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A broker in an open e-marketplace enables buyers and sellers to do business with each other. Although a broker plays an important role in e-marketplaces, theory and guidelines for matching between buyers and sellers in multi-attribute exchanges are limited. Therefore, a challenge for a broker’s responsibility is how to maximize a buyer’s total satisfaction degree as its goals under the consideration of trade-off between a buyer’s buying quantity and price paid to a seller, and other attributes. To solve this challenge, this paper proposes an economic model-based matching approach between a buyer’s requirements and a seller’s offers. The major contributions of this paper are that (i) a broker can model a seller’s price policy as per a buyer’s buying quantity through communication between a broker and a seller; (ii) due to each buyer’s different quantity demand, a broker models a buyer’s satisfaction degree as per a buyer’s buying quantity based on communication between a broker and a buyer; and (iii) to carry out a broker’s matching processes, an objective function and a set of constraints are generated to help a broker to maximize a buyer’s total satisfaction degree. Experimental results demonstrate the good performance of the proposed approach.

Smart Grid markets are dynamic and complex, and brokers are widely introduced to better manage the markets. However, brokers face great challenges, including the varying energy demands of consumers, the changing prices in the markets, and the competitions between each other. This paper proposes an intelligent broker model based on hybrid learning (including unsupervised, supervised and reinforcement learning), which generates smart trading strategies to adapt to the dynamics and complexity of Smart Grid markets. The proposed broker model comprises three interconnected modules. Customer demand prediction module predicts short-term demands of various consumers with a data-driven method. Wholesale market module employs a Markov Decision Process for the one-day-ahead power auction based on the predicted demand. Retail market module introduces independent reinforcement learning processes to optimize prices for different types of consumers to compete with other brokers in the retail market. We evaluate the proposed broker model on Power TAC platform. The experimental results show that our broker is not only is competitive in making profit, but also maintains a good supply-demand balance. In addition, we also discover two empirical laws in the competitive power market environment, which are: 1. profit margin shrinks when there are fierce competitions in markets; 2. the imbalance rate of supply demand increases when the market environment is more competitive.

Social conventions have been used as an efficient mechanism to facilitate coordination among agents. Establishing a convention in a decentralised manner has attracted much attention in the literature. Existing techniques on convention emergence are not robust. These techniques may establish sub-conventions under particular network structures. The emergence of sub-conventions indicates that agents in a society fail to conform to a single convention. As a result, the coordination among these agents is negatively affected. In this paper, we propose a strategy to avoid sub-conventions under diverse network structures. The proposed strategy requires agents to only have local views. We prove that a convention can be established using the proposed strategy. We also give empirical studies on the speed of convention emergence with various experimental settings

Regressions on Sequential Data (RSD) are widely used in different disciplines. This paper proposes DeepRSD, which utilizes several different neural networks to result in an effective end-to-end learning method for RSD problems. There have been several variants of deep Recurrent Neural Networks (RNNs) in classification problems. The main functional part of DeepRSD is the stacked bi-directional RNNs, which is the most suitable deep RNN model for sequential data. We explore several conditions to ensure a plausible training of DeepRSD. More importantly, we propose an alternative dropout to improve its generalization. We apply DeepRSD to two different real-world problems and achieve state-of-the-art performances. Through comparisons with state-of-the-art methods, we conclude that DeepRSD can be a competitive method for RSD problems.

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.

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.

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.

In metropolitan regions, emergency events request urgent response within a short time limit in order to minimise the damage and the number of fatality. Most of these events require different resources that are usually distributed over a large area. How to efficiently allocate the distributed resources to an event is a challenging research issue. Traditional centralised resource allocation approaches have difficulties to find out the best resource assignment within the event’s time limits by considering the dynamics of the metropolitan environment and the event itself. In this paper, a multiagent-based decentralised resource allocation approach using domain transportation theory is proposed to handle an emergency event with multiple tasks. Experimental results indicates that the proposed approach can effectively generate the optimal resource allocation plans by considering multiple factors of an emergency event.

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.

Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

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.

This paper investigates how norm emergence can be facilitated by agents' adaptive learning behaviors in networked multiagent systems. A general learning framework is proposed, in which agents can dynamically adapt their learning behaviors through social learning of their individual learning experience. Extensive verification of the proposed framework is conducted in a variety of situations, using comprehensive evaluation criteria of efficiency, effectiveness and efficacy. Experimental results show that the adaptive learning framework is robust and efficient for evolving stable norms among agents.

SUMMARY Multiagent learning provides a promising paradigm to study how autonomous agents learn to achieve coordinated behavior in multiagent systems. In multiagent learning, the concurrency of multiple distributed learning processes makes the environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents’ behavior in this dynamic environment is a difficult problem especially when agents do not know the domain structure and at the same time have only local observability of the environment. In this paper, a coordinated learning approach is proposed to enable agents to learn where and how to coordinate their behavior in loosely coupled multiagent systems where the sparse interactions of agents constrain coordination to some specific parts of the environment. In the proposed approach, an agent first collects statistical information to detect those states where coordination is most necessary by considering not only the potential contributions from all the domain states but also the direct causes of the miscoordination in a conflicting state. The agent then learns to coordinate its behavior with others through its local observability of the environment according to different scenarios of state transitions. To handle the uncertainties caused by agents’ local observability, an optimistic estimation mechanism is introduced to guide the learning process of the agents. Empirical studies show that the proposed approach can achieve a better performance by improving the average agent reward compared with an uncoordinated learning approach and by reducing the computational complexity significantly compared with a centralized learning approach. Copyright © 2012 John Wiley & Sons, Ltd.

Outages and faults in interconnected power systems may cause cascading sequences of events, and catastrophic failures of power systems. How to efficiently manage power systems and restore the systems from faults is a challenging research issue in power engineering. Multi-agent systems are employed to address such a challenge in recent years. A centralized coordination strategy was firstly introduced to manage agents in a power system. Such a strategy usually adopts a single central coordinator to control the whole system for system management, maintenance, and restoration purposes. However, disadvantages such as deficiencies in robustness, openness, and flexibility prevent this strategy from extensive online applications. Consequently, a decentralized coordination strategy was proposed to overcome such limitations. But the decentralized coordination strategy cannot efficiently provide a global solution when serious faults spread out in a power system. In this paper, a conceptual multi-agent system design is introduced to express our proposal in power system modeling. A novel dynamic team forming mechanism is proposed to dynamically manage agents in power system with a flexible coordination structure, so as to balance the effectiveness and efficiency of the introduced multi-agent system. The results from simulations of case studies indicate the performance of the proposed multi-agent model.

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.

In Agent Based Scheduling and Planning Systems, autonomous agents are used to execute scheduling/planning tasks on behalves of represented enterprises. As application domains become more and more complex, agents are required to handle a number of changing and uncertain factors. This makes it necessary to embed state prediction mechanisms in Agent Based Scheduling and Planning Systems. In this paper, a Colored Petri Net based approach for supporting automated scheduling and planning is introduced. In the approach, we adopt an augmentation Colored Petri Net model which can not only analyse future states of a system but also estimate the success probability of reaching a particular future state. By using augmentation Colored Petri Nets to model relative dynamic factors in scheduling/planning problems, agents can predict the probable future states of a system and corresponding risks of reaching those states. The proposed approach can enable agents to make more rational and accurate decisions in complex scheduling and planning problems.

Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A double-layered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.

Multiagent learning (MAL) is a promising technique for agents to learn efficient coordinated behaviors in multiagent systems (MASs). In MAL, concurrent multiple distributed learning processes can make the learning environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents’ behaviors in this dynamic environment is a difficult problem, especially when agents do not know the domain structure and have only local observability of the environment. In this paper, a coordinated MAL approach is proposed to enable agents to learn efficient coordinated behaviors by exploiting agent independence in loosely coupled MASs. The main feature of the proposed approach is to explicitly quantify and dynamically adapt agent independence during learning so that agents can make a trade-off between a single-agent learning process and a coordinated learning process for an efficient decision making. The proposed approach is employed to solve two-robot navigation problems in different scales of domains. Experimental results show that agents using the proposed approach can learn to act in concert or independently in different areas of the environment, which results in great computational savings and near optimal performance.

In this paper, a hierarchical learning framework is proposed for emergence of social norms in networked multiagent systems. This framework features a bottom level of agents and several levels of supervisors. Agents in the bottom level interact with each other using reinforcement learning methods, and report their information to their supervisors after each interaction. Supervisors then aggregate the reported information and produce guide policies by exchanging information with other supervisors. The guide policies are then passed down to the subordinate agents in order to adjust their learning behaviors heuristically. Experiments are carried out to explore the efficiency of norm emergence under the proposed framework, and results verify that learning from local interactions integrating hierarchical supervision can be an effective mechanism for emergence of social norms.

A power market is a special kind of e-market. In a power market, all trading processes are related to three parties: buyers, suppliers and brokers. A broker acts as middlemen between buyers and suppliers in a trading process. In a power market, how to select a potential supplier for a buyer through a broker based on the buyer’s requirements is a challenging research problem. This paper proposes relaxation strategy with fuzzy constraints for supplier selection. The strategy includes three components, i.e., a supplier selection, a fuzzy constraint relaxation, and a decision making. The major contributions of this paper are that (1) the trading process between buyers and suppliers through brokers is modeled by using fuzzy constraints through the consideration of multiple attributes of the buyer’s requirements as well as potential power suppliers; and (2) a buyer can utilize a relaxation with fuzzy constraints to change its requirements in difficult situations when a broker cannot find any supplier to satisfy a buyer’s requirements. Experimental results show that our approach is successfully applied in a simulated power market.

