Towards Explainable AI Algorithms via Fitness Landscape Analysis in Evolutionary Computation

通过进化计算中的适应度景观分析实现可解释的人工智能算法

• 批准号: 2890959
• 金额: --
• 依托单位: Edinburgh Napier University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890959
• 关键词: Towards; Explainable; AI; Algorithms; via

There lies a motivation in investigating how well meta-heuristic search methods like Genetic Algorithms perform when used alongside illumination algorithms like MAP-Elites to confidently select the best candidate solutions from the solution space. From these 'best' candidate solutions, the focus is then on how these solutions can be explained using XAI algorithms, and how well XAI algorithms can be represented using Fitness Landscapes. There is research to support the use of EC and MAP-Elites together [1] and suggests that their use can be explored to help better accommodate user preferences. There is also research that supports the use of XAI and landscape analysis, with one study finding that the landscape was neutral overall. A neutral landscape can indicate redundant features exist within model configurations, meaning that the end analysis is not as refined as it could be [3]. Therefore, it is important to ensure that the models only contain the most pertinent features that are needed for the transformation especially when there is a reliance on the outcome of the model.Fitness landscapes are a useful way to keep track of the model fitness generated by XAI algorithms, and XAI methods could also be used in fitness landscapes to reinforce the strength of the EC algorithm and the trust from the end-users. The interplay between these terms opens up a lot of potential to research new areas where this could be applied, for instance this could be implemented in healthcare diagnosis tools, the decision could be supported by a number of factors pertinent to that illness, which can be achieved using an XAI method known as Counterfactual Analysis [2]. The fitness of this model can be shown on a fitness landscape, where this model can be generated across 10 simulated runs. In each run, the number of best candidate solutions (models) could be selected using MAP-Elites. These solutions will have an associated fitness score and can be plotted on a fitness landscape to reinforce how strong the model is across each of the independent simulations, using something like decision trees to determine if the landscape analysis is good or bad. This is just one possible scenario, other scenarios can be modelled and experimented with to discover the potential of integrating these technologies. Currently, there exists a number of XAI tools that try to bridge this gap in understanding for end-users[4]. The interest will be in seeing how well these things work together for a set of different problem domains, where the requirements for each end-user will differ.

研究遗传算法等元启发式搜索方法与 MAP-Elites 等照明算法一起使用时的表现如何，以自信地从解决方案空间中选择最佳候选解决方案是有动机的。从这些“最佳”候选解决方案中，重点是如何使用 XAI 算法解释这些解决方案，以及如何使用健身景观来表示 XAI 算法。有研究支持 EC 和 MAP-Elites 一起使用 [1]，并表明可以探索它们的使用以帮助更好地适应用户偏好。还有研究支持使用 XAI 和景观分析，其中一项研究发现景观总体上是中性的。中性景观可以表明模型配置中存在冗余特征，这意味着最终分析没有达到应有的程度[3]。因此，确保模型仅包含转换所需的最相关的特征非常重要，尤其是当依赖模型的结果时。适应度景观是跟踪 XAI 算法生成的模型适应度的有用方法，并且 XAI 方法也可以用于适应度景观中，以增强 EC 算法的强度和最终用户的信任。这些术语之间的相互作用为研究可应用的新领域开辟了很大的潜力，例如，这可以在医疗保健诊断工具中实现，该决策可以得到与该疾病相关的许多因素的支持，这可以使用称为反事实分析的 XAI 方法来实现 [2]。该模型的适应度可以在适应度景观上显示，其中该模型可以通过 10 次模拟运行生成。在每次运行中，可以使用 MAP-Elites 选择最佳候选解决方案（模型）的数量。这些解决方案将具有相关的适应度分数，并且可以绘制在适应度景观上，以加强模型在每个独立模拟中的强度，使用决策树之类的东西来确定景观分析是好是坏。这只是一种可能的场景，可以对其他场景进行建模和实验，以发现集成这些技术的潜力。目前，存在许多 XAI 工具试图弥合最终用户理解上的差距[4]。我们感兴趣的是看看这些东西在一组不同的问题领域中协同工作的效果如何，其中每个最终用户的要求会有所不同。