Arrow Research search

Author name cluster

Gerhard Weiss

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.

19 papers
1 author row

Possible papers

19

AAMAS Conference 2024 Conference Paper

ANOTO: Improving Automated Negotiation via Offline-to-Online Reinforcement Learning

  • Siqi Chen
  • Jianing Zhao
  • Kai Zhao
  • Gerhard Weiss
  • Fengyun Zhang
  • Ran Su
  • Yang Dong
  • Daqian Li

Automated negotiation is a crucial component for establishing cooperation and collaboration within multi-agent systems. While reinforcement learning (RL)-based negotiating agents have achieved remarkable success in various scenarios, they still face limitations due to certain assumptions on which they are based. In this work, we proposes a novel approach called ANOTO to improve the negotiating agents’ ability via offline-to-online RL. ANOTO enables a negotiating agent (1) to communicate with opponents using an end-to-end strategy that covers all negotiation actions, (2) to learn negotiation strategies from historical offline data without requiring active interactions, and (3) to enhance the optimization process during the online phase, facilitating rapid and stable performance improvements for the learned offline strategies. Experimental results, based on a number of negotiation scenarios and recent winning agents from the Automated Negotiating Agents Competitions (ANAC), are provided.

PDF

AAMAS Conference 2019 Conference Paper

ONECG: Online Negotiation Environment for Coalitional Games

  • Siqi Chen
  • Yonghao Cui
  • Cong Shang
  • Jianye Hao
  • Gerhard Weiss

Coalitional games can be used to model a variety of problems in the real world. In coalitional game theory, how players form coalitions and divide payoffs is one fundamental issue to be answered. This demo presents an online negotiation environment for coalitional games (ONECG), in which coalitional negotiation can be conduced in a distributed way between people, agents, or in mixed settings via offer exchange and natural language communication. ONECG also allows con- figuration of specifications of coalitional games, and supports the rapid development of new negotiating agents through a set of well-defined APIs. This new environment is helpful to facilitate research on training human negotiation skills in coalitional games as well as the design of negotiation agents.

PDF

TAAS Journal 2014 Journal Article

An Intelligent Agent for Bilateral Negotiation with Unknown Opponents in Continuous-Time Domains

  • Siqi Chen
  • Gerhard Weiss

Automated negotiation among self-interested autonomous agents has gained tremendous attention due to the diversity of its broad range of potential real-world applications. This article deals with a prominent type of such negotiations, namely, multiissue negotiation that runs under continuous-time constraints and in which the negotiating agents have no prior knowledge about their opponents’ preferences and strategies. A negotiation strategy called Dragon is described that employs sparse pseudoinput Gaussian processes. Specifically, Dragon enables an agent (1) to precisely model the behavior of its opponents with comparably low computational load and (2) to make decisions effectively and adaptively in very complex negotiation settings. Extensive experimental results, based on a number of negotiation scenarios and state-of-the-art negotiating agents from Automated Negotiating Agents Competitions, are provided. Moreover, the robustness of our strategy is evaluated through both empirical game-theoretic and spatial evolutionary game-theoretic analysis.

Details DOI

AAAI Conference 2014 Conference Paper

Theory of Cooperation in Complex Social Networks

  • Bijan Ranjbar-Sahraei
  • Haitham Bou Ammar
  • Daan Bloembergen
  • Karl Tuyls
  • Gerhard Weiss

This paper presents a theoretical as well as empirical study on the evolution of cooperation on complex social networks, following the continuous action iterated prisoner’s dilemma (CAIPD) model. In particular, convergence to network-wide agreement is proven for both evolutionary networks with fixed interaction dynamics, as well as for coevolutionary networks where these dynamics change over time. Moreover, an extension to the CAIPD model is proposed that allows to model influence on the evolution of cooperation in social networks. As such, this work contributes to a better understanding of behavioral change on social networks, and provides a first step towards their active control.

