Real-Time Passenger Load Estimation using In-Vehicle Data

Increased urbanisation has led to significant challenges for public transport operators. Inconsistent demand leads to peaks in passenger activity on the network. Moreover, the COVID-19 pandemic has introduced a need for social distancing as well, limiting the desired capacity of vehicles. To combat this, intelligent real-time and data-driven...

Applying Constraint Programming To Enterprise Modelling

Enterprise Modelling (EM) is the process of producing models, which in turn can be used to support understanding, analysis, (re)design, reasoning, control and learning about various aspects of an enterprise. Various EM techniques and languages exist, and are often supported by computational tools, in particular simulation. The goal of this...

Unsatisfiable core learning for Chuffed: Improving the performance of Chuffed, a Lazy-Clause-Generation solver, by using machine learning to predict unsatisfiable cores

Solving propositional satisfiability (SAT) and constraint programming (CP) instances has been a fundamental part of a wide range of modern applications. For this reason a lot of research went into improving the efficiency of modern SAT and CP solvers. Recently much of this research has gone into exploring the possibilities of integrating machine...

Learning Efficient Search Approximation in Mixed Integer Branch and Bound

In line with the growing trend of using machine learning to improve solving of combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming branch-and-bound tree by using a learned policy. In contrast to previous work using imitation learning, our policy is focused on learning which of a...

Applying Machine Learning to Learn System Dynamics Models for Urban Systems

System Dynamics (SD) is an approach to study the nonlinear behaviour of complex systems over time. SD models provide a high­level understanding of the system and aid in designing policies to achieve specific system behaviours. Conventional SD modelling requires an intensive amount of time, human resources and effort. Applying Machine Learning ...

Knowing one’s opponents: Self Modeling Advantage Actor Critic for the Iterated Prisoner’s Dilemma

A recent advancement in Reinforcement Learning is the capability of modelling opponents. In this work, we are interested in going back to basics and testing this capability within the Iterated Prisoner's Dilemma, a simple method for modelling multi agent systems. Using the self modelling advantage actor critic model, we set up a single agent...