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Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.

Service Level Agreement (SLA) negotiations are capable of helping define the quality of service in order to meet the customer’s service requirements. To date, a large number of negotiation protocols are proposed to handle single SLA negotiations, but little work can be found in handling multiple interdependent SLA negotiations in dynamic negotiation environments. This paper proposes an adaptive protocol for concurrently handling multiple interdependent SLA negotiations in dynamic environments. First, interdependencies between SLA negotiations are represented by a graph-based model. Then, an updating mechanism is proposed to handle the dynamism of multiple SLA negotiations. By applying the proposed updating mechanism, a protocol for concurrently processing SLA negotiations in dynamic environments with unexpected changes of service requests is presented. Experimental results show that the proposed approach can effectively handle unexpected changes of service requests from customers in dynamic environments, and successfully lead multiple SLA negotiations to agreements aligning with customers.

Opponent modeling is an important technique in automated negotiations. Many of the existing opponent modeling methods are focusing on predicting the opponent’s private information to improve the agent’s benefits. However, these modeling methods overlook an ability to improve the negotiation outcomes by adapting to different types of private information about the opponent when they are available beforehand. This availability may be provided by some prediction algorithms, or be prior knowledge of the agent. In this paper, we name the above ability as Information Adaptation, and propose a novel Opponent Modeling method with Information Adaptation (OMIA). Specifically, the future concessions of the opponent will firstly be learned based on the opponent’s historical offers. Then, an expected utility calculation function is introduced to adaptively guide the agent’s negotiation strategy by considering the availability and value of the opponent’s private information. The experimental results show that OMIA can adapt to different types of information, helping the agent reach agreements with the opponent and achieve higher utility values comparing to those which lack the information adaptation ability.

In traditional policy generation models, the preferences over polices are often represented by qualitative orderings due to the difficulty of acquisition of accurate utility. Thus, it is difficult to evaluate agreements in such models so that players cannot adjust their strategies during a policy generation process. To this end, this paper introduces a negotiation-based model for policy generation, which contains two evaluation methods, both from the perspectives of concessional utilities and consistency, to guide players to make decisions flexibly. The first method is used to model humans’ reasoning about how to calculate concessional utilities from uncertain preference information of policies based on fuzzy reasoning, while the second method is used to measure similarity between an ideal agreement and an offer based on a prioritised consistency degree. The experimental results show the difference between the evaluation methods and confirm that the proposed model and evaluation methods can help players achieve better agreements than an existing model.

Electronic Commerce has been a significant commercial phenomenon in recent years, and brings more benefits to people by comparison with the traditional market in aspects of cost, convenience and efficiency. The use of agent technology in e-markets for automatic bargains between buyers and sellers further increases the advantages of the e-market. However, most of existing agent-based bargain strategies assume a fixed number of negotiation participants, which may fail to enlarge agents' profits or to lead a bargain to a success when these strategies are applied in the e-market-based agent negotiations straightway. Problems such as unexpected changes on negotiation participants, possible changes on agents' expected negotiation outcomes, and unexpected switching in-between the buyer's and seller's markets need to be considered in order to guarantee agents' benefits and the success of negotiations. This paper proposes a novel agent negotiation model to help agents to perform a more effective bargain in e-markets by considering the objectiveness of the e-markets and the subjectiveness of the agents. The e-market situation by considering the number of bargain participants is proposed to reflect the objectiveness of the e-markets, and the agents' negotiation attitudes is introduced to indicate agents' responses to possible changes of the e-markets. Both the objectiveness of e-markets and the subjectiveness of agents are taken into account in negotiation procedures such as offer evaluation, negotiation decision making and counter-offer generation. Experimental results on a simulated e-market illustrate the benefits and efficiency of the proposed negotiation model in handling agents bargain problem in complex and dynamic e-market environments.

Bilateral agent negotiation is considered as a fundamental research issue in autonomous agent negotiation, and was studied well by researchers. Generally, a predefined negotiation decision function and utility function are used to generate an offer in each negotiation round according to a negotiator's negotiation strategy, preference, and restrictions. However, such a negotiation procedure may not work well when the negotiator's utility function is nonlinear, and the unique offer is difficult to be generated. That is because if the negotiator's utility function is non-monotonic, the negotiator may find several offers that come with the same utility at the same time; and if the negotiator's utility function is discrete, the negotiator may not find an offer to satisfy its expected utility exactly. In order to solve such a problem, we propose a novel negotiation model in this paper. Firstly, a 3D model is introduced to illustrate the relationships between an agent's utility function, negotiation decision function and offer generation function. Then two negotiation mechanisms are proposed to handle two types of nonlinear utility functions respectively, i.e. a multiple offer mechanism is introduced to handle non-monotonic utility functions, and an approximating offer mechanism is introduced to handle discrete utility functions. Lastly, a combined negotiation mechanism is proposed to handle nonlinear utility functions in general situations by considering both the non-monotonic and discrete. The experimental results demonstrate the effectiveness and efficiency of the proposed negotiation model.