PDF Details

AAMAS Conference 2013 Conference Paper

A Macroscopic Model for Multi-Robot Stigmergic Coverage

  • Bijan Ranjbar-Sahraei
  • Gerhard Weiss
  • Karl Tuyls

This paper explores a multi-robot coverage approach called StiCo (for“Stigmergic Coverage”) by deriving a probabilistic macroscopic model. The proposed model makes it possible to quickly and efficiently study the swarm-type behavior of StiCo, and also allows for making predictions about its long term behavior. The model is validated in a twofold way: through computer simulations, and with real robots.

PDF

EAAI Journal 2013 Journal Article

An efficient automated negotiation strategy for complex environments

  • Siqi Chen
  • Gerhard Weiss

A complex and challenging bilateral negotiation environment for rational autonomous agents is where agents negotiate multi-issue contracts in unknown application domains with unknown opponents under real-time constraints. In this paper we present a negotiation strategy called EMAR for this kind of environment that relies on a combination of Empirical Mode Decomposition ( EM ̲ D ) and Autoregressive Moving Average ( AR ̲ MA ). EMAR enables a negotiating agent to acquire an opponent model and to use this model for adjusting its target utility in real-time on the basis of an adaptive concession-making mechanism. Experimental results show that EMAR outperforms best performing agents from the recent Automated Negotiating Agents Competitions (ANAC) in a wide range of application domains. Moreover, an analysis based on empirical game theory is provided that shows the robustness of EMAR in different negotiation contexts.

Details DOI

IJCAI Conference 2013 Conference Paper

Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains

  • Siqi Chen
  • Haitham Bou Ammar
  • Karl Tuyls
  • Gerhard Weiss

Learning in automated negotiations, while useful, is hard because of the indirect way the target function can be observed and the limited amount of experience available to learn from. This paper proposes two novel opponent modeling techniques based on deep learning methods. Moreover, to improve the learning efficacy of negotiating agents, the second approach is also capable of transferring knowledge efficiently between negotiation tasks. Transfer is conducted by automatically mapping the source knowledge to the target in a rich feature space. Experiments show that using these techniques the proposed strategies outperform existing state-of-the-art agents in highly competitive and complex negotiation domains. Furthermore, the empirical game theoretic analysis reveals the robustness of the proposed strategies.

PDF Details DOI

JAAMAS Journal 2012 Journal Article

A brief introduction to agent mining

  • Longbing Cao
  • Gerhard Weiss
  • Philip S. Yu

Abstract Agent mining is an emerging interdisciplinary area that integrates multiagent systems, data mining and knowledge discovery, machine learning and other relevant areas. It brings new opportunities to tackling issues in relevant fields more efficiently by engaging together the individual technologies. It will also bring about symbiosis and symbionts that combine advantages from the corresponding constituent systems. In this editorial, we briefly introduce the concept of agent mining, the main areas of research, and challenges and opportunities in agent mining. Finally, we give an overview of the papers in this special issue.

Details DOI

AAMAS Conference 2012 Conference Paper

Reinforcement Learning Transfer via Sparse Coding

  • Haitham Bou Ammar
  • Karl Tuyls
  • Matthew Taylor
  • Kurt Driessen
  • Gerhard Weiss

Although Reinforcement Learning (RL) has been successfully deployed in a variety of tasks, learning speed remains a fundamental problem for applying RL in complex environments. Transfer learning aims to ameliorate this shortcoming by speeding up learning through the adaptation of previously learned behaviors in similar tasks. Transfer techniques often use an inter-task mapping, which determines how a pair of tasks are related. Instead of relying on a hand-coded inter-task mapping, this paper proposes a novel transfer learning method capable of autonomously creating an inter-task mapping by using a novel combination of sparse coding, sparse projection learning and sparse Gaussian processes. We also propose two new transfer algorithms (\emph{TrLSPI} and \emph{TrFQI}) based on least squares policy iteration and fitted-Q-iteration. Experiments not only show successful transfer of information between similar tasks, inverted pendulum to cart pole, but also between two very different domains: mountain car to cart pole. This paper empirically shows that the learned inter-task mapping can be successfully used to (1) improve the performance of a learned policy on a fixed number of samples, (2) reduce the learning times needed by the algorithms to converge to a policy on a fixed number of samples, and (3) converge faster to a near-optimal policy given a large number of samples.

