1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
Introduction to cognitive architectures, with a focus on those that have been implemented as software. Differentiates architectures from models and theories.
Cognitive architecture aims to model human cognition through computational processes that account for perception, reasoning, learning, and other cognitive functions. It proposes blueprint models for intelligent agents and artificial consciousness. Key aspects of cognitive architectures include modeling multiple aspects of cognition through a unified theory, accounting for limitations in human cognition, and demonstrating robust and flexible behavior over time through learning.
The document discusses object-oriented modeling and design. It describes object-oriented design as planning a system of interacting objects to solve software problems using concepts like data abstraction, polymorphism, and data hiding. It outlines the object-oriented design process, including identifying classes and objects, defining attributes and relationships, and specifying interfaces. The benefits of object-oriented design are its reusability and mapping to real-world entities.
John Laird, University of Michigan, presentation at Cognitive Systems Institute Speaker Series on "A Cognitive Architecture Approach to Interactive Task Learning"
This document discusses cognitive science research in virtual worlds. It summarizes different cognitive architecture approaches like symbolic, emergent, and hybrid architectures. It also discusses memory structures and applications of cognitive architectures like Soar and OpenCog Prime. Finally, it considers how cognitive science experiments could be conducted in virtual worlds and how results could potentially transfer learning to embodied artificial agents.
The document discusses cognitive architectures, which are engineering approaches for modeling cognitive systems like humans. It notes that cognitive architectures aim to provide a unified set of mechanisms to explain various cognitive functions like language, problem solving, dreaming, goal-directed behavior, symbol usage, and learning. The document then reviews several specific cognitive architectures, including Soar, ACT-R, LIDA, and 4CAPS. It also discusses challenges in creating cognitive architectures that integrate symbolic and sub-symbolic approaches and can be implemented on neural hardware at large scales.
1. The document discusses multiple representations in human cognition and cognitive architectures. It focuses on visual mental imagery and how cognitive models can incorporate different representational formats like diagrams, images, and symbols.
2. Current cognitive architectures mainly use symbolic representations which are insufficient for modeling visual imagery. A few models employ array-based representations to better capture spatial reasoning and imagery abilities.
3. For cognitive models to exhibit human-level intelligence, they need mechanisms for flexibly selecting and coordinating multiple internal and external representations.
1. The document discusses multiple representations in cognitive architectures, including symbolic and visual/imagery-based representations.
2. It reviews past and current attempts to model visual mental imagery in cognitive architectures using array-based and retinotopic representations.
3. The concept of multi-representation cognition is introduced, where problems can be solved using different mental representations, like mathematical/symbolic vs. visual imagery representations, each with their own advantages.
Introduction to cognitive architectures, with a focus on those that have been implemented as software. Differentiates architectures from models and theories.
Cognitive architecture aims to model human cognition through computational processes that account for perception, reasoning, learning, and other cognitive functions. It proposes blueprint models for intelligent agents and artificial consciousness. Key aspects of cognitive architectures include modeling multiple aspects of cognition through a unified theory, accounting for limitations in human cognition, and demonstrating robust and flexible behavior over time through learning.
The document discusses object-oriented modeling and design. It describes object-oriented design as planning a system of interacting objects to solve software problems using concepts like data abstraction, polymorphism, and data hiding. It outlines the object-oriented design process, including identifying classes and objects, defining attributes and relationships, and specifying interfaces. The benefits of object-oriented design are its reusability and mapping to real-world entities.
John Laird, University of Michigan, presentation at Cognitive Systems Institute Speaker Series on "A Cognitive Architecture Approach to Interactive Task Learning"
This document discusses cognitive science research in virtual worlds. It summarizes different cognitive architecture approaches like symbolic, emergent, and hybrid architectures. It also discusses memory structures and applications of cognitive architectures like Soar and OpenCog Prime. Finally, it considers how cognitive science experiments could be conducted in virtual worlds and how results could potentially transfer learning to embodied artificial agents.
The document discusses cognitive architectures, which are engineering approaches for modeling cognitive systems like humans. It notes that cognitive architectures aim to provide a unified set of mechanisms to explain various cognitive functions like language, problem solving, dreaming, goal-directed behavior, symbol usage, and learning. The document then reviews several specific cognitive architectures, including Soar, ACT-R, LIDA, and 4CAPS. It also discusses challenges in creating cognitive architectures that integrate symbolic and sub-symbolic approaches and can be implemented on neural hardware at large scales.
Extending the knowledge level of cognitive architectures with Conceptual Spac...Antonio Lieto
Extending the knowledge level of cognitive architectures with Conceptual Spaces (+ a case study with Dual-PECCS: a hybrid knowledge representation system for common sense reasoning). Talk given at Stockholm, September 2016.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecn...Antonio Lieto
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
This document describes a system for facial emotion detection using convolutional neural networks. The system uses Haar cascade classifiers to detect faces in images and then applies a convolutional neural network to recognize seven basic emotions (happiness, sadness, anger, fear, disgust, surprise, contempt) from facial expressions. The convolutional neural network architecture includes convolutional layers to extract features, ReLU layers for non-linearity, pooling layers for dimensionality reduction, and fully connected layers for emotion classification. The system is described as having potential applications in security systems, driver monitoring systems, and other real-time emotion detection use cases.
This document summarizes Lei Wang's research on developing SPD matrix-based feature representations for visual recognition. It first introduces covariance representation using covariance matrices as features and discusses challenges with small sample sizes. It then summarizes the author's three main contributions: 1) Discriminatively learning covariance representation to address unreliable eigenvalue estimation, 2) Exploring sparse inverse covariance representation to model feature structure sparsity, 3) Moving to kernel matrix representations to model nonlinear relationships and address singular covariance estimates. The author applies these representations to tasks like action recognition, object recognition, and image set classification, showing improved performance over baselines. Open questions around better understanding and optimizing SPD representations are also discussed.
Study on Different Human Emotions Using Back Propagation Methodijiert bestjournal
With fast evolving technology,Cognitive Science plays a vital role in our day-to-day life. Cognitive science is summed up as the study of mind based on scientific methods. It is al l about the sum of all interdisciplinary like philosophy,psychology,linguistics,artificial intelligence,robot ics,and neuroscience. In this paper,I focused on the facial expressions or emotions of human being as it has an impor tant role in interpersonal relations. Without verb communication,one can imagine the mood of a person by expressions. In this method,we use back propagation neural network for implementation. It is an information proce ssing system that has been developed as a generalization of the mathematical model of human recognition.
This document summarizes theories of divided attention from psychological literature. It describes dual task experiments and factors like task similarity, difficulty, and practice that influence performance. Early theories proposed either a single, limited central processor (Kahneman) or multiple specialized modules (Allport). Later theories like multiple resource theory (Navon & Gopher) and Baddeley's model of working memory provided a synthesis, combining a central executive with modality-specific subsystems to better explain dual task findings. However, all theories have limitations in fully specifying the cognitive architecture underlying divided attention.
The document discusses a commonsense reasoning framework called TCL that integrates typicality, probabilities, and cognitive heuristics. TCL extends description logics with a typicality operator and probabilistic semantics to model prototypical properties. It also uses cognitive heuristics like head-modifier to identify plausible mechanisms for concept combination. The framework has been applied to generate novel content and classify emotions, with encouraging results explaining item-emotion associations for the deaf community.
This document summarizes recent research on human visual perception and its relevance to visualization and computer graphics. It discusses how:
1) The human visual system can rapidly categorize images into regions and properties based on simple parallel computations, before focused attention (called preattentive processing).
2) Five theories of preattentive processing are described, focusing on a limited set of basic visual features (color, size, orientation etc.) that can be detected very quickly.
3) Later research showed that attention still influences early vision, and what we see depends on where attention is focused and what is already in our visual memory.
This document summarizes various clustering techniques used for image segmentation. It discusses clustering approaches like k-means, hierarchical clustering, spectral clustering (Ncut), and relevance feedback. The document provides examples and explanations of how these techniques work. For instance, it explains that k-means clustering aims to group images into predefined clusters by minimizing intra-cluster distances. It also gives an example of how hierarchical clustering builds clusters in a tree structure. In summary, the document surveys popular clustering methods and how they can segment images in an effective manner by grouping similar pixels or images.
Functional and Structural Models of Commonsense Reasoning in Cognitive Archit...Antonio Lieto
The document provides an overview of functional and structural models of commonsense reasoning in cognitive architectures. It discusses several approaches to commonsense reasoning including semantic networks, frames, scripts, and default logic. It also discusses different levels of representation including conceptual spaces, typicality, and compositionality. The document proposes dual process models that integrate heterogeneous representations like prototypes and exemplars. It presents computational models like Dual PECCS and TCL that implement aspects of commonsense reasoning through integrated and connected representations.
The document presents a revised taxonomy for learning objectives based on Bloom's Taxonomy. It describes two dimensions - the cognitive process dimension involving higher and lower order thinking skills, and the knowledge dimension ranging from concrete to abstract knowledge. The cognitive process dimension includes six categories (remember, understand, apply, analyze, evaluate, create) along with specific cognitive processes within each category. The knowledge dimension includes four types of knowledge (factual, conceptual, procedural, metacognitive). Learning objectives involve a cognitive process verb paired with a knowledge object or noun. The taxonomy provides a framework for determining clear learning objectives involving different cognitive and knowledge requirements.