A broker in a market enables buyers and sellers to do business with each other and can provide many value-adding functions that cannot be replaced by direct buyer-seller dealings. Recently, some research has focused on this issue. However, broker modelling based on buyer’s membership functions to carry out a matching process between buyer’s requirements in fuzzy preference information and seller’s offers is still sparse. Thus, this paper proposes membership function based matching approach of buyers and sellers through a broker in open e-marketplace. The major contributions of this paper are that (i) a proposed framework is applicable to help a broker to carry out the matching process between buyers and sellers; (ii) a proposed method is to determine buyer’s attribute weight with soft constraints by using association rule mining; and (iii) an objective optimization function and a set of constraints are built to help a broker to maximize buyer’s total utility. Experimental results demonstrate the good performance of the proposed approach in terms of satisfying buyer’s requirements and maximizing buyer’s total utility.

Concurrent multiple negotiation (CMN) provides a mechanism for an agent to simultaneously conduct more than one negotiation. There may exist different interdependency relationships among these negotiations and these interdependency relationships can impact the outcomes of these negotiations. The outcomes of these concurrent negotiations contribute together for the agent to achieve an overall negotiation goal. Handling a CMN while considering interdependency relationships among multiple negotiations is a challenging research problem. This paper: 1) comprehensively highlights research problems of negotiations at concurrent negotiation level; 2) provides a graph-based CMN model with consideration of the interdependency relationships; and 3) proposes a colored Petri net-based negotiation protocol for conducting CMNs. With the proposed protocol, a CMN can be efficiently and concurrently processed and negotiation agreements can be efficiently achieved. Experimental results indicate the effectiveness and efficiency of the proposed protocol in terms of the negotiation success rate, the negotiation time and the negotiation outcome.

In multi-issue negotiation, agents’ preferences are extremely important factors for reaching mutual beneficial agreements. However, agents would usually keeping their preferences in secret in order to avoid be exploited by their opponents during a negotiation. Thus, preference modelling has become an important research direction in the area of agent-based negotiation. In this paper, a bilateral multi-issue negotiation approach is proposed to help both negotiation agents to maximise their utilities under a setting that the opponent agent’s preference is private information. In the proposed approach, Bayesian learning is employed to analyse the opponent’s historical offers and approximately predicate the opponent’s preference over negotiation issues. Besides, a counter-offer proposition algorithm is integrated in our approach to help agents to generate mutual beneficial offers based on the preference learning result. Also, the experimental results indicate the good performance of the proposed approach in aspects of utility gain and negotiation efficiency.

Concurrent Multiple Negotiation (CMN) mechanism is necessary for agents to achieve agreements in multiple negotiations, and it has become a very important research topic in multi-agent systems in recent years. However, in the open and dynamic negotiation environment, negotiations may be dynamically and concurrently initialized or terminated during the process of other existing negotiations. Therefore, how to process dynamic CMN becomes a serious challenge in agent negotiation research. The motivation of this paper is to propose an adaptive mechanism for handling dynamic CMN by considering the possible changes of concurrent negotiations. First, a formal mechanism for modeling and representing dynamic CMN is presented. Then, a novel Colored Petri Net-based CMN protocol for processing CMN with unexpected negotiation changes is presented. We also demonstrate the performance and procedure of the proposed approach in handling the dynamism of negotiations in CMN, and the experimental results show that the proposed approach can effectively handle unexpected changes in the CMN dynamically, and successfully lead the CMN to agreements.

Electronic Commerce has been a very significant commercial phenomenon in recent years, and autonomous agents are widely adopted by business or individuals in electronic marketplaces to fulfill time consuming tasks in trading. Agent negotiation mechanisms are usually applied between conflicted agents in order to reach a mutually beneficial agreement. Prediction of trading agents' strategies and behaviours in negotiation is a very significant research topic in agent negotiation. By employing the prediction results on opponents' possible strategies and behaviours during a negotiation, trading agents can plan and perform corresponding strategies in order to maximize their own profits. Significant achievements have been made on this topic. However, most existing approaches are based on machine learning mechanisms, which may fail to capture opponents' behaviours in open and dynamic electronic marketplaces. In this paper, two agent behaviour expectation approaches are introduced to help trading agents to capture opponents' potential behaviours during a negotiation in complex e-marketplaces. i The regression analysis approach focuses on illustrating the main trends of opponents' trading behaviours; ii the vector analysis approach pays more attention to identifying opponents' detailed negotiation strategies. The experimental results show the efficiency and efficacy of the two proposed approaches in open and dynamic negotiation environments.

One of the crucial issues of automated negotiation in multi-agent systems is how to reach an agreement when a negotiation environment becomes open and dynamic. Even though some strategies have been proposed by researchers, most of them can only work within a static negotiation environment. In this paper, we present a model for designing a strategy for agents that makes adjustable rates of concession by negotiating according to the changes of environments with uncertain and dynamic outside options. This proposal is based on the market-driven agents (MDAs) model, and is guided by four factors in order to determine the degree of concession. These factors are trading opportunity, trading competition, trading time and strategy, and eagerness. The contribution of this paper is extending the MDAs model to an open and dynamic negotiation environment by considering both the current and potential changes of the environment.

Multi-issue negotiation can lead negotiators to bi-beneficial outcomes which are not applicable in single issue negotiation. In a multi-issue negotiation, a negotiator’s preference has a significant impact on the negotiation result. Most existing multi-issue negotiation strategies are based on an assumption that a negotiator will fix its predefined preference throughout a negotiation, and the negotiator’s concern on negotiated issues will not be impacted for any reason. Very little work has been done to consider a situation in which a negotiator may modify its preference during a negotiation. The motivation of this paper is to introduce a novel optimal bi-lateral multi-issue negotiation approach to handle the situation where a negotiator may modify its preference dynamically during a negotiation, and to lead the negotiation result to a bi-beneficial outcome. In order to do so, an agent behavior prediction method, an agent preference prediction method, and two optimal offer generation methods are proposed. Experimental results indicate good performance of all proposed methods, and a significant improvement is achieved on all negotiators’ utilities.

In agent negotiation, agents usually need to know their opponents’ negotiation parameters (i.e preference, deadline, reservation utility) to effectively adjust their negotiation strategies, thus an agreement can be reached. However, in a competitive negotiation environment, agents may not be willing to reveal their negotiation parameters, which increases the difficulty of reaching an agreement. In order to solve this problem, agents need to have the learning ability to predict their opponents’ negotiation parameters. In this paper, a Bayesian-based prediction approach is proposed to help an agent to predict its opponent’s negotiation deadline and reservation utility in bilateral multi-issue negotiation. Besides, a concession strategy adjustment algorithm is integrated into the proposed prediction approach to improve the negotiation result. The experimental results indicate that the proposed approach can increase the profit and the success rate of bilateral multi-issue negotiation.

Endowing the negotiation agent with a learning ability such that a more beneficial agreement might be obtained is increasingly gaining attention in agent negotiation research community. In this paper, we propose a novel bilateral negotiation model based on Bayesian learning to enable self-interested agents to adapt negotiation strategies dynamically during the negotiation process. Specifically, we assume that two agents negotiate over a single issue based on time-dependent tactic. The learning agent has a belief about the probability distribution of its opponent’s negotiation parameters (i.e., the deadline and reservation offer). By observing opponent’s historical offers and comparing them with the fitted offers derived from a regression analysis, the agent can revise its belief using the Bayesian updating rule and can correspondingly adapt its concession strategy to benefit itself. By being evaluated empirically, this model shows its effectiveness for the agent to learn the possible range of its opponent’s private information and alter its concession strategy adaptively, as a result a better negotiation outcome can be achieved.

Negotiating agents play a key role in e-markets and become more popular. However, in much existing work, the e-markets are assumed to be closed and static, which is unrealistic. To address the issue, this paper developed negotiating agents that can adapt their negotiation strategies, outcome expectations, offer evaluations, and counter-offers generations in dynamic, open e-markets. Also, the proposed agents can generate multiple counter-offers according to different preferences so as to further improve their negotiation outcomes. Finally, the experimental results show the improvements on agents' profits by employing our negotiation model.

Multiagent learning is a challenging problem in the area of multiagent systems because of the non-stationary environment caused by the interdependencies between agents. Learning for coordination becomes more difficult when agents do not know the structure of the environment and have only local observability. In this paper, an approach is proposed to enable autonomous agents to learn where and how to coordinate their behaviours in an environment where the interactions between agents are sparse. Our approach firstly adopts a statistical method to detect those states where coordination is most necessary. A Q-learning based coordination mechanism is then applied to coordinate agents' behaviours based on their local observability of the environment. We test our approach in grid world domains to show its good performance.

In this paper, a Multi-Negotiation Network (MNN) and a Multi-Negotiation Influence Diagram (MNID) are proposed to optimally handle Multiple Related Negotiations (MRN) in a multi-agent system. Most popular, state-of-the-art approaches perform MRN sequentially. However, a sequential procedure may not optimally execute MRN in terms of maximizing the global outcome, and may even lead to unnecessary losses in some situations. The motivation of this research is to use a MNN to handle MRN concurrently so as to maximize the expected utility of MRN. Firstly, both the joint success rate and the joint utility by considering all related negotiations are dynamically calculated based on a MNN. Secondly, by employing a MNID, an agent's possible decision on each related negotiation is reflected by the value of expected utility. Lastly, through comparing expected utilities between all possible policies to conduct MRN, an optimal policy is generated to optimize the global outcome ofMRN. The experimental results indicate that the proposed approach can improve the global outcome of MRN in a successful end scenario, and avoid unnecessary losses in an unsuccessful end scenario.

Electronic Commerce has been a very significant commercial phenomenon in recent years and autonomous agents have made the advantage of e-marketplace to be more distinct. By comparison with traditional markets, the e-marketplace can bring more benefits to its participators. However, when an e-market environment becomes open and dynamic, existing agent negotiation models may expose some limitations in such a complex situation. Static negotiation strategies and encounter rules are difficulty to capture changes of market situations and omit potential impacts on negotiators’ profits. For example, shoppers’ benefits may be damaged when a market shifts away from the seller’s market to the buyer’s market. In this paper, we propose a novel desire-based negotiation model to solve such a challenge in open and dynamic e-marketplace through capturing changes of market situation and potential impacts on agents’ reserved expectations. Experimental results illustrate the benefits and efficiency of the proposed model in complex e-market environments.

Multi-Agent Systems (MAS) are particularly well suited to complex problem solving, whether the MAS comprises cooperative or competitive (selfinterested) agents. In this context we discuss both dynamic team formation among the former, as well as partner selection strategies with the latter type of agent. One-shot, long-term, and (fuzzy-based) flexible formation strategies are compared and contrasted, and experiments described which compare these strategies along dimensions of Agent Search Time and Award Distribution Situation.We find that the flexible formation strategy is best suited to self-interested agents in open, dynamic environments. Agent negotiation among competitive agents is also discussed, in the context of collaborative problem solving. We present a modification to Zhang’s Dual Concern Model which enables agents to make reasonable estimates of potential partner behavior during negotiation. Lastly, we introduce a Quadratic Regression approach to partner behavior analysis/estimation, which overcomes some of the limitations of Machine Learning-based approaches.

Multi-issue negotiation protocols represent a promising field since most negotiation problems in the real world involve multiple issues. Our work focuses on negotiation with interdependent issues, in which agent utility functions are nonlinear. We have proposed the multi-round representative-based protocol that utilizes the amount of agents’ private information revealed. However, the detailed effect of the representative selection method has not been shown. In this paper, we investigate the effect of the revealed area based selection method (RAS) in which agents who revealed larger utility area will be selected representatives. In the experiments, we compare the selection method with the random selection method in which representative agents are randomly selected. As a result, RAS is better for the optimality and the failure rate of finding solutions. Also, the fairness of the number of times to be representative agents is better. Moreover, we demonstrate its effect to the optimality and the failure rate in the experiment.

Electronic commerce has been a significant commercial phenomenon in recent years and autonomous agents have made the advantages of e-markets more distinct. However, as e-market environments become open and dynamic, existing agent negotiation approaches expose some limitations. Static negotiation strategies and offer evaluation approaches might fail to capture dynamic changes of market situations, as well as changes of negotiators' expectations on negotiation outcomes. When market situations change, agents may need modify their negotiation strategies, expectations and criteria on offer evaluations as well as counter-offer generations in order to maximize their profits. Furthermore, in multi-issue negotiations, agents may have multiple preferences, which might not be delivered by most of existing negotiation approaches. In this paper, we propose a market-based multi-issue negotiation model to capture the dynamic changes of negotiation environments and impacts on negotiation strategies, counter-offer generations and offer evaluations. Also, the proposed model allows negotiators to deliver multiple offers to match their different preferences and negotiators would have more chances to reach agreements. Experimental results illustrate improvements of the proposed model on negotiators' utilities and efficiencies of the whole negotiation system by comparing with the performance of NDF negotiation model.

Traditional negotiation approaches pay intensive attention to decision making models in order to reach the optimal agreements, while placing insufficient efforts on the problem of partner selection. In open and dynamic environments, when the number of potential partners is huge, it may be expensive or even impractical to perform complicated negotiations with all of its potential partners. In this paper, based on the proposed extended dual model, we propose both linear and non-linear approaches for partner selection in multi-agent systems. By employing these two approaches with the extended dual concern model, agents can adapt their individual behaviors for partners selection in negotiation. The proposed approaches have three merits, which are: (1) both agents' own benefits and their potential partners' benefits are considered during the partners selection process; (2) agents' preferences are employed by the proposed approaches which ensure the selection results to accord with agents' expectations; (3) the proposed approaches are sensitive to changes of the negotiation environment, so they can be adopted in open and dynamic negotiation environments. According to the case study in four scenarios, the selection results are reasonable and accord with agents' expectations.

Multi-issue negotiations can lead negotiants to the "win-win" (optimal) outcomes which is not applicable in single issue negotiations. Negotiants' preferences on all negotiated issues in multi-issue negotiations impact the negotiation results a lot. Most of existing multi-issue negotiation strategies are based on the situation that all negotiants have fixed preferences, and very little work has been done on situations when negotiants modify their preferences during the negotiation. However, as the negotiation environment becomes open and dynamic, negotiants may modify their preferences dynamic for higher profits. The motivation of this paper is to propose a novel optimal multi-issue negotiations approach to handle the situation when negotiants' preferences are changed. In this model, agents' preferences are predicted dynamic based on the historical records of the current negotiation, then optimal offers are generated by employing the predicted preferences so as to lead the negotiation to "win-win" outcomes (if applicable). The experimental results indicates the proposed approach can improve negotiants' profits and efficiency considerably.

Prediction partners' behaviors in negotiation has been an active research direction in recent years. By employing the estimation results, agents can modify their own ways in order to achieve an agreement much quicker or to look after much higher benefits for themselves. Some of estimation strategies have been proposed by researchers to predict agents' behaviors, and most of them are based on machine learning mechanisms. However, when the application domains become open and dynamic, and agent relationships are complicated, it is difficult to train data which can be used to predict all potential behaviors of all agents in a multi-agent system. Furthermore because the estimation results may have errors, a single result maybe not accurate and practical enough in most situations. In order to address these issues mentioned above, we propose a power regression analysis mechanism to predict partners' behaviors in this paper. The proposed approach is based only on the history of the offers during the current negotiation and does not require any training process in advance. This approach can not only estimate a particular behavior, but also an interval of behaviors according to an accuracy requirement. The experimental results illustrate that by employing the proposed approach, agents can gain more accurate estimation results on partners' behaviors by comparing with other two estimation functions.

One of the most crucial criterion in automated negotiation is how to reach a consensus agreement for all negotiators under any negotiation environment. Currently, most negotiation strategies can work under the static environment only. This paper presents a model for designing negotiation agents that makes adjustable rates of concession by reacting to changing market situations with uncertain and dynamic outside options. This work is based on the model of market-driven agents (MDAs). To determine the amount of the concession for each trading cycle, these market-driven agents are guided by four mathematical functions of trading opportunity, trading competition, trading time and strategy and trading eagerness. The contribution of this paper is designing and developing an extended MDA model with the flexibility to respond to uncertain and dynamic outside options, so as to increase problem solving ability for agent negotiation in broad application domains.

Traditional negotiation approaches pay intensive attention to decision making models in order to reach the optimal agreements, while placing insufficient efforts on the problem of partner selection. In this paper, a fuzzy-based approach for partner selection in multi-agent systems is proposed. By employing both the fuzzy logic and the extended dual concern model, agents can adapt their individual behaviors for partner selection in negotiation. The proposed approach has three merits, which are: (1) both the agent's own benefit and its potential partners' benefits are considered for partner selection in negotiation; (2) by employing the extended dual concern model, agents' attitudes to its potential partners are considered for partner selection in negotiation; and (3) by employing the fuzzy logic, the proposed partner selection approach can be applied in open and dynamic environments easily and flexibly, and the selection results are much more accurate and reasonable.