Dynamic mapping strategies for interactive art installations: an embodied combined HCI HRI HHI approach

This paper proposes a theoretical framework for dealing with the paradigm of interactivity in new media art, and how the broad use of the term in different research fields can lead to some misunderstandings. The paper addresses a conceptual view on how we can implement interaction in new media art from an embodied approach that unites views from HCI, HRI and HHI. The focus is on an intuitive mapping of a multitude of sensor data and to extend upon this using the paradigm of (1) finite state machines (FSM) to address dynamic mapping strategies, (2) mediality to address aisthesis and (3) embodiment to address valid mapping strategies originated from natural body movements. The theory put forward is illustrated by a case study

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

COMBINED ARTIFICIAL INTELLIGENCE BEHAVIOUR SYSTEMS IN SERIOUS GAMING

This thesis proposes a novel methodology for creating Artificial Agents with semi-realistic behaviour, with such behaviour defined as overcoming common limitations of mainstream behaviour systems; rapidly switching between actions, ignoring “obvious” event priorities, etc. Behaviour in these Agents is not fully realistic as some limitations remain; Agents have a “perfect” knowledge about the surrounding environment, and an inability to transfer knowledge to other Agents (no communication). The novel methodology is achieved by hybridising existing Artificial Intelligence (AI) behaviour systems. In most artificial agents (Agents) behaviour is created using a single behaviour system, whereas this work combines several systems in a novel way to overcome the limitations of each. A further proposal is the separation of behavioural concerns into behaviour systems that are best suited to their needs, as well as describing a biologically inspired memory system that further aids in the production of semi-realistic behaviour. Current behaviour systems are often inherently limited, and in this work it is shown that by combining systems that are complementary to each other, these limitations can be overcome without the need for a workaround. This work examines in detail Belief Desire Intention systems, as well as Finite State Machines and explores how these methodologies can complement each other when combined appropriately. By combining these systems together a hybrid system is proposed that is both fast to react and simple to maintain by separating behaviours into fast-reaction (instinctual) and slow-reaction (behavioural) behaviours, and assigning these to the most appropriate system. Computational intelligence learning techniques such as Artificial Neural Networks have been intentionally avoided, as these techniques commonly present their data in a “black box” system, whereas this work aims to make knowledge explicitly available to the user. A biologically inspired memory system has further been proposed in order to generate additional behaviours in Artificial Agents, such as behaviour related to forgetfulness. This work explores how humans can quickly recall information while still being able to store millions of pieces of information, and how this can be achieved in an artificial system

Comprehensive Believable Non Player Characters Creation and Management Tools for Emergent Gameplay

This thesis seeks a way to integrate popular psychosocial components required for believability to build a believable Non Player Characters (NPCs) model using the techniques of emergence. The believable NPCs model is scalable in terms of psychosocial models, customizable, flexible and data-driven. Comprehensive believable NPCs creation and management tools were developed to compose, generate, and maintain the system configuration data, as well as NPC profile data, using XML. Furthermore, a run-time prototype has been developed based on our proposed model to test its effectiveness. The prototype has also been evaluated for believable emergent behaviours in different social scenarios

Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem

Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction

The financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed

Artificial Intelligence for Small Satellites Mission Autonomy

Space mission engineering has always been recognized as a very challenging and innovative branch of engineering: since the beginning of the space race, numerous milestones, key successes and failures, improvements, and connections with other engineering domains have been reached. Despite its relative young age, space engineering discipline has not gone through homogeneous times: alternation of leading nations, shifts in public and private interests, allocations of resources to different domains and goals are all examples of an intrinsic dynamism that characterized this discipline. The dynamism is even more striking in the last two decades, in which several factors contributed to the fervour of this period. Two of the most important ones were certainly the increased presence and push of the commercial and private sector and the overall intent of reducing the size of the spacecraft while maintaining comparable level of performances. A key example of the second driver is the introduction, in 1999, of a new category of space systems called CubeSats. Envisioned and designed to ease the access to space for universities, by standardizing the development of the spacecraft and by ensuring high probabilities of acceptance as piggyback customers in launches, the standard was quickly adopted not only by universities, but also by agencies and private companies. CubeSats turned out to be a disruptive innovation, and the space mission ecosystem was deeply changed by this. New mission concepts and architectures are being developed: CubeSats are now considered as secondary payloads of bigger missions, constellations are being deployed in Low Earth Orbit to perform observation missions to a performance level considered to be only achievable by traditional, fully-sized spacecraft. CubeSats, and more in general the small satellites technology, had to overcome important challenges in the last few years that were constraining and reducing the diffusion and adoption potential of smaller spacecraft for scientific and technology demonstration missions. Among these challenges were: the miniaturization of propulsion technologies, to enable concepts such as Rendezvous and Docking, or interplanetary missions; the improvement of telecommunication state of the art for small satellites, to enable the downlink to Earth of all the data acquired during the mission; and the miniaturization of scientific instruments, to be able to exploit CubeSats in more meaningful, scientific, ways. With the size reduction and with the consolidation of the technology, many aspects of a space mission are reduced in consequence: among these, costs, development and launch times can be cited. An important aspect that has not been demonstrated to scale accordingly is operations: even for small satellite missions, human operators and performant ground control centres are needed. In addition, with the possibility of having constellations or interplanetary distributed missions, a redesign of how operations are management is required, to cope with the innovation in space mission architectures. The present work has been carried out to address the issue of operations for small satellite missions. The thesis presents a research, carried out in several institutions (Politecnico di Torino, MIT, NASA JPL), aimed at improving the autonomy level of space missions, and in particular of small satellites. The key technology exploited in the research is Artificial Intelligence, a computer science branch that has gained extreme interest in research disciplines such as medicine, security, image recognition and language processing, and is currently making its way in space engineering as well. The thesis focuses on three topics, and three related applications have been developed and are here presented: autonomous operations by means of event detection algorithms, intelligent failure detection on small satellite actuator systems, and decision-making support thanks to intelligent tradespace exploration during the preliminary design of space missions. The Artificial Intelligent technologies explored are: Machine Learning, and in particular Neural Networks; Knowledge-based Systems, and in particular Fuzzy Logics; Evolutionary Algorithms, and in particular Genetic Algorithms. The thesis covers the domain (small satellites), the technology (Artificial Intelligence), the focus (mission autonomy) and presents three case studies, that demonstrate the feasibility of employing Artificial Intelligence to enhance how missions are currently operated and designed

Autonomous characters in virtual environments: The technologies involved in artificial life and their affects on perceived intelligence and playability of computer games

Computer games are viewed by academics as un֊grounded hack and patch experiments. "The industry lacks the formalism and requirement for a "perfect" solution often necessary in the academic world " [Woob]. Academic Artifical Intelligence (AI) is often viewed as un-implementable and narrow minded by the majority of ทon-AI programmer. "Historically, AI tended to be focused, containing detailed problems and domain-specific techniques. This focus makes for easier study - or engineering - of particular solutions. " [СһаОЗ .By implementing several well known AI techniques into the same gaming environment and judging users reactions this project aims to make links between the academic nature of AI, as well as investigate the nature of practical implementation in a gaming environment. An online Java implemented version of the 1970'ร classic Space Invaders has been developed and tested, with the Aliens being controlled by 6 different approaches to modelling AI functions. In total information from 334 individuals games was recorded. Different types of games AI can create highly varied gaming experience as highlighted by the range of values and high standard deviation values seen in the results. The link between complex behaviour, complex control systems and perceived intelligence was not supported. A positive correlation identified between how fun the users found the game and how intelligent they perceived the Aliens to be, would seem to be logical. As games get visually more and more impressive, the need for intelligent characters cannot be denied because it is one of the few way in which games can set themselves apart from the competition. Conclusions identified that computer games must remain focussed on their end- goal, that of producing a fun game. Whilst complex and clever AI can help to achieve it, the AI itself can never overshadow the end result

Model of models -- Part 1

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.Comment: arXiv admin note: text overlap with arXiv:2301.1355