PDF

AAMAS Conference 2012 Conference Paper

Stigmergic Coverage Algorithm for Multi-Robot Systems

  • Bijan Ranjbar-Sahraei
  • Gerhard Weiss
  • Ali Nakisaee

We demonstrate the realization of stigmergic coverage for multi-robot systems. Compared to current state-of-the-art algorithms for multi-robot coverage, our Stigmergy-based Coverage algorithm (StiCo) has several key advantages. In particular, it does not need direct robot-robot communication. Moreover, this algorithm does not require any prior information about the environment. Simulation results illustrate robustness, scalability and simplicity of the algorithm.

PDF

AAMAS Conference 2011 Conference Paper

Bee-Inspired Foraging In An Embodied Swarm

  • Sjriek Alers
  • Daan Bloembergen
  • Daniel Hennes
  • Steven De Jong
  • Michael Kaisers
  • Nyree Lemmens
  • Karl Tuyls
  • Gerhard Weiss

We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation of environmental parameters (e. g. pheromones).

PDF

EAAI Journal 2005 Journal Article

Expectation-oriented modeling

  • Matthias Nickles
  • Michael Rovatsos
  • Gerhard Weiss

This work introduces expectation-oriented modeling (EOM) as a conceptual and formal framework for the modeling and influencing of black- or gray-box agents and agent interaction from the viewpoint of modelers like artificial agents and application designers. EOM is unique in that autonomous agent behavior is not restricted in advance, but only if it turns out to be necessary at runtime, and does so exploiting a seamless combination of evolving probabilistic and normative behavioral expectations as the key modeling abstraction and as the primary level of analysis and influence. Expectations are attitudes which allow for the relation of observed actions and other events to the modelers intentions and beliefs in an integrated, adaptive manner. In this regard, this work introduces a formal framework for the representation and the semantics of expectations embedded in social contexts. We see the applicability of EOM especially in open domains with a priori unknown and possibly unreliable and insincere actors, where the modeler cannot rely on cooperation or pursue her goals through the exertion of strictly normative power, e. g. the development and assertion of flexible interaction policies for trading platforms in the Internet, as illustrated in a case study. To our knowledge, EOM is the first approach to the modeling, cognitive analysis and influencing of social interaction that aims at tackling the level of expectations explicitly and systematically.

Details DOI

AAAI Conference 1994 Conference Paper

Hierarchical Chunking in Classifier Systems

  • Gerhard Weiss

Two standard schemes for learning in classifier systems have been proposed in the literature: the bucket brigade algorithm (BBA) and the profit sharing plan (PSP). The BBA is a local learning scheme which requires less memory and lower peak computation than the PSP, whereas the PSP is a global learning scheme which typically achieves a clearly better performance than the BBA. This "requirement versus achievement" difference, known as the locality/globality dilemma, is addressed in this paper. A new algorithm called hierarchical chunking algorithm (HCA) is presented which aims at synthesizing the local and the global learning schemes. This algorithm offers a solution to the locality/globality dilemma for the important class of reactive classifier systems. The contents is as follows. Section 1 describes the locality/globality dilemma and motivates the necessity of its solution. Section 2 briefly introduces basic aspects of (reactive) classifier systems that are relevant to this paper. Section 3 presents the HCA. Section 4 gives an experimental comparison of the HCA, the BBA and the PSP. Section 5 concludes the paper with a discussion and an outlook on future work.

PDF Details
v2026.09.13