Mr Aidan Wallace

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Computing Science Stirling

Mr Aidan Wallace

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About me

I'm a PhD student currently working on an investigation into explaining the decisions of commonly-used non-deterministic solvers for optimization problems, this is a joint project between the University of Stirling, Robert Gordon University and BT. The project is centred around Explainable AI, while this is well established as a concept the current research success has primarily been focused on methods that mimic human reasoning, meaning the path to solution can be more readily understood by end users and decision makers. In the case of non-deterministic solvers, the journey to a solution is driven by much more inherently random processes that can gleam and 'store' problem learning as they solve as opposed to making deductions from prior experience or knowledge. I will focus on surrogate problem models to investigate new ways of generating user-understandable problem knowledge from analysis of algorithm behaviour, as well as look at and investigate the use of natural language generation and visualisation to convert any insight gained into comprehensible explanations for domain experts and decision makers

Outputs (2)

Outputs

Conference Paper (published)

Brownlee A, Wallace A & Cairns D (2021) Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation. In: Martin K, Wiratunga N & Wijekoon A (eds.) Proceedings of the SICSA eXplainable Artifical Intelligence Workshop 2021. CEUR Workshop Proceedings, 2894. SICSA eXplainable Artifical Intelligence Workshop 2021, Aberdeen, 01.06.2021-01.06.2021. Aachen: CEUR Workshop Proceedings, pp. 64-70. http://ceur-ws.org/Vol-2894/short9.pdf


Conference Paper (published)

Wallace A, Brownlee AEI & Cairns D (2021) Towards explaining metaheuristic solution quality by data mining surrogate fitness models for importance of variables. In: Bramer M & Ellis R (eds.) Artificial Intelligence XXXVIII. Lecture Notes in Computer Science, 13101. 41st SGAI International Conference on Artificial Intelligence, AI 2021, Cambridge, 14.12.2021-16.12.2021. Cham, Switzerland: Springer, pp. 58-72. https://doi.org/10.1007/978-3-030-91100-3_5