Menq-Wen Lin

Modeling Agent Negotiation Via Fuzzy Constraints in E-Business

In e‐business, disputes between two or more parties arise for various reasons and involve different issues. Thus, resolution of these disputes frequently relies on some form of negotiation. This article presents a general problem‐solving framework for modeling multi‐issue multilateral agent negotiation using fuzzy constraints in e‐business. Fuzzy constraints are thus used not only to define each agents demands involving human concepts, but also to represent the relationships among agents. A concession strategy, based on fuzzy constraint‐based problem‐solving, is proposed to relax demands, and a trade‐off strategy is presented to evaluate existing alternatives. This approach provides a systematic method for reaching an agreement that benefits all agents with a high satisfaction degree of constraints. Meanwhile, by applying the method, agents can move toward an agreement more quickly, because their search focuses only on the feasible solution space. An example application to negotiate an insurance policy among agents is provided to demonstrate the usefulness and effectiveness of the proposed framework.

Fuzzy constraint-based agent negotiation

Conflicts between two or more parties arise for various reasons and perspectives. Thus, resolution of conflicts frequently relies on some form of negotiation. This paper presents a general problem-solving framework for modeling multi-issue multilateral negotiation using fuzzy constraints. Agent negotiation is formulated as a distributed fuzzy constraint satisfaction problem (DFCSP). Fuzzy constrains are thus used to naturally represent each agent’s desires involving imprecision and human conceptualization, particularly when lexical imprecision and subjective matters are concerned. On the other hand, based on fuzzy constraint-based problem-solving, our approach enables an agent not only to systematically relax fuzzy constraints to generate a proposal, but also to employ fuzzy similarity to select the alternative that is subject to its acceptability by the opponents. This task of problem-solving is to reach an agreement that benefits all agents with a high satisfaction degree of fuzzy constraints, and move towards the deal more quickly since their search focuses only on the feasible solution space. An application to multilateral negotiation of a travel planning is provided to demonstrate the usefulness and effectiveness of our framework.

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

This work presents a general framework of agent negotiation with opponent learning via fuzzy constraint-directed 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.

Agent negotiation as fuzzy constraint processing

This paper presents a general problem-solving framework for the modeling of agent negotiation via fuzzy constraint processing. In this approach, fuzzy constraint processing serves to improve the agents individual negotiation strategy, and also to represent the agent negotiation mechanism and interaction framework for multi-objective agent negotiation. We formulate agent negotiation as a distributed fuzzy constraint satisfaction problem (DFCSP). Fuzzy constraints, in this way, can be used not only to define the needs of agents together with their priorities, urgency, and preferences, but also the similarity between the offers and counter-offers. On the other hand, basing on fuzzy constraint-based problem-solving, we propose concession strategy for fuzzy constraint relaxation, and trade-off strategy for modifying the current solution. The purpose is to generate an acceptable solution with a maximal satisfaction degree. Additionally, we also define a state transition function which specifies the operational semantics for representing the temporally changing states and agent interaction. To demonstrate the effectiveness of this approach, an example of badge-trading is provided.

Modeling of agent negotiation via fuzzy constraint processing

This paper proposes a general problem-solving framework for modeling of agent negotiation via fuzzy constraint processing to acquire an optimal solution that satisfies agents in the environment of incomplete and imprecise information. In this approach, fuzzy constraint processing serves not only to improve the efficiency of the agents individual negotiation strategy which aims at the maximal satisfaction of its self-interest, but also to represent a negotiation protocol which determines legal or meaningful sequences of messages that must be satisfied. Thus, our approach is to model agent negotiation based on fuzzy constraint processing as a distributed fuzzy constraint satisfaction problem which objective is to find a solution that has maximal satisfaction for all fuzzy constraints in the distributed environment. In this paper, we introduce the concept of fuzzy constraint processing and the definition of distributed fuzzy constraint network. Then, the approach for modeling agent negotiation via fuzzy constraint processing is presented. Next, to demonstrate the effectiveness of this approach, an example of phone-trading in electronic marketplace is provided.

Modeling opponent's beliefs via fuzzy constraint-directed approach in agent negotiation

This work adopted the fuzzy constraint-directed approach to model opponents beliefs in agent negotiation. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The fuzzy probability constraint is used to cluster the opponents regularities and to eliminate the noisy hypotheses or beliefs, so as to increase the efficiency on the convergence of behavior patterns and to improve the effectiveness on beliefs learning. The fuzzy instance reasoning reuses 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 allows the agent to make a concession dynamically based on desirable objectives. Moreover, experimental results suggest that the proposed framework enabled an agent to achieve a higher reward, a fairer deal, or a less cost of negotiation.