Dr Lee Christie

Dr Lee Christie's Supervisions (1)

PhD in Computing
Doctor of Philosophy [PhD]

Level Doctor of Philosophy [PhD]

Student Dr Martin Fyvie

Status Complete

Part Time No

Years 2020 - 2024

Project Title Explainability of Non-Deterministic Solvers

Project Description Explainable AI is a well-established concept, but research success in the area has mainly focused on methods that mimic human reasoning, making the path to solution readily understood by end-users. In non-deterministic solvers, the path to solution is driven by random processes that accumulate problem learning as they solve, as opposed to deduction from prior knowledge or experience. A technical description of these processes, while in some sense explanatory, is hard for non-experts to comprehend. The research challenge that ENDS will address is how to derive human-understandable knowledge about the problem from the non-deterministic solution process and translate that into an explanatory form for end-users.

Awarding Institution Robert Gordon University

Director of Studies John McCall

Second Supervisor Lee Christie

Additional Supervisor Ciprian Zavoianu

Thesis Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics.

Level	Doctor of Philosophy [PhD]
Student	Dr Martin Fyvie
Status	Complete
Part Time	No
Years	2020 - 2024
Project Title	Explainability of Non-Deterministic Solvers
Project Description	Explainable AI is a well-established concept, but research success in the area has mainly focused on methods that mimic human reasoning, making the path to solution readily understood by end-users. In non-deterministic solvers, the path to solution is driven by random processes that accumulate problem learning as they solve, as opposed to deduction from prior knowledge or experience. A technical description of these processes, while in some sense explanatory, is hard for non-experts to comprehend. The research challenge that ENDS will address is how to derive human-understandable knowledge about the problem from the non-deterministic solution process and translate that into an explanatory form for end-users.
Awarding Institution	Robert Gordon University
Director of Studies	John McCall
Second Supervisor	Lee Christie
Additional Supervisor	Ciprian Zavoianu
Thesis	Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics.