Multiscale computation and dynamic attention in biological and artificial intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence

Empowerment As Replacement for the Three Laws of Robotics

© 2017 Salge and Polani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.The greater ubiquity of robots creates a need for generic guidelines for robot behaviour. We focus less on how a robot can technically achieve a predefined goal, and more on what a robot should do in the first place. Particularly, we are interested in the question how a heuristic should look like which motivates the robot's behaviour in interaction with human agents. We make a concrete, operational proposal as to how the information-theoretic concept of empowerment can be used as a generic heuristic to quantify concepts such as self-preservation, protection of the human partner and responding to human actions. While elsewhere we studied involved single-agent scenarios in detail, here we present proof-of-principle scenarios demonstrating how empowerment interpreted in light of these perspectives allows one to specify core concepts with a similar aim as Asimov's Three Laws of Robotics in an operational way. Importantly, this route does not depend on having to establish an explicit verbalized understanding of human language and conventions in the robots. Also, it incorporates the ability to take into account a rich variety of different situations and types of robotic embodiment.Peer reviewe

Emotion modelling with human belief revision in computer games

163 leaves : ill. (some col) ; 29 cm.Includes abstract and appendices.Includes bibliographical references (leaves 149-157).Emotion modelling is receiving more and more attention from various fields, e.g. cognitive science, psychology, computer science and neuroscience. Most of these fields share the common research consensus that emotion can be beneficial to human's mental activities. This thesis is also grounded on the same consensus and makes further validations based on the following two hypotheses: One is emotional agents in games should behave more like human beings than emotionless agents; the other is that agents having full emotional architecture should obtain better playing performance than agents with only partial architecture. Based on theoretical support, the author further hypothesizes that peoples' long term belief can be one of the sources to release complex emotions. The experiment result suggests the emotional agents did perform significantly better than emotionless ones, but it was unable to significantly reflect the advantages from fully structured emotional agents over the ones of the partial architecture

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Towards Deep Learning with Competing Generalisation Objectives

The unreasonable effectiveness of Deep Learning continues to deliver unprecedented Artificial Intelligence capabilities to billions of people. Growing datasets and technological advances keep extending the reach of expressive model architectures trained through efficient optimisations. Thus, deep learning approaches continue to provide increasingly proficient subroutines for, among others, computer vision and natural interaction through speech and text. Due to their scalable learning and inference priors, higher performance is often gained cost-effectively through largely automatic training. As a result, new and improved capabilities empower more people while the costs of access drop. The arising opportunities and challenges have profoundly influenced research. Quality attributes of scalable software became central desiderata of deep learning paradigms, including reusability, efficiency, robustness and safety. Ongoing research into continual, meta- and robust learning aims to maximise such scalability metrics in addition to multiple generalisation criteria, despite possible conflicts. A significant challenge is to satisfy competing criteria automatically and cost-effectively. In this thesis, we introduce a unifying perspective on learning with competing generalisation objectives and make three additional contributions. When autonomous learning through multi-criteria optimisation is impractical, it is reasonable to ask whether knowledge of appropriate trade-offs could make it simultaneously effective and efficient. Informed by explicit trade-offs of interest to particular applications, we developed and evaluated bespoke model architecture priors. We introduced a novel architecture for sim-to-real transfer of robotic control policies by learning progressively to generalise anew. Competing desiderata of continual learning were balanced through disjoint capacity and hierarchical reuse of previously learnt representations. A new state-of-the-art meta-learning approach is then proposed. We showed that meta-trained hypernetworks efficiently store and flexibly reuse knowledge for new generalisation criteria through few-shot gradient-based optimisation. Finally, we characterised empirical trade-offs between the many desiderata of adversarial robustness and demonstrated a novel defensive capability of implicit neural networks to hinder many attacks simultaneously

