Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game

Interpersonal relations are fickle, with close friendships often dissolving into enmity. In this work, we explore linguistic cues that presage such transitions by studying dyadic interactions in an online strategy game where players form alliances and break those alliances through betrayal. We characterize friendships that are unlikely to last and examine temporal patterns that foretell betrayal. We reveal that subtle signs of imminent betrayal are encoded in the conversational patterns of the dyad, even if the victim is not aware of the relationship's fate. In particular, we find that lasting friendships exhibit a form of balance that manifests itself through language. In contrast, sudden changes in the balance of certain conversational attributes---such as positive sentiment, politeness, or focus on future planning---signal impending betrayal.Comment: To appear at ACL 2015. 10pp, 4 fig. Data and other info available at http://vene.ro/betrayal

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Ethics of Artificial Intelligence

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made and used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn

Social networking and digital gaming media convergence : classification and its consequences for appropriation

Within the field of Information Systems, a good proportion of research is concerned with the work organisation and this has, to some extent, restricted the kind of application areas given consideration. Yet, it is clear that information and communication technology deployments beyond the work organisation are acquiring increased importance in our lives. With this in mind, we offer a field study of the appropriation of an online play space known as Habbo Hotel. Habbo Hotel, as a site of media convergence, incorporates social networking and digital gaming functionality. Our research highlights the ethical problems such a dual classification of technology may bring. We focus upon a particular set of activities undertaken within and facilitated by the space – scamming. Scammers dupe members with respect to their ‘Furni’, virtual objects that have online and offline economic value. Through our analysis we show that sometimes, online activities are bracketed off from those defined as offline and that this can be related to how the technology is classified by members – as a social networking site and/or a digital game. In turn, this may affect members’ beliefs about rights and wrongs. We conclude that given increasing media convergence, the way forward is to continue the project of educating people regarding the difficulties of determining rights and wrongs, and how rights and wrongs may be acted out with respect to new technologies of play online and offline

APPLICATION OF GAME THEORY FOR ACTIVE CYBER DEFENSE AGAINST ADVANCED PERSISTENT THREATS

Advanced persistent threats (APTs) are determined, adaptive, and stealthy threat actors in cyber space. They are often hosted in, or sponsored by, adversary nation-states. As such, they are challenging opponents for both the U.S. military and the cyber-defense industry. Current defenses against APTs are largely reactive. This thesis used machine learning and game theory to test simulations of proactive defenses against APTs. We first applied machine learning to two benchmark APT datasets to classify APT network traffic by attack phase. This data was then used in a game model with reinforcement learning to learn the best tactics for both the APT attacker and the defender. The game model included security and resource levels, necessary conditions on actions, results of actions, success probabilities, and realistic costs and benefits for actions. The game model was run thousands of times with semi-random choices with reinforcement learning through a program created by NPS Professor Neil Rowe. Results showed that our methods could model active cyber defense strategies for defenders against both historical and hypothetical APT campaigns. Our game model is an extensible planning tool to recommend actions for defenders for active cyber defense planning against APTs.Approved for public release. Distribution is unlimited.Captain, United States Marine CorpsCaptain, United States Marine CorpsDISA, Arlington, VA, 2220