Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation Techniques

Kiruthika, Usha; Somasundaram, Thamarai Selvi; Raja, S. Kanaga Suba

doi:10.1007/s10726-020-09704-z

Cited by 27 publications

(15 citation statements)

References 96 publications

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“…Automated negotiation has been widely studied in the literature (see e.g. [232,233,234,235,236,237,238]). Generally, a negotiation mechanism has three main components [239,233]: 1) the set of possible outcomes; 2) negotiation protocol that defines the rules for the negotiation; 3) the negotiation strategies of the participants that, given the set of possible outcomes and the negotiation protocol, define how the agents decide about their actions during the negotiation.…”

Section: B Decision Making and Negotiationmentioning

confidence: 99%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Kokkonen¹,

Lovén²,

Motlagh³

et al. 2022

Preprint

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Future AI applications require performance, reliability, and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on AI for edge, that is, the AI methods used in resource orchestration. We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence. To justify the claim, we provide a general definition for continuum orchestration, and look at how current and emerging orchestration paradigms are suitable for the computing continuum. We describe certain major emerging research themes that may affect future orchestration, and provide an early vision of an orchestration paradigm that embraces those research themes. Finally, we survey current key edge AI methods and look at how they may contribute into fulfilling the vision of future continuum orchestration.

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Section: B Decision Making and Negotiationmentioning

confidence: 99%

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Kokkonen¹,

Lovén²,

Motlagh³

et al. 2022

Preprint

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show abstract

“…Furthermore, various techniques can be used to model the opponents. Based on a comprehensive literature review, Kiruthika et al (2020) classified opponent models according to the technique used in statistical, machine-learning, heuristic, and other models. One of the most commonly used methods for modeling opponents is Bayesian learning, which is a statistical model.…”

Section: Opponent Modelingmentioning

confidence: 99%

An intelligent web-based spatial group decision support system to investigate the role of the opponents’ modeling in urban land use planning

Ghavami

Taleai²,

Arentze

2022

Land Use Policy

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“…When building a negotiation agent, we normally consider three phases: pre-negotiation phase (i.e., estimation of agent owner's preferences, preference elicitation), negotiation phase (i.e., offer generation, opponent modelling) and post-negotiation phase (i.e., assessing the optimality of offers) [23]. In this paper, we are interested in the second phase, which involves a Decide component for choosing an optimal action 𝑎 𝑡 , i.e., 𝐻 𝑡 −1 𝐴 𝑜 →𝐴 𝑢 .…”

Section: Dlst-based Negotiation Modelmentioning

confidence: 99%

Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation

Bagga¹,

Paoletti²,

Stathis³

2022

Preprint

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We study how to exploit the notion of strategy templates to learn strategies for multi-issue bilateral negotiation. Each strategy template consists of a set of interpretable parameterized tactics that are used to decide an optimal action at any time. We use deep reinforcement learning throughout an actor-critic architecture to estimate the tactic parameter values for a threshold utility, when to accept an offer and how to generate a new bid. This contrasts with existing work that only estimates the threshold utility for those tactics. We pre-train the strategy by supervision from the dataset collected using "teacher strategies", thereby decreasing the exploration time required for learning during negotiation. As a result, we build automated agents for multi-issue negotiations that can adapt to different negotiation domains without the need to be pre-programmed. We empirically show that our work outperforms the state-of-the-art in terms of the individual as well as social efficiency.

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Lifecycle Model of a Negotiation Agent: A Survey of Automated Negotiation Techniques

Cited by 27 publications

References 96 publications

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

An intelligent web-based spatial group decision support system to investigate the role of the opponents’ modeling in urban land use planning

Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation

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