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Matthias Klusch

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14 papers
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14

AAMAS Conference 2012 Conference Paper

A Development Environment for Engineering Intelligent Avatars for Semantically-enhanced Simulated Realities

  • Stefan Warwas
  • Matthias Klusch
  • Klaus Fischer
  • Philipp Slusallek

As of today, the behavior of avatars in virtual worlds is usually realized by script sequences which provide the illusion of intelligent behavior to the user. In the research project ISReal, our research group developed the first platform for deploying virtual worlds based on Semantic Web technology, which enables agents to reason about and plan with semantically annotated 3D objects. Powerful tool support is required to design agents which exploit the functionality of the ISReal platform. We decided to reuse existing facilities provided by the model-driven \textsc{Bochica} framework for AOSE and extended it with a platform model for agents situated in semantically-enhanced simulated realities.

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AAMAS Conference 2010 Conference Paper

On Monotonic Mixed Tactics and Strategies for Bilateral Multi-Issue Negotiations

  • Jan Richter
  • Matthias Klusch
  • Ryszard Kowalczyk

We present an initial comparative evaluation between monotonic mixing and the traditional linear weighted combination of tactics in a multi-issue negotiation scenario. As thetraditional mixing method may produce a non-monotonicsequence of utilities of proposed offers in case imitative andnon-imitative tactics are mixed together (even when weightsare static) we demonstrate that both agents can gain higherutilities in many scenarios when using monotonic mixing.

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AAMAS Conference 2007 Conference Paper

Programming and Simulation of Quantum Search Agents

  • Matthias Klusch
  • René Schubotz

Key idea of this work is to appropriately extend one prominent generic agent architecture, namely InteRRap [8], to the case of a quantum pattern matching (QPM) based type-I quantum search agent (QSA) that is supposed to run on a hybrid quantum computer, and to show it's feasibility by instantiating the respective QuantumInteRRap architecture. For a comprehensive and in-depth introduction to quantum computation (QC) we refer the interested reader to [10]. An extended version of this work can be found at [6].

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EAAI Journal 2006 Journal Article

Inference in distributed data clustering

  • Josenildo Costa da Silva
  • Matthias Klusch

In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present KDEC-S algorithm for distributed data clustering, which is shown to provide mining results while preserving confidentiality of original data. We also present a confidentiality framework with which we can state the confidentiality level of KDEC-S. The underlying idea of KDEC-S is to use an approximation of density estimation such that the original data cannot be reconstructed to a given extent.

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EAAI Journal 2005 Journal Article

Distributed data mining and agents

  • Josenildo C. da Silva
  • Chris Giannella
  • Ruchita Bhargava
  • Hillol Kargupta
  • Matthias Klusch

Multi-agent systems (MAS) offer an architecture for distributed problem solving. Distributed data mining (DDM) algorithms focus on one class of such distributed problem solving tasks—analysis and modeling of distributed data. This paper offers a perspective on DDM algorithms in the context of multi-agents systems. It discusses broadly the connection between DDM and MAS. It provides a high-level survey of DDM, then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. It reviews algorithms for distributed clustering, including privacy-preserving ones. It describes challenges for clustering in sensor-network environments, potential shortcomings of the current algorithms, and future work accordingly. It also discusses confidentiality (privacy preservation) and presents a new algorithm for privacy-preserving density-based clustering.

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IJCAI Conference 2003 Conference Paper

Distributed Clustering Based on Sampling Local Density Estimates

  • Matthias Klusch
  • Stefano Lodi
  • Gianluca Moro

Huge amounts of data are stored in autonomous, geographically distributed sources. The discovery of previously unknown, implicit and valuable knowledge is a key aspect of the exploitation of such sources. In recent years several approaches to knowledge discovery and data mining, and in particular to clustering, have been developed, but only a few of them are designed for distributed data sources. We propose a novel distributed clustering algorithm based on non-parametric kernel density estimation, which takes into account the issues of privacy and communication costs that arise in a distributed environment.

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ICAPS Conference 1998 Conference Paper

Multi-Agent Coalition Formation in Power Transmission Planning: A Bilateral Shapley Value Approach

  • Javier Contreras
  • Matthias Klusch
  • Jerome Yen

more decentralized system or negotiation infrastructure has Deregulation and restructuring have become unavoidable re trends to the power industry recently, in order to increase its im efficiency, to reduce operation costs, or to provide customers al a better service. The once centralized system planning and pa management must be remodeled to reflect the changes in the market environment. We propose and have developed a [2 multi-agent based system to assist players, such as, owners in of power generation stations, owners of transmission lines, gu and groups of consumers, to select partners to form coalitions. The system provides a cooperation plan and its associated cost allocation plan for the user to support its fr decision making process. Among several coalition formation ne and cost allocation criteria, we have selected the Bilateral tr Shapley Value (BSV) as the theoretical foundation to tr develop the system. We have tested the multi-agent system with a classical transmission expansion example. wh wh co

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