The applicability of widely adopted machine learning (ML) methods to classification is circumscribed by the imperatives of explicability and uncertainty, particularly evident in domains such as healthcare, behavioural sciences, and finances, wherein accountability assumes priority. Recently, Small and Incomplete Dataset Analyser (SaNDA) has been proposed to enhance the ability to perform classification in such domains, by developing a data abstraction protocol using a ROC curve-based method. This paper focuses on column-wise data transformations called abstractions, which are crucial for SaNDA's classification process and explores alternative abstractions protocols, such as constant binning and quantiles. The best-performing methods have been compared against Random Forest as a baseline for explainable methods. The results suggests that SaNDA can be a viable substitute for Random Forest when data is incomplete, even with minimal missing values. It consistently maintains high accuracy even when half of the dataset is missing, unlike Random Forest which experiences a significant decline in accuracy under similar conditions.

Pirate syndicates capturing tankers to siphon oil, causing an estimated cost of $5 billion a year, has become a serious security issue for maritime traffic. In response to the threat, coast guards and navies deploy patrol boats to protect international oil trade. However, given the vast area of the sea and the highly time and space dependent behaviors of both players, it remains a significant challenge to find efficient ways to deploy patrol resources. In this paper, we address the research challenges and provide four key contributions. First, we construct a Stackelberg model of the oil-siphoning problem based on incident reports of actual attacks; Second, we propose a compact formulation and a constraint generation algorithm, which tackle the exponentially growth of the defender’s and attacker’s strategy spaces, respectively, to compute efficient strategies of security agencies; Third, to further improve the scalability, we propose an abstraction method, which exploits the intrinsic similarity of defender’s strategy space, to solve extremely large-scale games; Finally, we evaluate our approaches through extensive simulations and a detailed case study with real ship traffic data. The results demonstrate that our approach achieves a dramatic improvement of scalability with modest influence on the solution quality and can scale up to realistic-sized problems.

Games that model real world interactions are often complex, with huge numbers of possible strategies and information states. We are interested in better understanding the effect of abstraction in game-theoretic analysis. In particular, we focus on the strategy selection problem: how should an agent choose a strategy to play in a game, based on an abstracted game model? This problem has three interacting Figure 1: 2-players asymmetric abstractions components: (1) the method for abstracting the game, (2) the method for selecting a strategy based on the abstraction, and An example of an abstraction meta-game is shown in Figure (3) the method for mapping this strategy back to the original 1. In this example, we have two players who are playing game. This approach has been studied extensively for the one-shot normal form game shown at the top of the poker, which is a 2-player, zero-sum game. However, much figure; this is the base game. They each perform their own less is known about how abstraction interacts with strategy (unspecified) abstraction to reduce the game.

Proc. Eleventh Intl. Joint Conference on Artificial Intelligence (pp. 681-687). Detroit, MI: IJCAII.