Arrow Research search

Author name cluster

Mark Klein

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers
1 author row

Possible papers

11

JAAMAS Journal 2026 Journal Article

Using Domain-Independent Exception Handling Services to Enable Robust Open Multi-Agent Systems: The Case of Agent Death

  • Mark Klein
  • Juan-Antonio Rodriguez-Aguilar
  • Chrysanthos Dellarocas

Abstract This paper addresses a simple but critical question: how can we create robust multi-agent systems out of the often unreliable agents and infrastructures we can expect to find in open systems contexts? We propose an approach to this problem based on distinct exception handling (EH) services that enact coordination protocol-specific but domain-independent strategies to monitor agent systems for problems (‘exceptions’) and intervene when necessary to avoid or resolve them. The value of this approach is demonstrated for the ‘agent death’ exception in the Contract Net protocol; we show through simulation that the EH service approach provides substantially improved performance compared to existing approaches in a way that is appropriate for open multi-agent systems.

Details DOI

AAMAS Conference 2017 Conference Paper

Multi-Agent Nonlinear Negotiation for Wi-Fi Channel Assignment

  • Enrique de la Hoz
  • Ivan Marsa-Maestre
  • Jose Manuel Gimenez-Guzman
  • David Orden
  • Mark Klein

Optimizing resource use in complex networks with self-interested participants (e. g. transportation networks, electric grids, Internet systems) is a challenging and increasingly critical real-world problem. We propose an approach for solving this problem based on multi-agent nonlinear negotiation, and demonstrate it in the context of Wi-Fi channel assignment. We compare the performance of our proposed approaches with a complete information optimizer based on particle swarms, together with the de facto heuristic technique based on using the least congested channel. We have evaluated all these techniques in a wide range of settings, including randomly generated scenarios and real-world ones. Our experiments show that our approach outperforms the rest of techniques in terms of social welfare. The particle swarm optimizer is the only technique whose performance is close to ours, but its computation cost is much higher. Finally, we also study the effect of some graphs metrics on the gain that our approach can achieve.

PDF

AAAI Conference 2014 Conference Paper

Scalable Complex Contract Negotiation with Structured Search and Agenda Management

  • Xiaoqin Zhang
  • Mark Klein
  • Ivan Marsa-Maestre

A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios.

PDF Details

AAMAS Conference 2012 Conference Paper

Hierarchical Clustering and Linguistic Mediation Rules for Multiagent Negotiation

  • Enrique de la Hoz
  • Miguel Angel Lopez Carmona
  • Mark Klein
  • Ivan Marsa-Maestre

We propose a framework based on Hierarchical Clustering (HC) to perform multiagent negotiations where we can specify the type of agreements needed in terms of utility sharing among the agents. The proposed multi-round mediation process is based on the analysis of the agents’ offers at each negotiation round and the generation of a social contract at each round as a feedback to the agents, which explore the negotiation space to generate new offers. This mechanism efficiently manages negotiations following predefined consensus policies avoiding zones of no agreement.

PDF

IJCAI Conference 2009 Conference Paper

  • Ivan Marsa-Maestre
  • Miguel A. Lopez-Carmona
  • Juan R. Velasco
  • Takayuki Ito
  • Mark Klein
  • Katsuhide Fujita

Negotiation scenarios involving nonlinear utility functions are specially challenging, because traditional negotiation mechanisms cannot be applied. Even mechanisms designed and proven useful for nonlinear utility spaces may fail if the utility space is highly nonlinear. For example, although both contract sampling and constraint sampling have been successfully used in auction based negotiations with constraint-based utility spaces, they tend to fail in highly nonlinear utility scenarios. In this paper, we will show that the performance of these approaches decrease drastically in highly nonlinear utility scenarios, and propose a mechanism which balances utility and deal probability for the bidding and deal identification processes. The experiments show that the proposed mechanisms yield better results than the previous approaches in highly nonlinear negotiation scenarios.

PDF Details

AAMAS Conference 2008 Conference Paper

A Preliminary result on a representative-based multi-round protocol for multi-issue negotiations

  • Katsuhide Fujita
  • Takayuki Ito
  • Mark Klein

Multi-issue negotiation protocols represent a promising field since most negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with interdependent issues, in which agent utility functions are nonlinear. Existing works have not yet focused on agents’ private information. In addition, they were not scalable in the sense that they have shown a high failure rate for making agreements among 5 or more agents. In this paper, we focus on a novel multi-round representative-based protocol that utilizes the amount of agents’ private information revealed. Experimental results demonstrate that our mechanism reduces the failure rate in making agreements, and it is scalable on the number of agents compared with existing approaches.

PDF

IJCAI Conference 2007 Conference Paper

  • Takayuki Ito
  • Hiromitsu HATTORI
  • Mark Klein

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 a bidding-based 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.

PDF Details

AAMAS Conference 2007 Conference Paper

Using Iterative Narrowing to Enable Multi-Party Negotiations with Multiple Interdependent Issues

  • Hiromitsu HATTORI
  • Mark Klein
  • Takayuki Ito

Multi-issue negotiations are a central part of many coordination challenges, and thus represent an important research topic. Almost all previous work in this area has assumed that negotiation issues are independent, but this is rarely the case in real-world contexts. Our work focuses on negotiation with interdependent issues and, therefore, nonlinear (multi-optimum) agent utility functions. Since the utility functions are typically very complex, the challenge becomes finding high-quality negotiation outcomes without making unrealistic demands concerning how much agents reveal about their utilities. Since negotiations often involve more than two parties, the approach should also be scalable. In this paper, we propose a novel protocol for addressing these challenges, wherein agents approach agreements by iteratively narrowing the space of possible agreements. In the early stages, agents submit rough bids representing promising regions from their utility functions. In later stages, they submit increasingly narrow bids for the subset of those regions that the negotiating parties all liked. We show that our method outperforms existing methods in large nonlinear utility spaces, and is computationally feasible for negotiations with as many as ten agents.

PDF