The document presents a revised taxonomy for learning objectives based on Bloom's Taxonomy. It describes two dimensions - the cognitive process dimension and knowledge dimension. The cognitive process dimension involves lower and higher order thinking skills. The knowledge dimension ranges from concrete to abstract knowledge. Together these dimensions provide a framework for classifying learning objectives according to the type of cognitive process and knowledge required. Tables further explain the categories within each dimension and provide examples of learning objectives.
This document summarizes research on how technical illustrations can best represent physical tasks and movements. It discusses how mental imagery and mental rotation are important for understanding complex spatial relationships. The research aims to understand how people comprehend images from different perspectives and camera positions when showing physical tasks. Based on literature, illustrations showing tasks from a performer's viewpoint may be easier to understand, as this provides constant body position cues. Images should also maximize viewpoints across the display plane for easier judgment of distances and angles between objects.
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
Object recognition with cortex like mechanisms pami-07dingggthu
This document summarizes a new framework for robust object recognition inspired by the visual cortex. It describes a hierarchical system that builds an increasingly complex and invariant feature representation through alternating template matching and maximum pooling operations. The approach demonstrates strong performance on single object recognition, multiclass categorization, and scene understanding tasks. Given its biological constraints, it performs surprisingly well and competes with state-of-the-art systems while learning from few examples. The success of this cortex-like model provides plausibility for feedforward models of object recognition in the visual cortex.
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
A Reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014) at the International Conference Problem@Web, Vilamoura, Portugal, 2-4 May 2014
This document provides an overview of cognitive architectures. It discusses several cognitive architectures including Soar, ACT-R, IBM's Watson, and TrueNorth. Soar and ACT-R are described as two of the earliest and most influential cognitive architectures. They model aspects of human cognition like problem solving, learning, perception and more. IBM's Watson demonstrated natural language processing abilities, while TrueNorth is IBM's neuromorphic computing architecture that aims to simulate the human brain more efficiently. The document also discusses classifications of cognitive architectures and their applications in areas like autonomous vehicles.
The document discusses modeling cognition from an embodied perspective. It argues that cognition emerges from adaptive sensorimotor loops involving the external world, body, and brain. To truly understand complex cognitive systems, one must take a systemic approach and study the whole, integrated system in natural behavioral situations. The critical elements for cognition may not be a certain number of neurons, brain size, or specific structures, but rather the embodied interactions and experience of an entity with its environment.
Cognitive modeling is a process that develops computational models of human cognition. It involves integrating a computer program with a cognitive architecture based on how the human brain works. The goal is to develop programs that can exhibit human-like problem solving and decision making. Cognitive models are described formally, derived from principles of cognition, and make precise quantitative predictions. They aim to scientifically explain cognitive processes and how those processes interact.
This document provides an overview of Brian Fisher's background and research in visual analytics as a cognitive science. Some key points:
- Fisher has a background in experimental psychology and cognitive science and does research at the intersection of visualization, human-computer interaction, and cognitive science.
- He discusses the challenges of analyzing "big data" and how visual analytics can help by drawing on theories from cognitive science. Visual analytics needs to be built on theories of cognition, perception, and interaction.
- Fisher advocates for visual analytics to become a translational cognitive science by bridging fields like informatics, visualization, and psychology through collaborative work and shared research questions. His approach involves starting collaborative projects in the intersection of these fields.
Extending the knowledge level of cognitive architectures with Conceptual Spac...Antonio Lieto
Extending the knowledge level of cognitive architectures with Conceptual Spaces (+ a case study with Dual-PECCS: a hybrid knowledge representation system for common sense reasoning). Talk given at Stockholm, September 2016.
Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Rep...Antonio Lieto
This document summarizes Antonio Lieto's work on developing a cognitive framework called heterogeneous proxytypes for conceptual representation and reasoning in artificial systems. The framework incorporates multiple knowledge representations, including prototypes, exemplars, and theories. It allows different representations and reasoning mechanisms to be activated based on context. Lieto describes cognitive models that integrate heterogeneous proxytypes, like the DUAL-PECCS system, and evaluates them on commonsense reasoning tasks.
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecn...Antonio Lieto
Cognitive Agents with Commonsense - Invited Talk at Istituto Italiano di Tecnologia (IIT), I-Cog Initiative. https://www.facebook.com/icog.initiative/posts/129265685733532
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
This document describes a system for facial emotion detection using convolutional neural networks. The system uses Haar cascade classifiers to detect faces in images and then applies a convolutional neural network to recognize seven basic emotions (happiness, sadness, anger, fear, disgust, surprise, contempt) from facial expressions. The convolutional neural network architecture includes convolutional layers to extract features, ReLU layers for non-linearity, pooling layers for dimensionality reduction, and fully connected layers for emotion classification. The system is described as having potential applications in security systems, driver monitoring systems, and other real-time emotion detection use cases.
