In this paper an extension of the Network Security Games (NSG) is presented, that aims to incorporate the advantages of "standard" expert-based security risk assessment procedures and provide proper formalisation for general large-scale infrastructure protection problems. An instantiation procedure of the model is proposed, which is grounded on the classical security risk assessment methodologies, building a bridge between general standards and Game Theory Security models. The security control selection problem is modelled as a multi-objective optimisation problem. Two interwoven models are developed for addressing the security risk assessment problem. The asset model describes the system and its parameters, while the attack model is used to formalise possible threat scenarios. A specific solver for the stated multi-objective optimisation problem is described in details with theoretically grounded justification of its' correctness. Proposed model is instantiated for an airport case study, and the essential building blocks of the methodology are discussed. The work reported in this paper shows the feasibility of a generalised mathematically founded approach to security risk assessment in large-scale system engineering.

Human attitudes of objection, protest, and rebellion have undeniable potential to bring about social benefits, from social justice to healthy balance in relationships. At times, they can even be argued to be ethically obligatory. Conversely, AI rebellion is largely seen as a dangerous, destructive prospect. With the increase of interest in collaborative human/AI environments in which synthetic agents play social roles or, at least, exhibit behavior with social and ethical implications, we believe that AI rebellion could have benefits similar to those of its counterpart in humans. We introduce a framework meant to help categorize and design Rebel Agents, discuss their social and ethical implications, and assess their potential benefits and the risks they may pose. We also present AI rebellion scenarios in two considerably different contexts (military unmanned vehicles and computational social creativity) that exemplify components of the framework.

A kidney exchange is a centrally-administered barter market where patients swap their willing yet incompatible donors. Modern kidney exchanges use 2-cycles, 3-cycles, and chains initiated by non-directed donors (altruists who are willing to give a kidney to anyone) as the means for swapping. We propose significant generalizations to kidney exchange. We allow more than one donor to donate in exchange for their desired patient receiving a kidney. We also allow for the possibility of a donor willing to donate if any of a number of patients receive kidneys. Furthermore, we combine these notions and generalize them.The generalization is to exchange among organ clubs, where a club is willing to donate organs outside the club if and only if the club receives organs from outside the club according to given specifications. Forms of organ clubs already exist — under an arrangement where one gets to be in the club as a potential recipient if one is willing to donate one's organs to the club upon death. Our approach can be used as an inter-club exchange mechanism that increases systemwide good (and can also be applied to live donation). In this paper we introduce these ideas, present the notion of operation frames that can be used to sequence the operations across batches, and present integer programming formulations for the market clearing problems for these new types of organ exchanges.

Information visualization is the study of interactive visual representations of abstract data to reinforce human cognition. Most existing visualization techniques are not suited to explore and understand datasets from heterogeneous and complex sources. Assuming that agent-based models properly represent the complexity of a real system, we propose to use an approach based on the definition of an agent-based model to facilitate visual representation of simulation outputs and complex data. These concepts have been implemented in the GAMA modeling and simulation platform, in which we developed a 3D immersive environment offering the user different points of view and ways to interact. We implemented models chosen for their properties to support a linear progression in terms of complexity to test their flexibility, modularity, and adaptability. Finally, we demonstrate through the particular case of data visualization, how our approach allows us, in real time, to represent, clarify, or even discover dynamics and how that progress in terms of visualization can contribute, in turn, to improve the modeling of complex systems.

Learning approximate payoff functions is important to understand the dynamics in multi-player interactions. In general repeat games, each player's payoff can be represented as a combination of all other players' action choices using normal forms, which grow exponentially as the number of action choices increases. Graphical games, however, provide a compact representation to specify the inter-relations where one player's action choice is influenced by its neighbourhood. In this paper, we present how to learn players' approximate payoff functions from normal-form representations, yet also learn a compact graphical game representation of the inter-relations among the players. In this normal form representation, we explore the structural connections of mutual influence between players' action choices in game playing. We formally describe the problem of learning a player influence network and give a novel reward structure-learning algorithm for multiagent graphical games, called the Multi-Descendent Regression Learning Structure Algorithm (MDRLSA). We evaluate MDRLSA on random graphical games generated in GAMUT. Experiments show that MDRLSA can efficiently identify the independence among players and extract the influence graph accurately. The running time of MDRLSA increases linearly with the number of strategy profiles of a game. Compared with state-of-the-art graphical game model learning methods, MDRLSA shows efficiency in terms of time and accuracy.

This paper presents a demonstration of how AI can be useful in the game design and development process of a modern board game. By using an artificial intelligence algorithm to play a substantial amount of matches of the Ticket to Ride board game and collecting data, we can analyze several features of the gameplay as well as of the game board. Results revealed loopholes in the game's rules and pointed towards trends in how the game is played. We are then led to the conclusion that large scale simulation utilizing artificial intelligence can offer valuable information regarding modern board games and their designs that would ordinarily be prohibitively expensive or time-consuming to discover manually.

Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the state-action values rather than exploiting the relationship between and among entities that constitute the sensory input. Because of this difference, DRL needs vast amount of experience samples to learn. In this paper, we propose a Multi-focus Attention Network (MANet) which mimics human ability to spatially abstract the low-level sensory input into multiple entities and attend to them simultaneously. The proposed method first divides the low-level input into several segments which we refer to as partial states. After this segmentation, parallel attention layers attend to the partial states relevant to solving the task. Our model estimates state-action values using these attended partial states. In our experiments, MANet attains highest scores with significantly less experience samples. Additionally, the model shows higher performance compared to the Deep Q-network and the single attention model as benchmarks. Furthermore, we extend our model to attentive communication model for performing multi-agent cooperative tasks. In multi-agent cooperative task experiments, our model shows 20% faster learning than existing state-of-the-art model.

Plan Recognition is the problem of inferring the goals and plans of an agent given a set of observations. In Multi-Agent Plan Recognition (MAPR) the task is extended to inferring the goals and plans of multiple agents. Previous MAPR approaches have largely focused on recognizing team structures and behaviors, given perfect and complete observations of the actions of individual agents. However, in many real-world applications of MAPR, observations are unreliable or missing; they are often over properties of the world rather than actions; and the observations that are made may not be explainable by the agents' goals and plans. Moreover, the actions of the agents could be durative or concurrent. In this paper, we address the problem of MAPR with temporal actions and with observations that can be unreliable, missing or unexplainable. To this end, we propose a multi-step compilation technique that enables the use of AI planning for the computation of the posterior probabilities of the possible goals. In addition, we propose a set of novel benchmarks that enable a standard evaluation of solutions that address the MAPR problem with temporal actions and such observations. We present results of an experimental evaluation on this set of benchmarks, using several temporal and diverse planners.

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

This work addresses the problem of choosing an appropriate plan for achieving a goal in any realistic complex situation where an agent has to respond and act upon uncertain and/or an unknown information. We use the belief-desire-intention (BDI) model, a popular model for developing agents. The flexibility of choosing among different plans to achieve a desired goal is one of the benefits of this model. This paper describes a particular algorithm for selecting the most appropriate plan. Since the agent may have to reason with incomplete or uncertain information, we explore how to integrate probabilities in the agent model for taking an appropriate action and keeping the system behavior within acceptable boundaries and compliance to acceptable norms. Considering the uncertainty of the current state of the environment, this process relies on probability and utility theory. The plan selection algorithm has been implemented with Jadex