Intelligent Opponent Research Articles

Strateška odbrana kao igra sajber bezbednosti

Currently, both assaulters and perservers strive to be in a position of access to information, and also control in the field of economics, warfare, and society. Describe in military terms, data has become a virtual 'high ground' from which a more knowledgeable opponent can be influenced. Strategic defence became a cyber security game. However, despite the numerous technological solutions introduced to address system vulnerabilities, the human factor remains the greatest threat to system security. On the example of Ukraine-Russia conflict, this paper promotes the implementation of military strategy concepts to cyberconflict in order to better address the dynamical challenges of continual interaction between conformable and intelligent opponents. It begins by arguing for the adaptability of a military paradigm in cyberspace, though, without a desire for cyberspace's militarization. The conclusion is that the military mindset, which is predicated on clashes with dynamic, adaptable opponents, is a more dependable strategy than the prevalent cyber-hygiene paradigm. Five levels of strategic thought were described in order to connect objectives to policy, strategy, campaigns and operations, tactics, and instruments. Each level of strategic thinking was applied to a hypothetical defence script. Finally, the paper showed a substitute to technocentric strategies that are insufficient to combat the opponent by incorporating strategic thinking into digital defence.

Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games

Controlling each member of the soldiers to carry out battle with Non-Playable Characters (NPC) is one of the secrets to winning Real-Time Strategy games. The game could be more complicated and offer a more engaging experience if every NPC acts like humans rather than machines with patterned behavior. Like people during a war, each army member's command requires rapid reflexes and direction to strike or evade attacks. An intelligent opponent based on ANN as NPC can react quickly to their opponents. The accuracy of ANN could be enhanced by weight modifications using a Genetic Algorithm (GA). The crossover and mutation rates significantly impact GA's performance as an ANN setup. This research aims to find the best crossover and mutation rates in GA as a weight adjustment in ANN. Experiments were conducted using an RTS game simulator using 20 scenarios on a maximum of 4000 iterations. The initial setup of each troop is random, with a seven-unit type available. In this research, the troops won because their men were subjected to fewer attacks than the opposing forces. The GA optimal crossover and mutation rates are determined using troop victories as a baseline. According to the findings, the best crossover rate for GA as an ANN weight adjustment is 0.6, whereas the specific mutation rate is 0.09. The crossover rate of 0.6 has the highest average win value and tends to increase every generation. As for the mutation rate of 0.09, it has the highest average win value. Thus, this preliminary study can develop NPC more humanly.

Adversarial risk analysis: An overview

AbstractAdversarial risk analysis (ARA) is a relatively new area of research that informs decision‐making when facing intelligent opponents and uncertain outcomes. It is a decision‐theoretic alternative to game theory. ARA enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities, and the type of strategic calculations that the opponent is using to make his decision. Within that framework, the analyst then solves the problem from the perspective of the opponent. This calculation produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility. This review covers conceptual, modeling, computational, and applied issues in ARA as well as interesting open research issues.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Defense and National Security

Adversarial Risk Analysis (Overview)

Adversarial risk analysis (ARA) is a relatively new area of research that informs decision-making when facing intelligent opponents and uncertain outcomes. It enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities and the type of strategic calculation that the opponent is using. Within that framework, the analyst then solves the problem from the perspective of the opponent while placing subjective probability distributions on all unknown quantities. This produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility. This overview covers conceptual, modeling, computational and applied issues in ARA.

Challenging state-of-the-art move ordering with Adaptive Data Structures

The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well-known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, i.e., by searching the best element first. A wide range of techniques have been developed over the years to achieve good move ordering, and improved tree pruning, in the field, in general and in particular, in the alpha-beta search, have been extensively studied. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we had previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion. Our previous results confirmed that it performs well in a very wide range of cases, and in varied types of board games. However, our previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, namely, the History Heuristic, and the Killer Moves technique. We also introduce, in this work, a mechanism by which these established move ordering strategies can be approximated, or directly implemented, in terms of ADSs. We confirm that, in a wide range of two-player and multi-player games, at various points in the game's progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.

