Travel behaviour analysis has recently witnessed a rapidly growing interest in regret-based models of choice behaviour. Two different model specifications have been introduced in the transportation literature. Chorus et al. (Transportation Research B 42: 1-18, 2008a; in: Proceedings 87th Annual ...
An Agent-based Route Choice Model specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of travel demand under an integrated framework... S Zhu,D Levinson,Z Lei - 《Social Science Electronic Publishing》 被引量: 9发表: 2011年 Modeling Onl...
latent class modelrandom regretmode choicedeparture time choicesequence modelWith the objective of enhancing our understanding of student travel patterns, we examine their mode and departure time choice for discretionary trip purposes. In our study, we hypothesize that students are likely to consider ...
Simultaneous move games model discrete, multistage BB A,VL A,ML B,... - 《Artificial Intelligence》 被引量: 0发表: 2016年 Reactive Synthesis Without Regret Two-player zero-sum games of infinite duration and their quantitative versions are used in verification to model the interaction between a...
Product recommendation and decision support systems must generally develop a model of user preferences by querying or otherwise interacting with a user. Recent approaches to elicitation using minimax regret have proven to be very powerful in simulation. In this work, we test both the effectiveness of...
Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number of papers have recently come out reporting success with deep model-base...
A novel 3WD model based on RT By using the basic idea of RT, Section 4.1 proposes a new 3WD method based on regret values, rejoicing values and overall psychological perception values, i.e., constructing the score function under the three strategies and analyzing the related properties of thre...
To face up these challenges, in this work, we incorporate a human decision-making model in reinforcement learning to control AVs for safe and efficient operations. Specifically, we adapt regret theory to describe a human driver's lane-changing behavior, and fit the personalized models to ...
Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number of papers have recently come out reporting success with deep model-based methods....
model selectionThis paper considers portfolio construction in a dynamic setting. We specifya loss function comprised of utility and complexity components with an unknowntradeoff parameter. We develop a novel regret-based criterion for selecting thetradeoff parameter to construct optimal sparse portfolios ...