Opponent Modeling in Automated Negotiation Applied to P2P Energy Trading

Automated negotiation is a key form of interaction in systems composed of multiple autonomous agents with different preferences. Such interactions aim to reach agreements through an iterative process of making offers. With the growth of Peer-to-Peer (P2P) energy markets due to the development and deployment of a variety of small-scale...

Efficiency of Peer-to-Peer Trading Coalitions in Energy Communities

Peer-to-peer trading and energy communities have garnered much attention over the last few years due to the wider spread of distributed energy resources. Much research has been performed on the mechanisms and methodologies behind their implementation and realisation. However, the efficiency and micro-structure of trading in such markets raise...

Learning a Latent Representation of the Opponent in Automated Negotiation

This paper introduces a strategy for learning opponent parameters in automated negotiation and using them for future negotiation sessions. The goal is to maximize the agent’s utility while being consistent in its performance over various negotiation scenarios. While a number of reinforcement learning approaches in the field have used Q-learning,...

Improving a Reinforcement Learning Negotiating Agent’s Performance by Extracting Information from the Opponent’s Sequence of Offers

With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.<br/><br/>Recent literature has shown great...

Opponent Modeling in Automated Bilateral Negotiation: Can Machine Learning Techniques Outperform State-of-the-Art Heuristic Techniques?

Automated negotiation agents can highly benefit from learning their opponent’s preferences. Multiple algorithms have been developed with the two main categories being: heuristic techniques and machine learning techniques. Historically, heuristic techniques have dominated the field, but with the recent development in the field of machine learning...

Investigation of the fairness metrics in automated negotiations

This paper aims to define the broad concept of fairness and investigate how it can be measured, especially considering fairness in automated negotiations. The report relies on the work on fairness issues that have been derived from the research of C. Albin [1]. Firstly, the paper elaborates on different fairness metrics from the literature...

BOFA - A Framework for Fairness in Automated Negotiations

As automated negotiating agents become more and more part of our daily life, additional care needs to be taken that the agents can negotiate fairly. Humans each have their own intrinsic view on fairness, which affects the negotiation processes and the degree to which the outcome is viewed as satisfactory. However, most current agents are built...

The Unintended Consequences Fairness Brings to Automated Negotiation

In this paper, the unintended consequences, also named edge cases in this paper, of integrating fairness into the automated negotiation process are researched. By finding these unintended consequences, we can deal with them accordingly or avoid them, as to not cause any problems with our fairness metric that might make our negotiation process...

Fairness by Discussion: An Alternative View on the Fairness of Protocols in Automated Negotiation

The field of automated negotiation promises to improve negotiations, thus, a fair outcome and process should also be considered when building these systems. However, issues exist with computational approaches to fairness with which the field of computer science is mainly concerned. To this end, we propose a new approach to fairness based on that...

An Agent-Based Social Simulation Approach to Task Allocation in Aircraft Maintenance Teams

Tighter profit margins and rising aircraft complexity are currently driving the need for aircraft maintenance organizations to increase efficiency. Many organizations believe that digitization is key for improving operational performance. Digitization of the task allocation process at a large aircraft maintenance organization did unexpectedly...

Strategy Configuration and Selection for Automated Negotiation Agents

Negotiation is the art of resolving conflicts of interests by finding outcomes that all parties agree with. Humans negotiate often, as there are many conflicts of interest in everyday life when working with other humans. Computer scientists have build computer agents that are capable of negotiating with each other in an attempt to outperform or...

Evaluating Multiparty Multilateral Turn-Taking Negotiation Protocols

In order to be accepted by negotiating parties, negotiation protocols need to be fair and unbiased. We extend the bilateral turn-taking negotiation protocol to multiparty multilateral turn-taking peer negotiation protocols maintaining its fairness properties. We define the de- sign space of such protocols and capture the design decisions that go...

Towards Understanding Negotiation Strategies: Analyzing the Dynamics of Strategy Components

With modern computers becoming more powerful, automated negotiation is coming more to the forefront of research. Software agents are able to aid human negotiators, help them achieve better outcomes and even negotiate on their behalf. An active topic in the field of automated negotiation concerns the development of negotiation strategies. While...