Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

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Design Anthropological Futures

A major contribution to the field, this ground-breaking book explores design anthropology's focus on futures and future-making. Examining what design anthropology is and what it is becoming, the authors push the frontiers of the discipline and reveal both the challenges for and the potential of this rapidly growing transdisciplinary field. Divided into four sections – Ethnographies of the Possible, Interventionist Speculation, Collaborative Formation of Issues, and Engaging Things – the book develops readers' understanding of the central theoretical and methodological aspects of future knowledge production in design anthropology. Bringing together renowned scholars such as George Marcus and Alison Clarke with young experimental design anthropologists from countries such as Denmark, Sweden, Austria, Brazil, the UK, and the United States, the sixteen chapters offer an unparalleled breadth of theoretical reflections and rich empirical case studies. Written by those at the forefront of the field, Design Anthropological Futures is destined to become a defining text for this growing discipline. A unique resource for students, scholars, and practitioners in design anthropology, design, architecture, material culture studies, and related fields

Habits and goals in synergy: a variational Bayesian framework for behavior

How to behave efficiently and flexibly is a central problem for understanding biological agents and creating intelligent embodied AI. It has been well known that behavior can be classified as two types: reward-maximizing habitual behavior, which is fast while inflexible; and goal-directed behavior, which is flexible while slow. Conventionally, habitual and goal-directed behaviors are considered handled by two distinct systems in the brain. Here, we propose to bridge the gap between the two behaviors, drawing on the principles of variational Bayesian theory. We incorporate both behaviors in one framework by introducing a Bayesian latent variable called "intention". The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal. Building on this idea, we present a novel Bayesian framework for modeling behaviors. Our proposed framework enables skill sharing between the two kinds of behaviors, and by leveraging the idea of predictive coding, it enables an agent to seamlessly generalize from habitual to goal-directed behavior without requiring additional training. The proposed framework suggests a fresh perspective for cognitive science and embodied AI, highlighting the potential for greater integration between habitual and goal-directed behaviors

Enhancing and advancing the understanding and study of dreaming and memory consolidation: reflections, challenges, theoretical clarity, and methodological considerations

Empirical investigations that search for a link between dreaming and sleep-dependent memory consolidation have focused on testing for an association between dreaming of what was learned, and improved memory performance for learned material. Empirical support for this is mixed, perhaps owing to the inherent challenges presented by the nature of dreams, and methodological inconsistencies. The purpose of this paper is to address critically prevalent assumptions and practices, with the aim of clarifying and enhancing research on this topic, chiefly by providing a theoretical synthesis of existing models and evidence. Also, it recommends the method of Targeted Memory Reactivation (TMR) as a means for investigating if dream content can be linked to specific cued activations. Other recommendations to enhance research practice and enquiry on this subject are also provided, focusing on the HOW and WHY we search for memory sources in dreams, and what purpose (if any) they might serve

Neural prosthetics for paralysis : algorithms and low-power analog architectures for decoding neural signals

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Physics, 2007.Includes bibliographical references (leaves 119-122).This thesis develops a system for adaptively and automatically learning to interpret patterns of electrical activity in neuronal populations in a real-time, on-line fashion. The system is primarily intended to enable the long-term implantation of low-power, microchip-based recording and decoding hardware in the brains of human patients in order to treat neurologic disorders. The decoding system developed in the present work interprets neural signals from the parietal cortex encoding arm movement intention, suggesting that the system could function as the decoder in a neural prosthetic limb, potentially enabling a paralyzed person to control an artificial limb just as the natural one was controlled, through thought alone. The same decoder is also used to interpret the activity of a population of thalami neurons encoding head orientation in absolute space. The success of the decoder in that context motivates the development of a model of generalized place cells to explain how networks of neurons adapt the configurations of their receptive fields in response to new stimuli, learn to encode the structure of new parameter spaces, and ultimately retrace trajectories through such spaces in the absence of the original stimuli.(cont.) Qualitative results of this model are shown to agree with experimental observations. This combination of results suggests that the neural signal decoder is applicable to a broad scope of neural systems, and that a microchip-based implementation of the decoder based on the designs presented in this thesis could function as a useful investigational tool for experimental neuroscience and potentially as an implantable interpreter of simple thoughts and dreams.by Benjamin Isaac Rapoport.S.M

What is neurorepresentationalism?:From neural activity and predictive processing to multi-level representations and consciousness

This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body (Pennartz, 2015)

The evolution of foresight: What is mental time travel and is it unique to humans?

In a dynamic world, mechanisms allowing prediction of future situations can provide a selective advantage. We suggest that memory systems differ in the degree of flexibility they offer for anticipatory behavior and put forward a corresponding taxonomy of prospection. The adaptive advantage of any memory system can only lie in what it contributes for future survival. The most flexible is episodic memory, which we suggest is part of a more general faculty of mental time travel that allows us not only to go back in time, but also to foresee, plan, and shape virtually any specific future event. We review comparative studies and find that, in spite of increased research in the area, there is as yet no convincing evidence for mental time travel in nonhuman animals. We submit that mental time travel is not an encapsulated cognitive system, but instead comprises several subsidiary mechanisms. A theater metaphor serves as an analogy for the kind of mechanisms required for effective mental time travel. We propose that future research should consider these mechanisms in addition to direct evidence of future-directed action. We maintain that the emergence of mental time travel in evolution was a crucial step towards our current success