The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.

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.

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.

To ensure we do not forget relevant aspects of AI, we The field of Artificial Intelligence (AI) is fertile: it is at the present some key works which have already focused on same time the root of the dreams and deceptions of many defining (artificial) intelligence in Section 2. We then highlight people, a common feature in science fiction, and various the potential lack of cross-fertilisation they may be subject technical projects in many domains of application. Although to in Section 3 and consider the definition of human we may appreciate the rich emotions and ideas brought by expertise to draw a definition of human intelligence in Section a concept such as AI, some people are seriously working on 4. Next, we generalise these definitions to cover also artificial it in an attempt to produce autonomous agents able to meet agents in Section 5 and provide more details about the the various needs of different users. These projects, however, domain-generic data and processes of our definition of intelligence have faced several troubles and unfulfilled promises in in Section 6. We rely further on the expertise field in the history of the field, leading to shortenings of funding Section 7 by describing three kinds of measures of expertise, and years of research efforts lost (Franklin 2014). Despite mapping them to existing measures of intelligence, and suggesting the presence of "intrepid researchers" to advance the field, directions to investigate. Finally, Section 8 expands from an industrial point of view such projects were abandoned the discussion to a novel conception of the field of AI as a and considered as failures.

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.

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.

The Strong Stackelberg Equilibrium (SSE) has drawn extensive attention recently in several security domains, which optimizes the defender's random allocation of limited security resources. However, the SSE concept neglects the advantage of defender's strategic revelation of her private information, and overestimates the observation ability of the adversaries. In this paper, we overcome these restrictions and analyze the tradeoff between strategic secrecy and commitment in security games. We propose a Disguised-resource Security Game (DSG) where the defender strategically disguises some of her resources. We compare strategic information revelation with public commitment and formally show that they have different advantages depending the payoff structure. To compute the Perfect Bayesian Equilibrium (PBE), several novel approaches are provided, including basic MILP formulations with mixed defender strategy and compact representation, a novel algorithm based on support set enumeration, and an approximation algorithm for epsilon-PBE. Extensive experimental evaluation shows that both strategic secrecy and Stackelberg commitment are critical measures in security domain, and our approaches can solve PBE for realistic-sized problems with good enough and robust solution quality.

Title: What are the phenomena to be explained?

The interactive computation paradigm is reviewed and a particular example is extended to form the stochastic analog of a computational process via a transcription of a minimal Turing Machine into an equivalent asynchronous Cellular Automaton with an exponential waiting times distribution of effective transitions. Furthermore, a special toolbox for analytic derivation of recursive relations of important statistical and other quantities is introduced in the form of an Inductive Combinatorial Hierarchy.