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.

In this paper we compare the abstraction methods used for state space search and planning in Artificial Intelligence with the state space relaxation methods used in Operations Research for various optimization problems such as the Travelling Salesman problem (TSP). Although developed independently, these methods are based on exactly the same general idea: lower bounds on distances in a given state space can be derived by computing exact distances in a ``simplified" state space. Our aim is to describe these methods so that the two communities understand what each other has done and can begin to work together.

Abstraction has been used in search and planning from the very beginning of AI. Many different methods and formalisms for abstraction have been proposed in the literature but they have been designed from various points of view and with varying purposes. Hence, these methods have been notoriously difficult to analyse and compare in a structured way. In order to improve upon this situation, we present a coherent and flexible framework for modelling abstraction (and abstraction-like) methods based on transformations on labelled graphs. Transformations can have certain method properties that are inherent in the abstraction methods and describe their fundamental modelling characteristics, and they can have certain instance properties that describe algorithmic and computational characteristics of problem instances. The usefulness of the framework is demonstrated by applying it to problems in both search and planning. First, we show that we can capture many search abstraction concepts (such as avoidance of backtracking between levels) and that we can put them into a broader context. We further model five different abstraction concepts from the planning literature. Analysing what method properties they have highlights their fundamental differences and similarities. Finally, we prove that method properties sometimes imply instance properties. Taking also those instance properties into account reveals important information about computational aspects of the five methods.

Abstraction has proven to be a powerful tool for controlling the combinatorics of a problemsolving search. It is also of critical importance for learning systems. In this article we present, and evaluate experimentally, a general abstraction method -- impasse-driven abstraction - which is able to provide necessary assistance to both problem solving and learning. It reduces the amount of time required to solve problems, and the time required to learn new rules. In addition, it results in the acquisition of rules that are more general than would have otherwise been learned.