The Contemporary Craft of Creating Characters Meets Today's Cognitive Architectures

2016 ◽  
pp. 151-180 ◽  
Author(s):  
Selmer Bringsjord ◽  
John Licato ◽  
Alexander Bringsjord
Keyword(s):  
Cognitive Architecture ◽  
Software Tools ◽  
Cognitive Architectures ◽  
Robust Modeling ◽  
Character Design

What does the contemporary craft of character design (by human authors), which is beyond the reach of foreseeable AI, and which isn't powered by any stunning, speculative, AI-infused technology (immersive or otherwise), but is instead aided by tried-and-true “AI-less” software tools and immemorial techniques that are still routinely taught today, imply with respect to today's computational cognitive architectures? This chapter narrows the scope of this large question, and argues that at present, perhaps only the cognitive architecture CLARION can represent and reason over knowledge at a level of logical expressivity sufficient to capture such characters, along with the robust modeling implied by contemporary story and character design.

Articulating the Role of Artificial Intelligence in Collective Intelligence: A Transactive Systems Framework

2021 ◽  
Vol 65 (1) ◽  
pp. 670-674
Author(s):  
Pranav Gupta ◽  
Anita Williams Woolley
Keyword(s):  
Artificial Intelligence ◽  
Collective Memory ◽  
Collective Intelligence ◽  
Cognitive Architecture ◽  
Cognitive Resources ◽  
Human Society ◽  
Cognitive Architectures ◽  
The Core ◽  
Systems Framework

Human society faces increasingly complex problems that require coordinated collective action. Artificial intelligence (AI) holds the potential to bring together the knowledge and associated action needed to find solutions at scale. In order to unleash the potential of human and AI systems, we need to understand the core functions of collective intelligence. To this end, we describe a socio-cognitive architecture that conceptualizes how boundedly rational individuals coordinate their cognitive resources and diverse goals to accomplish joint action. Our transactive systems framework articulates the inter-member processes underlying the emergence of collective memory, attention, and reasoning, which are fundamental to intelligence in any system. Much like the cognitive architectures that have guided the development of artificial intelligence, our transactive systems framework holds the potential to be formalized in computational terms to deepen our understanding of collective intelligence and pinpoint roles that AI can play in enhancing it.

scholarly journals A Hybrid Approach to Cognition in Radars

2018 ◽  
Vol 68 (2) ◽  
pp. 183 ◽  
Author(s):  
M. Justin Sagayaraj ◽  
Jithesh V. ◽  
J.B. Singh ◽  
Dange Roshani ◽  
K.G. Srinivasa
Keyword(s):  
Deep Learning ◽  
Cognitive Architecture ◽  
Hybrid Approach ◽  
Experimental Studies ◽  
Vital Role ◽  
Cognitive Architectures ◽  
Radar Systems ◽  
Learning Techniques ◽  
Time Calculation ◽  
High Resolution Radar

In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.

scholarly journals MERLIN a Cognitive Architecture for Service Robots

2020 ◽  
Vol 10 (17) ◽  
pp. 5989
Author(s):  
Miguel Á. González-Santamarta ◽  
Francisco J. Rodríguez-Lera ◽  
Claudia Álvarez-Aparicio ◽  
Ángel M. Guerrero-Higueras ◽  
Camino Fernández-Llamas
Keyword(s):  
Operating System ◽  
Cognitive Architecture ◽  
Public Spaces ◽  
Service Robots ◽  
Hybrid Architecture ◽  
Cognitive Architectures ◽  
Robot Operating System ◽  
Robot Behavior ◽  
Short And Long Term

Many social robots deployed in public spaces hide hybrid cognitive architectures for dealing with daily tasks. Mostly, two main blocks sustain these hybrid architectures for robot behavior generation: deliberative and behavioral-based mechanisms. Robot Operating System offers different solutions for implementing these blocks, however, some issues arise when both are released in the robot. This paper presents a software engineering approach for normalizing the process of integrating them and presenting them as a fully cognitive architecture named MERLIN. Providing implementation details and diagrams for established the architecture, this research tests empirically the proposed solution using a variation from the challenge defined in the SciRoc @home competition. The results validate the usability of our approach and show MERLIN as a hybrid architecture ready for short and long-term tasks, showing better results than using a by default approach, particularly when it is deployed in highly interactive scenarios.

