My thesis is largely focused on the parallelisation of UCT (and other Best-First Search techniques) and the ramifications of doing so. I have identified issues with chunking in UCT, created by some forms of parallelisation, and developed a solution to this involving buffering of simulations that appear "out of order" and reevaluation of propagation data. I have developed a technique for scalable distribution of both tree data and computation across a large scale compute cluster. The context of most of my work is General Game Playing, but the techniques themselves are largely agnostic to domain.

The annual trade show CES (Consumer Electronics Show) is a deluge of smart appliances, many of which do things totally unnecessary--case in point, the talking A.I.-powered toilet--but there are some products that legitimately excite the masses and prove to be of real-world use. A Beijing startup named Forward X is hoping its self-driving robot suitcase belongs in the latter camp. Having made its debut at the Las Vegas trade show to positive coverage, the Ovis is ready to hit the global market with a crowdfunding campaign that starts today. The company foresees most of its initial customers to be Americans, which explains why the marketing effort is mostly centered around its NorCal office. But a week ago, I got the chance to meet company founder Nicholas Chee for a demonstration in Hong Kong.

LAS VEGAS – Self-driving cars may take a while to arrive, but the self-driving suitcase is here now. Some of the technologies used in autonomous cars have been adapted in products unveiled at the 2018 Consumer Electronics Show in Las Vegas, to help travelers with the weighty problem of dealing with baggage. The "robot suitcase" from California-based startup Travelmate can be controlled with a smartphone app and can roll alongside its owner at speeds up to 11 kph (6.8 mph), navigating around obstacles. "This is really a robot which follows you around," said Travelmate founder and President Maximillian Kovtun. Travelmate designed the device -- which integrates elements of artificial intelligence -- to roll at a pace that matches that of the user, or it can be directed with the smartphone app in the same manner as a drone.

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.