Bridging ACT-R and Project Malmo, developing models of behavior in complex environments

Cognitive architectures such as ACT-R provide a system for simulating the mind and human behavior. On their own they model decision making of an isolated agent. However, applying a cognitive architecture to a complex environment yields more interesting results about how people make decisions in more realistic scenarios. Furthermore, cognitive architectures enable researchers to study human behavior in dangerous tasks which cannot be tested because they would harm participants. Nonetheless, these architectures aren’t commonly applied to such environments as they don’t come with one. It is left to the researcher to develop a task environment for their model. The difficulty in creating one prevents cognitive architectures from being utilized in more advanced studies. This project aims to address that issue by building a bridge between ACT-R and Project Malmo, an artificial general intelligence test suite. The bridge facilitates easy integration of new missions by allowing researchers to specify how to create the world and update it without worrying about the overhead of Malmo. Furthermore, this study analyses how well ACT-R’s utility learning system will adapt in a complex environment. The Adaptive Gain Theory was implemented to improve how the system adapts by using task engagement, derived from measures of utility, to dynamically modify noise. The system was tested using a modified Symbolic Maze task. Tests revealed the parameters of the Adaptive Gain mechanism need to be refined to have a greater impact on model performance. Nonetheless, the bridge provides an interface for ACT-R to be used to study decision making in a complex environment. Improving the bridge will enable more advanced experiments to be conducted whilst improving the Adaptive Gain Theory implementation will move us one step closer to understanding everyday intelligent behavior

A hybrid cognitive architecture with primal affect and physiology

Though computational cognitive architectures have been used to study several processes associated with human behavior, the study of integration of affect and emotion in these processes has been relatively sparse. Theory from affective science and affective neuroscience can be used to systematically integrate affect into cognitive architectures, particularly in areas where cognitive system behavior is known to be associated with physiological structure and behavior. I introduce a unified theory and model of human behavior that integrates physiology and primal affect with cognitive processes in a cognitive architecture. This new architecture gives a more tractable, mechanistic way to simulate affect-cognition interactions to provide specific, quantitative predictions. It considers affect as a lower-level, functional process that interacts with cognitive processes (e.g., declarative memory) to result in emotional behavior. This formulation makes it more straightforward to connect these affective representations with other related moderating processes that may not specifically be considered as emotional (e.g., thirst or stress). An improved understanding of the architecture that constrains our behavior gives us a better opportunity to comprehend why we behave the way we do and how we can use this knowledge to recognize and construct a more ideal internal and external environment

Rethinking Pedagogy: Exploring the Potential of Digital Technology in Achieving Quality Education

(First Paragraph) The Mahatma Gandhi Institute of Education for Peace and Sustainable Development (MGIEP) is UNESCO’s Category 1 education Institute in the Asia-Pacific region devoted to education for peace and sustainable development, as enshrined in SDG Target 4.7. UNESCO MGIEP promotes the use of digital learning platforms where teachers and students can co-create and share a highly interactive learning experience. With the rise of the internet, there has been a proliferation of online content and digital resources intended to support teaching and learning, albeit widely varying in quality. Digital education media and resources, if carefully designed and implemented, have a significant potential to be mobilized on a massive scale to support transformative learning for building sustainable, flourishing societies

OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond

An introduction to the OpenCog Hyperon framework for Artificiai General Intelligence is presented. Hyperon is a new, mostly from-the-ground-up rewrite/redesign of the OpenCog AGI framework, based on similar conceptual and cognitive principles to the previous OpenCog version, but incorporating a variety of new ideas at the mathematical, software architecture and AI-algorithm level. This review lightly summarizes: 1) some of the history behind OpenCog and Hyperon, 2) the core structures and processes underlying Hyperon as a software system, 3) the integration of this software system with the SingularityNET ecosystem's decentralized infrastructure, 4) the cognitive model(s) being experimentally pursued within Hyperon on the hopeful path to advanced AGI, 5) the prospects seen for advanced aspects like reflective self-modification and self-improvement of the codebase, 6) the tentative development roadmap and various challenges expected to be faced, 7) the thinking of the Hyperon team regarding how to guide this sort of work in a beneficial direction ... and gives links and references for readers who wish to delve further into any of these aspects

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

The Use of Games and Crowdsourcing for the Fabrication-aware Design of Residential Buildings

State-of-the-art participatory design acknowledges the true, ill-defined nature of design problems, taking into account stakeholders' values and preferences. However, it overburdens the architect, who has to synthesize far more constraints into a one-of-a-kind design. Generative Design promises to equip architects with great power to standardize and systemize the design process. However, the common trap of generative design is trying to treat architecture simply as a tame problem. In this work, I investigate the use of games and crowdsourcing in architecture through two sets of explorative questions. First, if everyone can participate in the network-enabled creation of the built environment, what role will they play? And what tools will they need to enable them? And second, if anyone can use digital fabrication to build any building, how will we design it? What design paradigms will govern this process? I present a map of design paradigms that lie at the intersections of Participatory Design, Generative Design, Game Design, and Crowd Wisdom. In four case studies, I explore techniques to employ the practices from the four fields in the service of architecture. Generative Design can lower the difficulty of the challenge to design by automating a large portion of the work. A newly formulated, unified taxonomy of generative design across the disciplines of architecture, computer science, and computer games builds the base for the use of algorithms in the case studies. The work introduces Playable Voxel-Shape Grammars, a new type of generative technique. It enables Game Design to guide participants through a series of challenges, effectively increasing their skills by helping them understand the underlying principles of the design task at hand. The use of crowdsourcing in architecture can mean thousands of architects creating content for a generative design system, to expand and open up its design space. Crowdsourcing can also be about millions of people online creating designs that an architect or a homeowner can refer to increase their understanding of the complex issues at hand in a given design project and for better decision making. At the same time, game design in architecture helps find the balance between algorithmically exploring pre-defined design alternatives and open-ended, free creativity. The research reveals a layered structure of entry points for crowd-contributed content as well as the granular nature of authorship among four different roles: non-expert stakeholders, architects, the crowd, and the tool-makers

Reconfiguring Human, Nonhuman and Posthuman in Literature and Culture

The time has come for human cultures to seriously think, to severely conceptualize, and to earnestly fabulate about all the nonhuman critters we share our world with, and to consider how to strive for more ethical cohabitation. Reconfiguring Human, Nonhuman and Posthuman in Literature and Culture tackles this severe matter within the framework of literary and cultural studies. The emphasis of the inquiry is on the various ways actual and fictional nonhumans are reconfigured in contemporary culture – although, as long as the domain of nonhumanity is carved in the negative space of humanity, addressing these issues will inevitably clamor for the reconfiguration of the human as well

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.https://digitalcommons.unomaha.edu/isqafacbooks/1000/thumbnail.jp

xxAI - Beyond Explainable AI

This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science