Computational Treatement of Historical Sound Changes

BRIEFLY: for pairs of languages there seem to be ‘sound substitutions’ that systematically map words of one language to words of similar meaning in another, something explicable by supposing that the words in question languages are descended from a common ancestor, with systematic and divergent sound evolutions along the way. Working with pencil-and-paper in a … Read more

Reinforcement Learning and Game Playing

There is a theoretical and implementational field ‘Game Theory’ (GT), originating in the 1940s, concerned with ‘games’: situations where players interact with each other and possibly some other objects in a very formalised way, gaining individual ‘pay-offs’ when the ‘game’ ends. Tic-Tac-Toe is a so-called ‘2-person zero-sum game’ and GT is concerned with what are … Read more

Simulating Populations and their use of Language in Cooperation

Individuals in a species (esp. humans) may cooperate and they may communicate. Recent decades have seen quite a lot of efforts to use computational simulation — so-called individual/agent-based modelling — to investigate what cooperation is , and how communication factors into it, striving to fit this into a general picture of varying levels of survival … Read more

The AI Museum Guide: Social Characters for Multiplayer VR

Imagine visiting a virtual museum as a family after closing time, where the exhibits have mysteriously come to life and an AI-driven virtual museum guide needs your group’s help to discover what has happened. This project will develop a short multiplayer VR experience for 3–4 players, in which a MetaHuman guide dynamically responds to the … Read more

Continual Model-based Reinforcement Learning – TAKEN

Model-based reinforcement learning (MBRL) is emerging as a powerful paradigm for building intelligent agents capable of operating in the physical world. Most existing MBRL algorithms assume that the environment is stationary, i.e., that its behaviour doesn’t change so that the learnt world model and the strategies than depend on it remain optimal over time. But … Read more

Mitigating Bus Bunching using Reinforcement Learning

An ideal bus route would offer highly predictable inter-bus arrival times to travellers, in particular, overcoming the well-known ‘bus bunching’ phenomenon – “I waited ages then three came together!”. There are many sources of variability in journey times such as traffic conditions, the impact of traffic lights, and interference between buses themselves, e.g.,  queueing at … Read more

Slot-based Motorway Driving for Connected and Automated Vehicles

The Research Ireland funded ClearWay1 project is investigating new models of road management is which each vehicle is allocated a so-called ‘slot’ in which to travel for the duration of its journey. Adherence to travelling in its allocated slot ensures congestion-free travel from source to destination for each vehicle (in the absence of unexpected events). … Read more

ML-powered Parking Assistance System

Parking in busy locations eg at large-scale events (such as concerts and mass-participation sporting events) or in shopping centres at busy times (eg during ‘Christmas shopping’) often results in traffic congestion in surrounding areas (see, for example, [1], for an extreme example!). This may arise due to vehicles searching for, entering, and leaving parking spaces … Read more