Interfacing the Modifiable Multitasking Environment with ACT-R for Computational Cognitive Modeling of Complex Tasks

2018 ◽  
Vol 62 (1) ◽  
pp. 316-320
Author(s):  
Leslie M. Blaha ◽  
Leif Carlsen ◽  
Tim Halverson ◽  
Brad Reynolds
Keyword(s):  
Open Source ◽  
User Interfaces ◽  
Cognitive Modeling ◽  
Cognitive Architecture ◽  
Software Tools ◽  
Graphical User Interfaces ◽  
Complex Tasks ◽  
Software Packages ◽  
Baseline Task ◽  
Computational Cognitive Modeling

We demonstrate a set of software tools designed to facilitate computational cognitive modeling of multitasking performance. The Modifiable Multitasking Environment (ModME) offers a flexible, browser-based platform for creating multitasking experiments. Simplified Interfacing for Modeling Cognition–JavaScript (SIMCog-JS) provides communication between the browser-based experiments in ModME and the Java implementation of the ACT-R cognitive architecture. The baseline configuration of these software packages enables an ACT-R model to perform pilot-like multitasking in the modified Multi-Attribute Task Battery, which is implemented as the baseline task available in ModME. We show how this combination facilitates the development of models for assessing multitasking workload. In this demonstration, we will explain the software packages and allow attendees to interact with system elements, particularly the ModME graphical user interfaces. All software is available open source for attendees to try themselves.

scholarly journals Development of a structural diagram of a cognitive transport system

2021 ◽  
pp. 166-174
Author(s):  
С.А. Селиверстов ◽  
Я.А. Селиверстов ◽  
А.Г. Котенко ◽  
О.Ю. Лукомская ◽  
Н.В. Шаталова ◽  
...  
Keyword(s):  
Transport System ◽  
Intelligent Systems ◽  
Cognitive Architecture ◽  
Unmanned Vehicles ◽  
Transport Systems ◽  
Structural Diagram ◽  
Cognitive Architectures ◽  
Transport Control ◽  
The Subject ◽  
The Brain

Развитие технологий проектирования интеллектуальных систем, использующих алгоритмы схожие с мыслительной обработкой мозга, стимулируют поиски новых подходов для создания искусственного интеллекта человеческого уровня. Для выполнения столь сложных задач используются когнитивные архитектуры, представляя собой следующий уровень развития разнородных процессов интеллектуализации. В данной статье под практической призмой интеллектуализации транспортных систем исследуется процесс развития современных когнитивных архитектур. Предметом исследования является разработка структурной схемы когнитивной транспортной системы. Для выполнения этой задачи исследуются новые парадигмы когнитивного управления, расширяющие подходы к вычислительному интеллекту. Уточняется понятие когнитивной архитектуры. Исследуются современные работы в области каталогизации когнитивных архитектур. Выявляются критерии оценки когнитивных архитектур. Подробно рассматриваются и анализируются когнитивные архитектуры 4D / RCS, ALLIANCE, LIDA, использующиеся в современных системах управления беспилотным транспортом и роботами. Выявляются положительные обобщенные факторы, направленные на эффективность когнитивной архитектуры. Разрабатывается структурная схема когнитивной транспортной системы, основные подсистемы которой включают когнитивное управление транспортом, когнитивные транспортные коммуникации, когнитивные транспортные средства. Описывается структура слоев. Отмечаются преимущества, в том числе бионинспирированность, модульность, объектно-ориентированность, параллелизм, возможность использования интеллектуальных методов обучения. The development of technologies for the design of intelligent systems using algorithms similar to mental processing of the brain stimulates the search for new approaches to create artificial intelligence at the human level. To perform such complex tasks, cognitive architectures are used, representing the next level of development of heterogeneous intellectualization processes. In this article, under the practical prism of intellectualization of transport systems, the process of development of modern cognitive architectures is investigated. The subject of the research is the development of a structural diagram of the cognitive transport system. To accomplish this task, new paradigms of cognitive control are being investigated, expanding approaches to computational intelligence. The concept of cognitive architecture is clarified. Examines current work in the field of cataloging cognitive architectures. Criteria for assessing cognitive architectures are identified. Cognitive architectures 4D / RCS, ALLIANCE, LIDA, used in modern control systems for unmanned vehicles and robots, are considered and analyzed in detail. We identify positive generalized factors aimed at the effectiveness of cognitive architecture. A structural diagram of the cognitive transport system is being developed, the main subsystems of which include cognitive transport control, cognitive transport communications, and cognitive vehicles. The structure of the layers is described. The advantages are noted, including bioninspiration, modularity, object-orientation, parallelism, the possibility of using intelligent teaching methods.

scholarly journals Animating Cognitive Models and Architectures: A Rule-Based Approach

10.29007/wjwz ◽  
2018 ◽  
Author(s):  
Nada Sharaf ◽  
Slim Abdennadher ◽  
Thom Fruehwirth ◽  
Daniel Gall
Keyword(s):  
Intelligent Agents ◽  
Computational Models ◽  
Cognitive Architecture ◽  
Proof Of Concept ◽  
Cognitive Agent ◽  
Cognitive Architectures ◽  
Learning Problem ◽  
Rule Based ◽  
Computational Psychology ◽  
Rule Based Approach

Computational psychology provides computational models exploring different aspects of cognition. A cognitive architecture includes the basic aspects of any cognitive agent. It consists of different correlated modules. In general, cognitive architectures provide the needed layouts for building intelligent agents. The paper presents the a rule-based approach to visually animate the simulations of models done through cognitive architectures. As a proof of concept, simulations through Adaptive Control of Thought-Rational (ACT-R) were animated. ACT-R is a well-known cognitive architecture. It was deployed to create models in different fields including, among others, learning, problem solving and languages.

