H.3.2.6. Games and infotainment
A.H. Khabbaz; A. Pouyan; M. Fateh; V. Abolghasemi
Abstract
This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on ...
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This paper, presents an adapted serious game for rating social ability in children with autism spectrum disorder (ASD). The required measurements are obtained by challenges of the proposed serious game. The proposed serious game uses reinforcement learning concepts for being adaptive. It is based on fuzzy logic to evaluate the social ability level of the children with ASD. The game adapts itself to the level of the autistic patient by reducing or increasing the challenges in the game via an intelligent agent during the play time. This task is accomplished by making more elements and reshaping them to a variety of real world shapes and redesigning their motions and speed. If autistic patient's communication level grows during the playtime, the challenges of game may become harder to make a dynamic procedure for evaluation. At each step or state, using fuzzy logic, the level of the player is estimated based on some attributes such as average of the distances between the fixed points gazed by the player, or number of the correct answers selected by the player divided by the number of the questioned objects. This paper offers the usage of dynamic AI difficulty system proposing a concept to enhance the conversation skills in autistic children. The proposed game is tested by participating of 3 autistic children. Each of them played the game in 5 turns. The results displays that the method is useful in the long-term.
H.3.2.6. Games and infotainment
A. Torkaman; R. Safabakhsh
Abstract
Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent ...
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Opponent modeling is a key challenge in Real-Time Strategy (RTS) games as the environment is adversarial in these games, and the player cannot predict the future actions of her opponent. Additionally, the environment is partially observable due to the fog of war. In this paper, we propose an opponent model which is robust to the observation noise existing due to the fog of war. In order to cope with the uncertainty existing in these games, we design a Bayesian network whose parameters are learned from an unlabeled game-logs 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. Experimental results on recorded games of human players show that the proposed model can predict 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.
