Negotiation among autonomous computational agents: principles, analysis and challenges

ACO-based traffic routing method with automated negotiation for connected vehicles

Most traffic control systems are centralized, where all the collected data can be analyzed to make a decision. However, there are problems with computational complexity and, more seriously, real-time decision-making. This paper proposes a decentralized traffic routing system based on a new pheromone model of ant colony optimization algorithm and an automated negotiation technique in a connected vehicle environment. In particular, connected vehicles utilize a new pheromone model, namely the inverted pheromone model, which generates a repulsive force between vehicles and gives negative feedback to the congested roads. They also perform a collective learning-based negotiation process for distributing traffic flows throughout the road networks, reducing traffic congestion. Via extensive simulations based on the Simulation of Urban Mobility, the proposed system shows that it can significantly reduce travel time and fuel consumption compared to existing systems.

Critical success factors for integrating artificial intelligence and robotics

Purpose

This paper aims to enlighten stakeholders about critical success factors (CSFs) in developing intelligent autonomous systems (IASs) by integrating artificial intelligence (AI) with robotics. It suggests a prioritization hierarchy model for building sustainable ecosystem for developing IASs.

Design/methodology/approach

This paper is based on the existing literature and on the opinion of 15 experts. All the experts have minimum of eight years of experience in AI and related technologies. The CSF theory is used as a theoretical lens and total interpretative structure modelling (TISM) is used for the prioritization of CSFs.

Findings

Developing countries like India could leverage IASs and associated technologies for solving different societal problems. Policymakers need to develop basic policies regarding data collection, standardized hardware, skilled manpower, funding and start-up culture that can act as building blocks in undertaking sustainable ecosystem for developing IASs and implementing national AI strategy. Clear-cut regulations need to be in place for the proper functioning of the ecosystem. Any technology that can function properly in India has better chances of working at the global level considering the size of the population.

Research limitations/implications

This paper had all its experts from India only, and that makes the limitation of this paper, as there is a possibility that some of the factors identified may not hold same significance in other countries.

Practical implications

Stakeholders will understand the critical factors that are important in developing sustainable ecosystem for IASs and what should be the possible order of activities corresponding to each CSF.

Originality/value

The paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs. Further, eight significant factors, that is, emerging economy multinational enterprises (EMNEs), governance, utility, manpower, capital, software, data and hardware, have come up as the most important factors in integrating AI with robotics in India.

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems

Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.

Automated negotiation in environmental resource management: Review and assessment

Negotiation is an integral part of our daily life and plays an important role in resolving conflicts and facilitating human interactions. Automated negotiation, which aims at capturing the human negotiation process using artificial intelligence and machine learning techniques, is well-established in e-commerce, but its application in environmental resource management remains limited. This is due to the inherent uncertainties and complexity of environmental issues, along with the diversity of stakeholders' perspectives when dealing with these issues. The objective of this paper is to describe the main components of automated negotiation, review and compare machine learning techniques in automated negotiation, and provide a guideline for the selection of suitable methods in the particular context of stakeholders' negotiation over environmental resource issues. We advocate that automated negotiation can facilitate the involvement of stakeholders in the exploration of a plurality of solutions in order to reach a mutually satisfying agreement and contribute to informed decisions in environmental management along with the need for further studies to consolidate the potential of this modeling approach.