Experimental Trials Based on a Neocortex-based Adaptive System Pattern

AbstractThis paper proposes a general design pattern for building adaptive systems. The Neocortex Adaptive System Pattern (NASP) architecture is an adaptive decision-making architecture. It is derived from the physical architecture observed within the neocortex of a primate brain. This architectural pattern is used as a basis to provide necessary functions to adaptive systems, allowing different adaptive system components with different methodologies and techniques to coexist and cooperate within a single system. Properties of the NASP are illustrated using an agent-based simulation experiment framework composed of simulated tank vs. tank game. This study supplies experimental results that compare adaptive decisions based on accuracy and timeliness. It shows that a more accurate decision may in fact be the less optimal one due to time constraints. The experimentation results suggest that multi-system adaptation can increase system performance, and learned information can identify time frames when an adaptation can increase system performance. The practice of designing and building agent based systems shares many principles and approaches with the NASP. An agent-based architecture has a common environment that is utilized to share the state of the system with member agents. It contains autonomous entities that communicate with each other in order to perform their designed functions. A unique contribution of the NASP approach over other research is to add the ability for different agents to create alternative courses of action and controls such as rule-based, neural, or Bayesian that are used to choose from those alternatives based on their latest information. While counter intuitive, the findings suggest that increased performance in this combatant domain suggest that earlier adaptations, using less information, improve the performance of the adaptive system. The paper provides a literature review of relevant neuroscience literature that describes the parallels between the architecture of the neocortex and NASP. The paper discusses the simulation experiments and associated results that illustrate how tradeoffs between information completeness and timeliness affect system performance within a NASP-based system

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)

This paper is about partitioning in parallel and distributed simulation. That means decomposing the simulation model into a numberof components and to properly allocate them on the execution units. An adaptive solution based on self-clustering, that considers both communication reduction and computational load-balancing, is proposed. The implementation of the proposed mechanism is tested using a simulation model that is challenging both in terms of structure and dynamicity. Various configurations of the simulation model and the execution environment have been considered. The obtained performance results are analyzed using a reference cost model. The results demonstrate that the proposed approach is promising and that it can reduce the simulation execution time in both parallel and distributed architectures

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Extended-XRI Body Interfaces for Hyper-Connected Metaverse Environments

Hybrid mixed-reality (XR) internet-of-things (IoT) research, here called XRI, aims at a strong integration between physical and virtual objects, environments, and agents wherein IoT-enabled edge devices are deployed for sensing, context understanding, networked communication and control of device actuators. Likewise, as augmented reality systems provide an immersive overlay on the environments, and virtual reality provides fully immersive environments, the merger of these domains leads to immersive smart spaces that are hyper-connected, adaptive and dynamic components that anchor the metaverse to real-world constructs. Enabling the human-in-the-loop to remain engaged and connected across these virtual-physical hybrid environments requires advances in user interaction that are multi-dimensional. This work investigates the potential to transition the user interface to the human body as an extended-reality avatar with hybrid extended-body interfaces that can interact both with the physical and virtual sides of the metaverse. It contributes: i) an overview of metaverses, XRI, and avatarization concepts, ii) a taxonomy landscape for extended XRI body interfaces, iii) an architecture and potential interactions for XRI body designs, iv) a prototype XRI body implementation based on the architecture, v) a design-science evaluation, toward enabling future design research directions

Towards homeostatic architecture: simulation of the generative process of a termite mound construction

This report sets out to the theme of the generation of a ‘living’, homeostatic and self-organizing architectural structure. The main research question this project addresses is what innovative techniques of design, construction and materials could prospectively be developed and eventually applied to create and sustain human-made buildings which are mostly adaptive, self-controlled and self-functioning, without option to a vast supply of materials and peripheral services. The hypothesis is that through the implementation of the biological building behaviour of termites, in terms of collective construction mechanisms that are based on environmental stimuli, we could achieve a simulation of the generative process of their adaptive structures, capable to inform in many ways human construction. The essay explicates the development of the 3-dimensional, agent-based simulation of the termite collective construction and analyzes the results, which involve besides physical modelling of the evolved structures. It finally elucidates the potential of this emerging and adaptive architectural performance to be translated to human practice and thus enlighten new ecological engineering and design methodologies