High quality bilingual dictionaries are rarely available for lower-density language pairs, especially for those that are closely related. Using a third language as a pivot to link two other languages is a well-known solution, and usually requires only two input bilingual dictionaries to automatically induce the new one. This approach, however, produces many incorrect translation pairs because the dictionary entries are normally are not transitive due to polysemy and the ambiguous words in the pivot language. Utilizing the complete structures of the input bilingual dictionaries positively influences the result since dropped meanings can be countered. Moreover, an additional input dictionary may provide more complete information for calculating the semantic distance between word senses which is key to suppressing wrong sense matches. This paper proposes an extended constraint optimization model to inducing new dictionaries of closely related languages from multiple input dictionaries, and its formalization based on Integer Linear Programming. Evaluations indicated that the proposal not only outperforms the baseline method, but also shows improvements in performance and scalability as more dictionaries are utilized.

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.

In metropolitan regions, emergency events with different severity levels usually require multiple resources that have appropriate functionalities, money expenditure, moving velocities, etc. These resources could distribute over an extensive area with different ownerships. Solving the resource allocation problem for such an event involves complicated collaboration of multiple emergency departments under strict time constraints. Traditional resource allocation approaches usually have difficulties to efficiently find out the best resource assignment within the time limits by considering the large number of possibilities, which result in a considerable increase in fatalities. In this paper, a multiagent-based decentralised resource allocation approach using the domain transportation theory is proposed to handle a multi-task emergency event. The proposed approach is designed to effectively select appropriate resources without the global information and to concurrently generate the resource deployment plans for multiple tasks by considering the severity level of an emergency event. In the experiments, the proposed approach is tested along with other related approaches, and the experimental results indicate that the proposed approach can efficiently generate the optimal solution in terms of resource allocation time and money expenditure.

Creating coordinated multiagent policies in an environment with uncertainties is a challenging issue in the research of multiagent learning. In this paper, a coordinated learning approach is proposed to enable agents to learn both individual policies and coordinated behaviors by exploiting independent relationships inherent in many multiagent systems. We illustrate how this approach is employed to solve coordination problems in robot navigation domains. Experimental results of different scales of domains prove the effectiveness of our learning approach.

Association rule mining is one of the useful techniques in data mining and knowledge discovery that extracts interesting relationships between items in datasets. Generally, the number of association rules in a particular dataset mainly depends on the measures of 'support' and 'confidence'. To choose the number of useful rules, normally, the measures of 'support' and 'confidence' need to be tried many times. In some cases, the measures of 'support' and 'confidence' are chosen by experience. Thus, it is a time consuming to find the optimal measure of 'support' and 'confidence' for the process of association rule mining in large datasets. This paper proposes a regression based approach to improve the association rule mining process through predicting the number of rules on datasets. The approach includes a regression model in a generic level for general domains and an instantiation scheme to create concrete models in particular domains for predicting the potential number of association rules on a dataset before mining. The proposed approach can be used in broad domains with different types of datasets to improve the association rule mining process. A case study to build a concrete regression model based on a real dataset is demostrated and the result shows the good performance of the proposed approach.

A distribution network carries electricity from transmission network to consumers through facilities such as substations, buses and feeders. Distributed generations emerge as the new alternative power resource to a distribution network at a smaller and distributed scale. On one hand, distributed generations can decrease substations' load and power price. On the other hand, they will bring difficulties to substations for demand management. This paper proposes a multiagent model to represent a radial distribution network. The model includes five types of agents, which are substation agents, bus agents, feeder agents, load agents and generation agents. Through communicating with neighbouring agents, each agent can dynamically balance its own power supply and consumption, so as to perform the demand management to the distribution network. The Java Agent Development Framework (JADE) was employed to implement the proposed agents and multiagent system. The simulation result on a case study well demonstrates the good design and performance of the proposed multiagent system in both agents communication and demand management.

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.

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.

Correct title: Effects of Revealed Area Based Selection Method for Representative-Based Protocol

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.

In Agent Based Scheduling and Planning Systems, autonomous agents are used to represent enterprises and operating scheduling/planning tasks. As application domains become more and more complex, agents are required to handle a number of changing and uncertain factors. This requirement makes it necessary to embed state prediction mechanisms in Agent Based Scheduling and Planning Systems. In this chapter, we introduce a Colored Petri Net based approach that use Colored Petri Net models to represent relative dynamic factors of scheduling/planning. Furthermore, in our approach, we first introduce and adopt an improved Colored Petri Net model which can not only analyse future states of a system but also estimate the success possibility of reaching a particular future state. By using the improved Colored Petri Net model, agents can predict the possible future states of a system and risks of reaching those states. Through embedding such mechanisms, agents can make more rational and accurate decisions in complex scheduling and planning problems.

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.

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.