CLA GAME REPORT : Causal Layered Analysis Game on Neo-Carbon Energy Scenarios

This report describes the process and results of Causal Layered Analysis (CLA) game session held on 11 June 2015 in the International Conference of “Futures Studies Tackling Wicked Problems”, organized by Finland Futures Research Centre in Turku. CLA is a qualitative method developed by Sohail Inayatullah. It enables a deeper investigation of alternative futures through an analysis done through four different layers; litany, system, worldview and myth/metaphor. The method functioned as the theoretical framework for the experimental game session conducted during the conference. The aim of the CLA game was to elaborate on and experiment with four transformational scenarios being developed by Finland Futures Research Centre for an ongoing Neo-Carbon Energy project. The tentative scenarios were constructed in the futures-orientated part of the project “Neo-Carbon Enabling Neo-Growth Society – Transformative Scenarios 2050”

Proceedings, MSVSCC 2015

The Virginia Modeling, Analysis and Simulation Center (VMASC) of Old Dominion University hosted the 2015 Modeling, Simulation, & Visualization Student capstone Conference on April 16th. The Capstone Conference features students in Modeling and Simulation, undergraduates and graduate degree programs, and fields from many colleges and/or universities. Students present their research to an audience of fellow students, faculty, judges, and other distinguished guests. For the students, these presentations afford them the opportunity to impart their innovative research to members of the M&S community from academic, industry, and government backgrounds. Also participating in the conference are faculty and judges who have volunteered their time to impart direct support to their students’ research, facilitate the various conference tracks, serve as judges for each of the tracks, and provide overall assistance to this conference. 2015 marks the ninth year of the VMASC Capstone Conference for Modeling, Simulation and Visualization. This year our conference attracted a number of fine student written papers and presentations, resulting in a total of 51 research works that were presented. This year’s conference had record attendance thanks to the support from the various different departments at Old Dominion University, other local Universities, and the United States Military Academy, at West Point. We greatly appreciated all of the work and energy that has gone into this year’s conference, it truly was a highly collaborative effort that has resulted in a very successful symposium for the M&S community and all of those involved. Below you will find a brief summary of the best papers and best presentations with some simple statistics of the overall conference contribution. Followed by that is a table of contents that breaks down by conference track category with a copy of each included body of work. Thank you again for your time and your contribution as this conference is designed to continuously evolve and adapt to better suit the authors and M&S supporters. Dr.Yuzhong Shen Graduate Program Director, MSVE Capstone Conference Chair John ShullGraduate Student, MSVE Capstone Conference Student Chai

The Role of Emotions in Autonomous Social Agents

The holy grail of both AI and cognitive science is human-level intelligence. Whereas AI relies on computer algorithms to simulate human abilities, cognitive scientists investigate the brain to understand the underlying mechanisms. For most of human history, emotions were thought to be nothing more than disturbances for cognition. Therefore, they were usually ostracized from research on intelligence. As a result, cognitive architectures only partially include emotions in their design, if at all. Recently, though, it was discovered that emotions and cognition are in fact inter-dependent systems. Consequently, before being able to fully replicate human-level intelligence, it is necessary to understand the concept of emotions and its many roles within the brain. In this thesis, working around the lack of definition for emotion, I show that emotions inform the brain as to the nature of a given situation and guide the decision-making process, to increase the survival potential of virtual agents. In particular ProtoEmo, an architecture replicating the circuits found at the base of the forebrain, is shown to have the ability to detect stimuli relevant to the survival of virtual agents. Hence, it outperforms other emotional agents in terms of survival capabilities, which are measured by the size of the remaining population at the end of a resource foraging task. PrimEmo, the architecture born from the integration of ProtoEmo with standard models of the reward and decision-making systems in the brain, displays survival capabilities similar to the advantage actor-critic algorithm. PrimEmo also shows promises for supporting primitive emotions characterized by their level of arousal and valence. After further refinement, PrimEmo could replace the core decision-making module of a cognitive architecture, such as ACT-R or SOAR. Not only would it confer survival capabilities to the architecture, it would also allow for the possibility of investigating full-fledged emotions, and even emotional expression

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia