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  1.  3
    Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies.Vee-Liem Saw, Luca Vismara & Lock Yue Chew - 2020 - Complexity 2020:1-18.
    We study how N intelligent buses serving a loop of M bus stops learn a no-boarding strategy and a holding strategy by reinforcement learning. The no-boarding and holding strategies emerge from the actions of stay or leave when a bus is at a bus stop and everyone who wishes to alight has done so. A reward that encourages the buses to strive towards a staggered phase difference amongst them whilst picking up passengers allows the reinforcement learning process to converge to (...)
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    Causal Reasoning and Meno’s Paradox.Melvin Chen & Lock Yue Chew - forthcoming - AI and Society:1-9.
    Causal reasoning is an aspect of learning, reasoning, and decision-making that involves the cognitive ability to discover relationships between causal relata, learn and understand these causal relationships, and make use of this causal knowledge in prediction, explanation, decision-making, and reasoning in terms of counterfactuals. Can we fully automate causal reasoning? One might feel inclined, on the basis of certain groundbreaking advances in causal epistemology, to reply in the affirmative. The aim of this paper is to demonstrate that one still has (...)
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    Analysis and Simulation of Intervention Strategies Against Bus Bunching by Means of an Empirical Agent-Based Model.Wei Liang Quek, Ning Ning Chung, Vee-Liem Saw & Lock Yue Chew - 2021 - Complexity 2021:1-24.
    In this paper, we propose an empirically based Monte Carlo bus-network model as a test bed to simulate intervention strategies to overcome the inefficiencies of bus bunching. The EMB model is an agent-based model which utilizes the positional and temporal data of the buses obtained from the Global Positioning System to constitute a set of empirical velocity distributions of the buses and a set of exponential distributions of interarrival time of passengers at the bus stops. Monte Carlo sampling is then (...)
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