, and both can see the virtual world. The result is an embodied agent that begins to develop the conceit that the user is interacting with a peer rather than a program.

The advances in natural language processing and the wide use of social networks have boosted the proliferation of chatbots. These are software services typically embedded within a social network, and which can be addressed using conversation through natural language. Many chatbots exist with different purposes, e.g., to book all kind of services, to automate software engineering tasks, or for customer support. In previous work, we proposed the use of chatbots for domain-specific modelling within social networks. In this short paper, we report on the needs for flexible modelling required by modelling using conversation. In particular, we propose a process of meta-model relaxation to make modelling more flexible, followed by correction steps to make the model conforming to its meta-model. The paper shows how this process is integrated within our conversational modelling framework, and illustrates the approach with an example.

Verbal and non-verbal behaviors that we use in order to effectively communicate with other people are vital for our success in our daily lives. Despite the importance of social skills, creating standardized methods for training them and supporting their training is challenging. Information and Communications Technology (ICT) may have a good potential to support social and emotional learning (SEL) through virtual social demonstration games. This paper presents initial work involving the design of a pedagogical scenario to facilitate teaching of socially appropriate and inappropriate behaviors when entering and standing in a small group of people, a common occurrence in collaborative social situations. This is achieved through the use of virtual characters and, initially, virtual reality (VR) environments for supporting situated learning in multiple contexts. We describe work done thus far on the demonstrator scenario and anticipated potentials, pitfalls and challenges involved in the approach.

Two systems pursuing their own goals in a shared world can interact in ways that are not so explicit - such that the presence of another system alone can interfere with how one is able to achieve its own goals. Drawing inspiration from human psychology and the theory of social action, we propose the notion of employing social action in socially situated agents as a means of alleviating interference in interacting systems. Here we demonstrate that these specific issues of behavioural and evolutionary instability caused by the unintended consequences of interactions can be addressed with agents capable of a fusion of goal-rationality and traditional action, resulting in a stable society capable of achieving goals during the course of evolution.

Hiring seasonal workers in call centers to provide customer service is a common practice in B2C companies. The quality of service delivered by both contracting and employee customer service agents depends heavily on the domain knowledge available to them. When observing the internal group messaging channels used by agents, we found that similar questions are often asked repetitively by different agents, especially from less experienced ones. The goal of our work is to leverage the promising advances in conversational AI to provide a chatbot-like mechanism for assisting agents in promptly resolving a customer's issue. In this paper, we develop a neural-based conversational solution that employs BiLSTM with attention mechanism and demonstrate how our system boosts the effectiveness of customer support agents. In addition, we discuss the design principles and the necessary considerations for our system. We then demonstrate how our system, named "Isa" (Intuit Smart Agent), can help customer service agents provide a high-quality customer experience by reducing customer wait time and by applying the knowledge accumulated from customer interactions in future applications.

We investigate a method of building a closed domain intelligent conversational agent using deep neural networks. A conversational agent is a dialog system intended to converse with a human, with a coherent structure. Our conversational agent uses a retrieval based model that identifies the intent of the input user query and maps it to a knowledge base to return appropriate results. Human conversations are based on context, but existing conversational agents are context insensitive. To overcome this limitation, our system uses a simple stack based context identification and storage system. The conversational agent generates responses according to the current context of conversation. allowing more human-like conversations.

Software agents represent an assured computing paradigm that tends to emerge to be an elegant technology to solve present day problems. The eminent Scientific Community has proved us with the usage or implementation of software agent's usage approach that simplifies the proposed solution in various types to solve the traditional computing problems arise. The proof of the same is implemented in several applications that exist based on this area of technology where the software agents have maximum benefits but on the same hand absence of the suitable security mechanisms that endures for systems that are based on representation of barriers exists in the paradigm with respect to present day industry. As the application proposing present security mechanisms is not a trivial one as the agent based system builders or developers who are not often security experts as they subsequently do not count on the area of expertise. This paper presents a novel approach for protecting the infrastructure for solving the issues considered to be malicious host in mobile agent system by implementing a secure protocol to migrate agents from host to host relying in various elements based on the enhanced Trusted Platforms Modules (TPM) for processing data. We use enhanced extension to the Java Agent Development framework (JADE) in our proposed system and a migrating protocol is used to validate the proposed framework (AVASPA).

Risk Assessment is a foundation of decision-making about a future project behaviour or action. The related decision made might entail further analyzes to perform risk- reduction. The risk is a general phenomenon that takes different depicts and types. Static risk and its circumstances do not significantly change over time while dynamic risk arises out of the changes in interrelated circumstances. A Multi-Agent System (MAS) approach has become a popular tool to tackle different problems that relate to risk. The MAS helps in the decision aid processes and when responding to the consequences of the risk. This paper surveys some of the existing methods and techniques of risk assessment in different application domains. The survey focuses on the employment of MAS approach in risk assessment. The survey outcomes an illustration of the roles and contributions of the MAS in the Dynamic Risk Assessment (DRA) field.

Being the most important critical infrastructure in Cyber-Physical Systems (CPSs), a smart grid exhibits the complicated nature of large scale, distributed, and dynamic environment. Taxonomy of attacks is an effective tool in systematically classifying attacks and it has been placed as a top research topic in CPS by a National Science Foundation (NSG) Workshop. Most existing taxonomy of attacks in CPS are inadequate in addressing the tight coupling of cyber-physical process or/and lack systematical construction. This paper attempts to introduce taxonomy of attacks of agent-based smart grids as an effective tool to provide a structured framework. The proposed idea of introducing the structure of space-time and information flow direction, security feature, and cyber-physical causality is innovative, and it can establish a taxonomy design mechanism that can systematically construct the taxonomy of cyber attacks, which could have a potential impact on the normal operation of the agent-based smart grids. Based on the cyber-physical relationship revealed in the taxonomy, a concrete physical process based cyber attack detection scheme has been proposed. A numerical illustrative example has been provided to validate the proposed physical process based cyber detection scheme.
 

Face-to-face negotiations always benefit if the interacting individuals trust each other. But trust is also important in online interactions, even for humans interacting with a computational agent. In this article, the authors describe a behavioral experiment to determine whether, by volunteering information that it need not disclose, a software agent in a multi-issue negotiation can alleviate mistrust in human counterparts who differ in their propensities to mistrust others. Results indicated that when cynical, mistrusting humans negotiated with an agent that proactively communicated its issue priority and invited reciprocation, there were significantly more agreements and better utilities than when the agent didn't volunteer such information. Furthermore, when the agent volunteered its issue priority, the outcomes for mistrusting individuals were as good as those for trusting individuals, for whom the volunteering of issue priority conferred no advantage. These findings provide insights for designing more effective, socially intelligent agents in online negotiation settings.