Strategic agents for multi-resource negotiation

An, Bo; Lesser, Victor; Sim, Kwang Mong

doi:10.1007/s10458-010-9137-2

Cited by 65 publications

(86 citation statements)

References 40 publications

(109 reference statements)

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“…Therefore, the equilibrium offer of buyer b i ∈ ∆ b at time t is (e t+1 s |∆ b ) ←s . 5 The corresponding offer update rule is the following:…”

Section: Pooling Choice Rulementioning

confidence: 99%

“…, 9} and the initial probability of each buyer type is set randomly. The reserve price of each buyer type is randomly selected from [40, 100] and the reserve price of the seller is randomly selected from [5,20]. Therefore, the reserve price of each buyer is always higher than the reserve price of the seller and the two agents have a negotiation space.…”

Section: Theorem 12 Our Algorithm Can Generate All Pure Strategy Sequmentioning

confidence: 99%

“…It has received a prominent attention in recent years [4,5,19,25] and its importance is widely acknowledged since intelligent agents that negotiate with each other on behalf of human users are expected to lead to more efficient negotiations [34]. A very common class of negotiation is bargaining.…”

Section: Introductionmentioning

confidence: 99%

“…The alternating-offers protocol captures the most important features of bargaining: bargaining consists of a sequence of offers and decisions to accept or reject these offers. The alternating-offers protocol has been widely used in the bargaining theory literature, e.g., [4,5,19,32,35], just to name a few.…”

Section: Introductionmentioning

confidence: 99%

“…According to this second approach, agents follow heuristic tactics that, even if producing non-equilibrium outcomes, find mutually satisfactory agreements. Well known examples are [5,14,27,36,37]. The two approaches have several interconnections, the former providing insights for the latter.…”

Section: Introductionmentioning

confidence: 99%

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Bilateral bargaining with one-sided uncertain reserve prices

Gatti

Lesser

2012

Auton Agent Multi-Agent Syst

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The problem of finding agents' rational strategies in bargaining with incomplete information is well known to be challenging. The literature provides a collection of results for very narrow uncertainty settings, but no generally applicable algorithm. This lack has led researchers to develop heuristic approaches in an attempt to find outcomes that, even if not being of equilibrium, are mutually satisfactory. In the present paper, we focus on the principal bargaining protocol (i.e., the alternating-offers protocol) where there is uncertainty regarding one agent's reserve price. We provide an algorithm based on the combination of game theoretic analysis and search techniques which finds pure strategy sequential equilibria when they exist. Our approach is sound, complete and, in principle, can be applied to other uncertainty settings, e.g., uncertain discount factors, and uncertain weights of negotiation issues in multi-issue negotiation. We experimentally evaluate our algorithm with a number of case studies showing that the average computational time is less than 30 s and at least one pure strategy equilibrium exists in almost all (about 99.7 %) the bilateral bargaining scenarios we have looked at in the paper.

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“…Therefore, the equilibrium offer of buyer b i ∈ ∆ b at time t is (e t+1 s |∆ b ) ←s . 5 The corresponding offer update rule is the following:…”

Section: Pooling Choice Rulementioning

confidence: 99%

Section: Theorem 12 Our Algorithm Can Generate All Pure Strategy Sequmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bilateral bargaining with one-sided uncertain reserve prices

Gatti

Lesser

2012

Auton Agent Multi-Agent Syst

Self Cite

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An intelligent agent‐based method for task allocation in competitive cloud environments

Kong

Zhang

et al. 2017

Concurrency and Computation

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In market-based cloud environments, both resource consumers and providers are self-interested; additionally, they can come and leave the environment freely. Therefore, the environment is competitive and uncertain. Because of the competition, participants may cheat in making deals, and this represents that the environment is insecure to resource providers who intend to earn profits through renting their resources to the tasks of resource consumers. Against this, in this paper, intelligent agents are designed to strategically quote for the tasks that they are interested in, on behalf of resource providers. Agents could quote according to the messages it obtained and the information learnt and predicted from the messages, to minimize the influence of insecure factors, such as cheating, competition, and dynamism. The experimental evaluation shows that the proposed method outperforms both a well-known multiresource negotiation-based task allocation method and a max-sum belief propagation-based method.

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Reward and Penalty Functions in Automated Negotiation

Luo

Yang

Leung

2015

Int. J. Intell. Syst.

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Automated negotiation is very important for organizing decentralized systems such as e-business, p2p systems, cloud computing, and so on. During the course of a negotiation, reward and penalty can be used to increase the chance of reaching agreements between negotiating agents, but have not been applied into automated negotiation systems well, especially integrating both in a single negotiation system. Thus, in this work we make an effort to reveal how the reward increases the acceptability of an offer and how the penalty decreases the deniability of an offer. More specifically, our study shows that the degree, to which a reward and a penalty influence the outcome, depends on the greedy degree for the reward and the creditable degree on the penalty. Therefore, if we know an offeree's utilities of accepting and denying an offer, the greedy degree for reward and the creditable degree on penalty, we can calculate how much reward and penalty the offerer agent needs to change the offeree's mind (i.e., from denying to accepting). C 2015 Wiley Periodicals, Inc.

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Strategic agents for multi-resource negotiation

Cited by 65 publications

References 40 publications

Bilateral bargaining with one-sided uncertain reserve prices

Bilateral bargaining with one-sided uncertain reserve prices

An intelligent agent‐based method for task allocation in competitive cloud environments

Reward and Penalty Functions in Automated Negotiation

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