A NEW AGENT MATCHING SCHEME USING AN ORDERED FUZZY SIMILARITY MEASURE AND GAME THEORY

In this paper, an agent matching method for bilateral contracts in a multi-agent market is proposed. Each agent has a hierarchical representation of its trading commodity attributes by a tree structure of fuzzy attributes. Using this structure, the similarity between the trees of each pair of buyer and seller is computed using a new ordered fuzzy similarity algorithm. Then, using the concept of Stackelberg equilibrium in a leader–follower game, matchmaking is performed among the sellers and buyers. The fuzzy similarities of each agent with others in its personal viewpoint have been used as its payoffs in a bimatrix game. Through a case study for bilateral contracts of energy, the capabilities of the proposed agent-based system are illustrated.     

SIMULTANEOUS STATE ESTIMATION AND LEARNING IN REPEATED COURNOT GAMES

The aim of this article is to propose that an intelligent agent can be able to decide properly in an incomplete information repeated Cournot game. The market model and the competitors’ decision models are not known to the players. The proposed agent employs a combination of the k-nearest neighbor (KNN) method and the Bayes classifier to predict the next action of its rivals, using the market decision history. The agent takes the predicted actions as an estimate of its next state and learns the expected payoff of its state-action pairs interactively using the reinforcement learning (RL) algorithm. The results of the proposed agent's competition with two benchmark competitors in different simulated Cournot games are presented. The simulation results show that the proposed agent can significantly earn more payoffs in comparison with the two benchmark agents.     

Joining of the alloys AA1050 and AA5754—Experimental characterization and multiscale modeling based on a cohesive zone element technique

The microstructure of two widely used engineering materials is experimentally and numerically investigated. Experimental measurements at the macroscopic scale are used for the validation of the micromechanical crystal plasticity computations. A major point of the paper is the development of a cohesive zone element formulation in the framework of zero-thickness interface elements. This enables the realistic modeling of bonding and debonding based on a traction-separation law. The ability of the cohesive zone element in describing the interface behavior is illustrated by means of several numerical examples, including the process of roll bonding.     

An agent-based system for bilateral contracts of energy

In this paper, an agent-based system for bilateral contracts of energy is proposed. The generating companies submit their offers to the demand companies. The demand companies also submit their bids to the generators. Each load or generator’s agent wants to match with an opponent, which offers the most valuable proposal. However, the problem of simultaneous decision-making causes decision conflicts among the agents. To overcome this conflict, we assume loads as the leaders and generators as the followers. We use Stackelberg game to match the seller and buyer agents. The negotiation process between a buyer and its potential seller will determine the power price between them. This process is carried out through a proposed combined time-behavioral protocol (TBP). With negligible changes in around the agreed price, this protocol can reduce the negotiation time considerably. After successful negotiation, the seller and buyer agents could sign a bilateral contract of energy if the market conditions allow it. The applicability of the proposed method is illustrated through a case study.     

A multi-state Q-learning based CSMA MAC protocol for wireless networks

Due to the shared nature of wireless channels, competition among the nodes to access media is inevitable. P-persistent carrier sense multiple access (CSMA) is a medium access control scheme widely used for resource allocation in wireless environments. The probability of transmission highly affects the throughput. We model the wireless nodes as agents in a network game. The strategy of an agent is defined as the probability of transmission. Agents don’t have a priori information about the network (e.g., number of nodes, other agents strategies) and learn their optimal strategy using the historical sensory information including the number of collisions or successful transmissions. In this paper, a multi-state reinforcement learning (RL) method is designed for this purpose. The main challenge in designing an RL agent is to define the states of the environment from agent’s perspective. For this purpose, in this paper, various state representations are proposed in a multi-state Q-learning model. This leads to different agents personalities ranging from cautious agents with risk aversion to aggressive risky agents. We also propose agents with combined personalities of cautiousness and aggressiveness. The performance of the proposed Q-learning agents with different state definitions in comparison with each other and also other benchmarking agents is examined via comprehensive simulation results.     

Cournot games with linear regression expectations in oligopolistic markets

In this paper, a Cournot game in an oligopolistic market with incomplete information is considered. The market consists of some producers that compete for getting higher payoffs. For optimal decision making, each player needs to estimate its rivals’ behaviors. This estimation is carried out using linear regression and recursive weighted least-squares method. As the information of each player about its rivals increases during the game, its estimation of their reaction functions becomes more accurate. Here, it is shown that by choosing appropriate regressors for estimating the strategies of other players at each time-step of the market and using them for making the next step decision, the game will converge to its Nash equilibrium point. The simulation results for an oligopolistic market show the effectiveness of the proposed method.