Agent negotiation as fuzzy constraint processing

Lai, K. Robert; Lin, Menq-Wen

doi:10.1109/fuzz.2002.1006644

Cited by 10 publications

(14 citation statements)

References 8 publications

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“…Fuzzy constraint satisfaction problems (FCSPs) [13] are defined by a collection of objects with the associated domains and a set of crisp or fuzzy constraints that relate the objects to the objective of determining whether a tuple exists that satisfies all the constraints to an extent that is greater than or equal to the threshold of acceptability. However, real-world environments are inherently distributed.…”

Section: Agent Negotiation As Distributed Fuzzy Constraint Processingmentioning

confidence: 99%

“…The concept [6,13,28], reasoning [3,21], and application [7,17] of fuzzy constraints are ready, so fuzzy constraint not only can be used to deal with imprecise and vague information inherited from fuzzy logic, but also can be used to adapt to a continually changing environment by adding or eliminating constraints. Accordingly, fuzzy constraints are regarded as suitable for addressing the imprecision and inter-dependencies involved in agent negotiation [5,12,14,18].…”

Section: Introductionmentioning

confidence: 99%

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Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation

Lai

Lin²,

2009

Appl Intell

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This work presents a general framework of agent negotiation with opponent learning via fuzzy constraintdirected approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The proposed approach via fuzzy probability constraint can not only cluster the opponent's information in negotiation process as proximate regularities to improve the convergence of behavior patterns, but also eliminate the noisy hypotheses or beliefs to enhance the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up the problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. In addition, the proposed interaction method enables the agent to make a concession dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allows an agent to achieve a higher reward, a fairer deal, or a smaller cost of negotiation.

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Section: Agent Negotiation As Distributed Fuzzy Constraint Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation

Lai

Lin²,

2009

Appl Intell

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“…Thus, many approaches [16], [18], [20] have formulated agent negotiation as a distributed fuzzy constraint satisfaction problem (DFCSP) or a prioritized fuzzy constraint satisfaction problem (PFCSP) to discover agents' potential agreements in order to reach a common satisfactory outcome. Following [13], a distributed fuzzy constraint network (DFCN) is defined as below.…”

Section: A Agent Negotiation As Distributed Fuzzy Constraint Processingmentioning

confidence: 99%

“…The concept [4], [13], [24], reasoning [2], [20], and application [6], [17] of fuzzy constraints are ready, so fuzzy constraint not only can be used to treat imprecise and vague information inherited from fuzzy logic, but also can be used to adapt to a continually changing negotiation environment by adding or eliminating constraints. Accordingly, fuzzy constraints are regarded as very suitable for addressing the imprecision and inter-dependencies involved in agent negotiation.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Constraint-Directed Autonomous Learning to Support Agent Negotiation

Lai

Lin³

et al. 2007

Third International Conference on Autonomic and Autonomous Systems (ICAS'07)

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This work presents a general framework of agent negotiation with autonomous learning via fuzzy constraint-directed approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint where each fuzzy constraint has a certain probability, and the fuzzy instance reasoning where each instance is represented as a primitive fuzzy constraint network. The proposed approach via fuzzy probability constraint can not only cluster the opponent's information in negotiation process as proximate regularities to increase the efficiency on the convergence of behavior patterns, but also eliminate the bulk of false hypotheses or beliefs to improves the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. Besides, the proposed interaction method enables the agent to make a concession dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allowed an agent to achieve a higher reward, fairer deal, or less cost of negotiation.

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“…This research focuses on methods or models for agent negotiation (Huang, P. and Sycara, K., 2002;Lopes, F. et al, 2001;Benyoucef, M. et al, 2001) and can incorporate techniques from other scientific areas such as game theory (Murakami, Y. et al, 2001), fuzzy logic (Lai, R. and Lin, M., 2002;Kowalczyk, R. and Bui, V., 2000) and genetic algorithms (Tu, M. et al, 2000). The research also extends to autonomous agent negotiation for specific application areas, such as e-commerce (Chung, M. and Honavar, V., 2000;Limthanmaphon, B. et al, 2000) and service level agreements for the Internet (Nguyen, T. et al, 2002).…”

Section: Introductionmentioning

confidence: 99%