HardHeaded

Krimpen, Thijs van; Looije, Daphne; Hajizadeh, Siamak

doi:10.1007/978-3-642-30737-9_17

Cited by 34 publications

(14 citation statements)

References 1 publication

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“…The HardHeaded agent [14] starts each negotiation session by computing the utility for all possible bids. Then it stores them in a search tree (i.e.…”

Section: A Hardheadedmentioning

confidence: 99%

“…This agent uses a learning module. The target of the learning module is to learn the utility value and the weights of the opposing agent [14]. To study the opposing agent, this agent makes two assumptions about the opponent; it first assumes that the opponent restricts the bids with a limited utility range.…”

Section: A Hardheadedmentioning

confidence: 99%

“…The second assumption is that the opponent does not prefer to be offered the same bid over and over again. HardHeaded's learning function is "a greedy reinforcement learning function" [14]. This learning function keeps updating the issue weights and value utilities of the preference profile immediately after each bid.…”

Section: A Hardheadedmentioning

confidence: 99%

“…This learning function keeps updating the issue weights and value utilities of the preference profile immediately after each bid. At the same time this learning function will always try to identify the most valuable bid, and the least valuable bid for the opponent, so it can offer a bid which is most likely to be accepted when the negotiating session is about to end [14].…”

Section: A Hardheadedmentioning

confidence: 99%

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Intelligent agents for automated cloud computing negotiation

Alsrheed

Rhalibi

Randles

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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Abstract-Presently, cloud providers offer "off-the-shelf" Service Level Agreements (SLA), on a "take it or leave it" basis. This paper, alternatively, proposes customized SLAs. An automated negotiation is needed to establish customized SLAs between service providers and consumers with no previous knowledge of each other. Traditional negotiations between humans are often fraught with difficulty. Thus, in this work, the use of intelligent agents to represent cloud providers and consumers is advocated. Rubinstein's Alternating Offers Protocol offers a suitable technical solution for this challenging problem. The purpose of this paper is to apply the state-of-the-art in negotiation automated algorithms/agents within a described Cloud Computing SLA framework, and to evaluate the most appropriate negotiation approach based on many criteria.

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“…The HardHeaded agent [14] starts each negotiation session by computing the utility for all possible bids. Then it stores them in a search tree (i.e.…”

Section: A Hardheadedmentioning

confidence: 99%

Section: A Hardheadedmentioning

confidence: 99%

Section: A Hardheadedmentioning

confidence: 99%

Section: A Hardheadedmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent agents for automated cloud computing negotiation

Alsrheed

Rhalibi

Randles

et al. 2014

2014 International Conference on Multimedia Computing and Systems (ICMCS)

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“…HardHeaded Frequency Model [9] Learns the issue weights based on how often the value of an issue changes. The value weights are estimated based on the frequency they are offered.…”

Section: Frequency Modelsmentioning

confidence: 99%

Predicting the Performance of Opponent Models in Automated Negotiation

Baarslag

Hendrikx

Hindriks

et al. 2013

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)

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Abstract-When two agents settle a mutual concern by negotiating with each other, they usually do not share their preferences so as to avoid exploitation. In such a setting, the agents may need to analyze each other's behavior to make an estimation of the opponent's preferences. This process of opponent modeling makes it possible to find a satisfying negotiation outcome for both parties. A large number of such opponent modeling techniques have already been introduced, together with different measures to assess their quality. The quality of an opponent model can be measured in two different ways: one is to use the agent's performance as a benchmark for the model's quality, the other is to directly evaluate its accuracy by using similarity measures. Both methods have been used extensively, and both have their distinct advantages and drawbacks. In this work we investigate the exact relation between the two, and we pinpoint the measures for accuracy that best predict performance gain. This leads us to new insights in how to construct an opponent model, and what we need to measure when optimizing performance.

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Effective Adaptive Strategy Selection Using Extended Fine-Tuning and CNN-Based Surrogate Model in Repeated-Encounter Bilateral Automated Negotiation

Chang,

Fujita

2024

Lecture Notes in Computer Science

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HardHeaded

Cited by 34 publications

References 1 publication

Intelligent agents for automated cloud computing negotiation

Intelligent agents for automated cloud computing negotiation

Predicting the Performance of Opponent Models in Automated Negotiation

Effective Adaptive Strategy Selection Using Extended Fine-Tuning and CNN-Based Surrogate Model in Repeated-Encounter Bilateral Automated Negotiation

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