Toward an Integrated Model of Driver Behavior in Cognitive Architecture

2001 ◽  
Vol 1779 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Dario D. Salvucci ◽  
Erwin R. Boer ◽  
Andrew Liu
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Cognitive Architecture ◽  
Driver Model ◽  
Behavioral Variability ◽  
Level Control ◽  
Cognitive Architectures ◽  
Computational Framework ◽  
Lane Changes ◽  
Motor Processes

Driving is a multitasking activity that requires drivers to manage their attention among various driving- and non-driving-related tasks. When one models drivers as continuous controllers, the discrete nature of drivers’ control actions is lost and with it an important component for characterizing behavioral variability. A proposal is made for the use of cognitive architectures for developing models of driver behavior that integrate cognitive and perceptual-motor processes in a serial model of task and attention management. A cognitive architecture is a computational framework that incorporates built-in, well-tested parameters and constraints on cognitive and perceptual-motor processes. All driver models implemented in a cognitive architecture necessarily inherit these parameters and constraints, resulting in more predictive and psychologically plausible models than those that do not characterize driving as a multitasking activity. These benefits are demonstrated with a driver model developed in the ACT-R cognitive architecture. The model is validated by comparing its behavior to that of human drivers navigating a four-lane highway with traffic in a fixed-based driving simulator. Results show that the model successfully predicts aspects of both lower-level control, such as steering and eye movements during lane changes, and higher-level cognitive tasks, such as task management and decision making. Many of these predictions are not explicitly built into the model but come from the cognitive architecture as a result of the model’s implementation in the ACT-R architecture.

Cognitive architecture enables comprehensive predictive models of visual search

2017 ◽  
Vol 40 ◽  
Author(s):  
David E. Kieras ◽  
Anthony Hornof
Keyword(s):  
Visual Search ◽  
Predictive Models ◽  
Cognitive Architecture ◽  
Cognitive Architectures ◽  
Related Literature ◽  
Simple Demonstration

AbstractWith a simple demonstration model, Hulleman & Olivers (H&O) effectively argue that theories of visual search need an overhaul. We point to related literature in which visual search is modeled in even more detail through the use of computational cognitive architectures that incorporate fundamental perceptual, cognitive, and motor mechanisms; the result of such work thus far bolsters their arguments considerably.

Personality-Based Cognitive Design of Characters in Virtual Environments

2016 ◽  
pp. 124-150 ◽  
Author(s):  
Maryam Saberi
Keyword(s):  
Cognitive Architecture ◽  
Emotional Reactions ◽  
Cognitive Architectures ◽  
Reasoning Abilities ◽  
The World ◽  
The One ◽  
Deliberative Reasoning ◽  
High Level ◽  
And Coping ◽  
Different Levels

Personality-based cognitive architectures should yield consistent patterns of behaviour through personality traits that have a modulatory influence at different levels: These factors affect, on the one hand, high-level components such as ‘emotional reactions' and ‘coping behaviour', and on the other hand, low-level parameters such as the ‘speed of movements and repetition of gestures. In our hybrid cognitive architecture, a deliberative reasoning about the world (e.g. strategies and goals of the 3D character) is combined with dynamic real-time response to the environment's changes and sensors' input (e.g. emotional changes). Hybrid system copes dynamically with changes in the environment, and is complicated enough to have reasoning abilities. Designing a cognitive architecture that gives the impression of personality to 3D agents can be a tremendous help making 3D characters more engaging and successful in interactions with humans.

Virtual Soar-Agent Implementations

2020 ◽  
pp. 485-508
Author(s):  
Jeremy Owen Turner
Keyword(s):  
Virtual Environments ◽  
Cognitive Architecture ◽  
Qualitative Approach ◽  
Virtual Agents ◽  
Virtual Agent ◽  
Cognitive Architectures ◽  
The Third ◽  
Agent Design ◽  
The Future ◽  
Technical Details

This chapter provides a brief overview of those virtual agent implementations directly inspired by the cognitive architecture: Soar. This chapter will take a qualitative approach to discussing examples of virtual Soar-agents. Finally, this chapter will speculate on the future of Soar virtual characters. The goals of this chapter are sixfold. The first goal is to explain why cognitive architectures are becoming increasingly important to virtual agent design(s). The second goal is to convey why this chapter focuses exclusively on virtual agents that utilize the Soar architecture. The third goal is to explore some of Soar's technical details. The fourth goal is to showcase a few diverse examples where Soar is beginning to have a design impact on virtual agents. The fifth goal addresses Soar's limitations – when applied to agent design in virtual environments. The final goal speculates on ways Soar can be expanded for virtual agent design(s) in the future.

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