Adversarial Risk Analysis for Enhancing Combat Simulation Models

Abstract Adversarial Risk Analysis (ARA) builds on statistical risk analysis and game theory to analyze decision situations involving two or more intelligent opponents who make decisions under uncertainty. During the past few years, the ARA approach-which is based on the explicit modelling of the decision making processes of a rational opponent-has been applied extensively in areas such as counterterrorism and corporate competition. In the context of military combat modelling, however, ARA has not been used systematically, even if there have been attempts to predict the opponent’s decisions based on wargaming, application of game theoretic equilibria, and the use of expert judgements. Against this backdrop, we argue that combining ARA with military combat modelling holds promise for enhancing the capabilities of combat modelling tools. We identify ways of combining ARA with combat modelling and give an illustrative example of how ARA can provide insights into a problem where the defender needs to estimate the utility gained from hiding its troop movements from the attacker. Even if the ARA approach can be challenging to apply, it can be instructive in that relevant assumptions about the resources, expectations and goals that guide the adversary’s decisions must be explicated.

Utilizing theory of mind for action selection applied in the domain of fighter pilot training

When developing intelligent agents, approaches that allow the anticipation of other agents is of utmost importance. For humans, this has also been shown to be crucial to establish good interactions. In this paper, a design for an agent that is equipped with theory of mind based reasoning capabilities is presented. The approach moves beyond the state of the art from several angles: first, it allows for the expression of certainties with respect to the predicted states of the other agents. Second, it allows the prediction during a substantial number of time steps in the future, thereby utilizing the theory of mind model multiple times. The approach has been applied to the domain of fighter pilots whereby intelligent opponents are developed to facilitate dedicated training for F16 fighter pilots.

Adversarial Risk Analysis for Counterterrorism Modeling

Recent large-scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the intentional actions of intelligent adversaries. Most approaches to these problems have a game-theoretic flavor although there are also several interesting decision-analytic-based proposals. One of them is the recently introduced framework for adversarial risk analysis, which deals with decision-making problems that involve intelligent opponents and uncertain outcomes. We explore how adversarial risk analysis addresses some standard counterterrorism models: simultaneous defend-attack models, sequential defend-attack-defend models, and sequential defend-attack models with private information. For each model, we first assess critically what would be a typical game-theoretic approach and then provide the corresponding solution proposed by the adversarial risk analysis framework, emphasizing how to coherently assess a predictive probability model of the adversary's actions, in a context in which we aim at supporting decisions of a defender versus an attacker. This illustrates the application of adversarial risk analysis to basic counterterrorism models that may be used as basic building blocks for more complex risk analysis of counterterrorism problems.

Graphical solution of ( n× m) matrix of a game theory

Game theory deals with decisions under uncertainty involving two or more intelligent opponents in which each opponent aspires to optimise his own decision at the expense of the other opponents. To solve the matrix of game theory, graphical method is the easiest compared to other methods such as dominance property, matrix method etc. In the graphical method it is assumed that there is no saddle point. But till now graphical method is restricted to (2× m) or ( n×2) matrix only. This paper highlights, the solution of ( n× m) matrix by graphical method, which is solved by dominance property till date. This method is easier to understand and takes less time to get the solution.

On the approximate solution of complex combat games

We view air-to-air combat as a two-target differential game between armed opponents. Although it is presently beyond the state of the art to solve for the optimal controls for both of the opponents, we can constrain the game to searching for each initial condition for the guidance law for each opponent from the sets of those considered. This constraint changes evaluation of the game to that of a discrete game, which can be solved by enumeration. As an example, a one-on-one two-dimensional air-to-air combat game has been designed. The game includes both attack and evasion guidance laws for both aircraft. To decide which of the guidance laws to use, the quantized initial-condition space is flooded with four simulated fights for each initial condition, depicting the strategies for the two opponents: attack-attack, attack-evasion, evasion-attack, and evasionevasion, and the outcomes are recorded. For each initial condition, the minimax procedure from discrete games selects the best choice from the available strategies for both players. The results are feedback and outcome charts that can be used to turn one of the automatic opponents into an intelligent opponent against a human pilot.