Auction-based Negotiation Research Articles

Water distribution in community irrigation using a multi-agent system

ABSTRACT Insufficient water for irrigation is a common problem in the Canterbury Region of New Zealand. Farmers have the option of applying for resource consent or joining a community irrigation scheme to take water. Water supply becomes more problematic during drought seasons as farmers must adhere to water restrictions imposed by the water authority. To deal with this problem, we developed an agent-based irrigation management system that can be used by farmers to calculate the ideal crop water needs on individual farms, which is particularly useful during periods of water scarcity. During water scarcity, most farms will have shortages of water. However, it is possible that there are farmers who will have excess water that could be distributed to those who need it. By doing this, farmers with excess water can make more profit and those who do not have enough water can purchase water to reduce their losses. In this work, we explore how auction-based negotiation in a multi-agent setting can be used to maximise water sharing within a community during periods of water scarcity. We evaluate various auction mechanisms that can be used to distribute excess water. In addition, we investigate the effect of various different agents' behaviours on water distribution and community profit.

A Framework of Heterogeneous Cloud Service and Multi Attributes Negotiation using Double Auction

Many double auction frameworks have been proposed for cloud service negotiations 1. However, the frameworks are not able to accommodate both the heterogeneous cloud services and multi-attributes negotiation simultaneously. Therefore, this paper proposed a double-auction framework to accommodate heterogeneous cloud services and multi-attributes negotiation. The study postulated four activities namely literature review, cloud service marketplace investigation, data formulation and double auction-based negotiation framework development. The market requirements, auction requirements and two new mechanisms of a double auction framework have been proposed. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach

Simultaneously running multiple projects are quite common in industries. These projects require local (always available to the concerned project) and global (shared among the projects) resources that are available in limited quantity. The limited availability of the global resources coupled with compelling schedule requirements at different projects leads to resource conflicts among projects. Effectively resolving these resource conflicts is a challenging task for practicing managers. This paper proposes a novel distributed multi-agent system using auctions based negotiation (DMAS/ABN) approach for resolving the resource conflicts and allocating multiple different types of shared resources amongst multiple competing projects. The existing multi-agent system (MAS) using auction makes use of exact methods (e.g. dynamic programming relaxation) for solving winner determination problem to resolve resource conflicts and allocation of single unit of only one type of shared resource. Consequently these methods fail to converge for some multi-project instances and unsuitable for real life large problems. In this paper the multi-unit combinatorial auction is proposed and winner determination problem is solved by efficient new heuristic.The proposed approach can solve complex large-sized multi-project instances without any limiting assumptions regarding the number of activities, shared resources or the number of projects. Additionally our approach further allows to random project release-time of projects which arrives dynamically over the planning horizon. The DMAS/ABN is tested on standard set of 140 problem instances. The results obtained are benchmarked against the three state-of-the-art decentralized algorithms and two existing centralized methods. For 82 of 140 instances DMAS/ABN found new best solutions with respect to average project delay (APD) and produced schedules on an average 16.79% (with maximum 57.09%) lower APD than all the five methods for solving the same class of problems.

Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agents' utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield individual and social welfares. In scenarios involving highly uncorrelated utility spaces, low social welfare usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.

Autonomic mobile sensor network with self-coordinated task allocation and execution

This paper describes a distributed layered architecture for resource-constrained multirobot cooperation, which is utilized in autonomic mobile sensor network coverage. In the upper layer, a dynamic task allocation scheme self-organizes the robot coalitions to track efficiently across regions. It uses concepts of ant behavior to self-regulate the regional distributions of robots in proportion to that of the moving targets to be tracked in a nonstationary environment. As a result, the adverse effects of task interference between robots are minimized and network coverage is improved. In the lower task execution layer, the robots use self-organizing neural networks to coordinate their target tracking within a region. Both layers employ self-organization techniques, which exhibit autonomic properties such as self-configuring, self-optimizing, self-healing, and self-protecting. Quantitative comparisons with other tracking strategies such as static sensor placements, potential fields, and auction-based negotiation show that our layered approach can provide better coverage, greater robustness to sensor failures, and greater flexibility to respond to environmental changes

An Auction-Based Negotiation Protocol for Agents with Nonlinear Utility Functions

Multi-issue negotiation protocols have been studied widely and represent a promising field since most negotiation problems in the real world involve multiple issues. The vast majority of this work has assumed that negotiation issues are independent, so agents can aggregate the utilities of the issue values by simple summation, producing linear utility functions. In the real world, however, such aggregations are often unrealistic. We cannot, for example, just add up the value of car's carburetor and the value of car's engine when engineers negotiate over the design a car. These value of these choices are interdependent, resulting in nonlinear utility functions. In this paper, we address this important gap in current negotiation techniques. We propose a negotiation protocol where agents employ adjusted sampling to generate proposals, and an auction mechanism is used to find social-welfare maximizing deals. Our experimental results show that our method substantially outperforms existing methods in large nonlinear utility spaces like those found in real world contexts. Further, we show that our protocol is incentive compatible.

Coordination infrastructures for mobile agents

The mobile agent technology is gaining more and more interest in the distributed systems community. In this context, suitable coordination models are needed to handle the coordination, both among mobile agents and between agents and Internet resources. This paper presents two coordination infrastructures that follow two different coordination models: Aglets, based on the message-passing coordination model, and Mobile Agent Reactive Spaces (MARS), based on the Linda-like one. These two approaches are compared in detail, and a mobile-agent application in the area of auction-based negotiation is used to evaluate the pros and cons of the Aglets and MARS solutions in a concrete field.