Opponent modeling is a key challenge in the Real-Time Strategy (RTS) games since the environment in these games is adversarial and the player is not able to predict the future actions of his/her Opponent. Moreover, the environment is partially observable due to the fog of war. In this paper, we propose an Opponent model that is robust to the existing observation noise due to the fog of war. In order to cope with the existing uncertainty in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-log dataset so it does not require a human expert‟ s knowledge. We evaluate our model on StarCraft, which is considered as a unified test-bed in this domain. The model is compared with that proposed by Synnaeve and Bessiere. The experimental results on the recorded games of human players show that the proposed model is capable of predicting the Opponent‟ s future decisions more effectively. Using this model, it is possible to create an adaptive game intelligence algorithm applicable to RTS games, where the concept of build order (the order of building construction) exists.

With the emergence of the World Wide Web, Electronic Commerce (E-commerce) has been growing rapidly in the past two decades. Intelligent agents play the main role in making the negotiation between different entities automatically. Automated negotiation allows resolving Opponent agents' mutual concerns to reach an agreement without the risk of losing individual profits. However, due to the unknown information about the Opponent's strategies, automated negotiation is difficult. The main challenge is how to reveal the optimal information about the Opponent's strategy during the negotiation process to propose the best counter-offer. In this paper, we design a buyer agent which can automatically negotiate with the Opponent using artificial intelligence techniques and machine learning methods. The proposed buyer agent is designed to learn the Opponent's strategies during the negotiation process using four methods: "Bayesian Learning", "Kernel Density Estimation", "Multilayer Perceptron Neural Network", and "Nonlinear Regression". Experimental results show that the use of machine learning methods increases the negotiation efficiency, which is measured and evaluated by parameters such as the rate agreement (RA), average buyer utility (ABU), average seller utility (ASU), average rounds (AR). Rate agreement and average buyer utility have increased from 58% to 74% and 90% to 94%, respectively, and average rounds have decreased from 10% to 0. 04%.

Glucose mediated imaging tools have recently be-come an area of research interest in the field of nuclear medicine, especially Positron Emission Tomography (PET) imaging. The difficulties in the use, preparation and cost of radioactively-labeled glycosylated compounds led to the research and development in this present study of a new gadolinium-labeled glucose that does not have a radioactive half-life or difficulties in its synthesis and utilization. Based on the structure of fluorodeoxyglucose (18FDG), a new compound consisting of a D-glucose conjugated to a well-known chelator DTPA was synthesized, labeled with Gd3+ and examined in vitro and in vivo. The results showed a good anti-cancer potency for the prepared complex. Gd3+ -DTPA-DG did not produce any significant alteration in the blood glucose level and was also phosphorylated by hexokinase enzyme. In vitro relaxation times T1 and T2 were determined and compared with a standard compound Magnevist®. Finally, imaging studies were performed in tumor-bearing mice and the tumor areas were shown successfully. This compound may have great potential in the future in radio-oncology.

During the last decades, Opponent modeling techniques, utilized to improve the negotiation outcome, have sparked interest in the negotiation research community. In this study, we first investigate the applicability of nearest neighbor method with different distance functions in modeling the Opponent's preferences. Then, we introduce a new distance-based model to extract the Opponent's preferences in a bilateral multi issue negotiation session. We devise an experiment to evaluate the efficiency of our proposed model in a real negotiation setting in terms of a number of performance measures.