Search

Load forecasting has been deeply studied because of its critical role in Smart Grid. In current Smart Grid, there are various types of customers with different energy consumption patterns. Customer’s energy consumption patterns are referred to as customer behaviors. It would significantly benefit load forecasting in a grid if customer behaviors could be taken into account. This paper proposes an innovative method that aggregates different types of customers by their identified behaviors, and then predicts the load of each customer cluster, so as to improve load forecasting accuracy of the whole grid. Sparse Continuous Conditional Random Fields (sCCRF) is proposed to effectively identify different customer behaviors through learning. A hierarchical clustering process is then introduced to aggregate customers according to the identified behaviors. Within each customer cluster, a representative sCCRF is fine-tuned to predict the load of its cluster. The final load of the whole grid is obtained by summing the loads of each cluster. The proposed method for load forecasting in Smart Grid has two major advantages. 1) Learning customer behaviors not only improves the prediction accuracy but also has a low computational cost. 2) sCCRF can effectively model the load forecasting problem of one customer, and simultaneously select key features to identify its energy consumption pattern. Experiments conducted from different perspectives demonstrate the advantages of the proposed load forecasting method. Further discussion is provided, indicating that the approach of learning customer behaviors can be extended as a general framework to facilitate decision making in other market domains.

Reinforcement learning agents can be helped by the knowledge transferred from experienced agents. This paper studies the problem of how an experienced agent helps another agent learn when they have different learning goals by action transfer. This problem is motivated by the widely existing situations where agents have different learning goals and only action transfer is available to agents. To tackle the problem, we propose an approach to facilitate the transfer of actions that are right to a learning agent’s goal. Experimental results show the effectiveness of the proposed approach in transferring right actions to an agent and helping the agent learn to reach a different goal.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.