Robot lecture for enhancing presentation in lecture

In lectures with presentation slides such as an e-learning lecture on video, it is important for lecturers to control their non-verbal behavior involving gaze, gesture, and paralanguage. However, it is not so easy even for well-experienced lecturers to properly use non-verbal behavior in their lecture to promote learners’ understanding. This paper proposes robot lecture, in which a robot substitutes for human lecturers, and reconstructs their non-verbal behavior to enhance their lecture. Towards such reconstruction, we have designed a model of non-verbal behavior in lecture. This paper also demonstrates a robot lecture system that appropriately reproduces non-verbal behavior of human lecturers with reconstructed one. In addition, this paper reports a case study involving 36 participants with the system, whose purpose was to ascertain whether robot lecture with reconstruction could be more effective for controlling learners' attention and more beneficial for understanding the lecture contents than video lecture by human and robot lecture with simple reproduction. The results of the case study with the system suggest the effect of promoting learners’ understanding of lecture contents, the necessity of reconstructing non-verbal behavior, and the validity of the non-verbal behavior model.

Behavior model of an intelligent agent based on a genetic algorithm

A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments. Keywords: non-player character, intelligent agent, simulation, genetic algorithm

Travel Choice Behavior Model Based on Mental Accounting of Travel Time and Cost

This paper analyzes the utility calculation principle of travelers from the perspective of mental accounting and proposes a travel choice behavior model that considers travel time and cost (MA-TC model). Then, a questionnaire is designed to analyze the results of the travel choice under different decision-making scenarios. Model parameters are estimated using nonlinear regression, and the utility calculation principles are developed under different hypothetical scenarios. Then, new expressions for the utility function under deterministic and risky conditions are presented. For verification, the nonlinear correlation coefficient and hit rate are used to compare the proposed MA-TC model with the other two models: (1) the classical prospect theory with travel time and cost (PT-TC model) and (2) mental accounting based on the original hedonic editing criterion (MA-HE model). The results show that model parameters under deterministic and risky conditions are pretty different. In the deterministic case, travelers have similar sensitivity to the change in gain and loss of travel time and cost. The prediction accuracy of the MA-TC model is 3% lower than the PT-TC model and 6% higher than the MA-HE model. Under risky conditions, travelers are more sensitive to the change in loss than to the change in gain. Additionally, travelers tend to overestimate small probabilities and underestimate high probabilities when losing more than when gaining. The prediction accuracy of the MA-TC model is 2% higher than the PT-TC model and 6% higher than the MA-HE model.

General Modeling Method of Threshold-Type Multivalued Memristor and Its Application in Digital Logic Circuits

This paper presents a general modeling method for threshold-type multivalued memristors. Through this memristor modeling method, it is very simple to establish threshold-type memristor behavior models with different numbers of memristance elements, and these models are verified by numerical MATLAB simulations. A corresponding circuit-level SPICE model of the ternary memristor behavior model is developed and simulated in LTspice, shown to be consistent with the MATLAB results. Finally, the SPICE model is used to design the AND gate, OR gate, and three NOT gates of ternary state-based logic, and the effectiveness of the circuit is proved by LTSpice simulation.