This document summarizes Lei Wang's research on developing SPD matrix-based feature representations for visual recognition. It first introduces covariance representation using covariance matrices as features and discusses challenges with small sample sizes. It then summarizes the author's three main contributions: 1) Discriminatively learning covariance representation to address unreliable eigenvalue estimation, 2) Exploring sparse inverse covariance representation to model feature structure sparsity, 3) Moving to kernel matrix representations to model nonlinear relationships and address singular covariance estimates. The author applies these representations to tasks like action recognition, object recognition, and image set classification, showing improved performance over baselines. Open questions around better understanding and optimizing SPD representations are also discussed.
Study on Different Human Emotions Using Back Propagation Methodijiert bestjournal
With fast evolving technology,Cognitive Science plays a vital role in our day-to-day life. Cognitive science is summed up as the study of mind based on scientific methods. It is al l about the sum of all interdisciplinary like philosophy,psychology,linguistics,artificial intelligence,robot ics,and neuroscience. In this paper,I focused on the facial expressions or emotions of human being as it has an impor tant role in interpersonal relations. Without verb communication,one can imagine the mood of a person by expressions. In this method,we use back propagation neural network for implementation. It is an information proce ssing system that has been developed as a generalization of the mathematical model of human recognition.
This document summarizes theories of divided attention from psychological literature. It describes dual task experiments and factors like task similarity, difficulty, and practice that influence performance. Early theories proposed either a single, limited central processor (Kahneman) or multiple specialized modules (Allport). Later theories like multiple resource theory (Navon & Gopher) and Baddeley's model of working memory provided a synthesis, combining a central executive with modality-specific subsystems to better explain dual task findings. However, all theories have limitations in fully specifying the cognitive architecture underlying divided attention.
The document discusses a commonsense reasoning framework called TCL that integrates typicality, probabilities, and cognitive heuristics. TCL extends description logics with a typicality operator and probabilistic semantics to model prototypical properties. It also uses cognitive heuristics like head-modifier to identify plausible mechanisms for concept combination. The framework has been applied to generate novel content and classify emotions, with encouraging results explaining item-emotion associations for the deaf community.
This document summarizes recent research on human visual perception and its relevance to visualization and computer graphics. It discusses how:
1) The human visual system can rapidly categorize images into regions and properties based on simple parallel computations, before focused attention (called preattentive processing).
2) Five theories of preattentive processing are described, focusing on a limited set of basic visual features (color, size, orientation etc.) that can be detected very quickly.
3) Later research showed that attention still influences early vision, and what we see depends on where attention is focused and what is already in our visual memory.
This document summarizes various clustering techniques used for image segmentation. It discusses clustering approaches like k-means, hierarchical clustering, spectral clustering (Ncut), and relevance feedback. The document provides examples and explanations of how these techniques work. For instance, it explains that k-means clustering aims to group images into predefined clusters by minimizing intra-cluster distances. It also gives an example of how hierarchical clustering builds clusters in a tree structure. In summary, the document surveys popular clustering methods and how they can segment images in an effective manner by grouping similar pixels or images.
Functional and Structural Models of Commonsense Reasoning in Cognitive Archit...Antonio Lieto
The document provides an overview of functional and structural models of commonsense reasoning in cognitive architectures. It discusses several approaches to commonsense reasoning including semantic networks, frames, scripts, and default logic. It also discusses different levels of representation including conceptual spaces, typicality, and compositionality. The document proposes dual process models that integrate heterogeneous representations like prototypes and exemplars. It presents computational models like Dual PECCS and TCL that implement aspects of commonsense reasoning through integrated and connected representations.
The document presents a revised taxonomy for learning objectives based on Bloom's Taxonomy. It describes two dimensions - the cognitive process dimension involving higher and lower order thinking skills, and the knowledge dimension ranging from concrete to abstract knowledge. The cognitive process dimension includes six categories (remember, understand, apply, analyze, evaluate, create) along with specific cognitive processes within each category. The knowledge dimension includes four types of knowledge (factual, conceptual, procedural, metacognitive). Learning objectives involve a cognitive process verb paired with a knowledge object or noun. The taxonomy provides a framework for determining clear learning objectives involving different cognitive and knowledge requirements.
The document presents a revised taxonomy for learning objectives based on Bloom's Taxonomy. It describes two dimensions - the cognitive process dimension and knowledge dimension. The cognitive process dimension involves lower and higher order thinking skills. The knowledge dimension ranges from concrete to abstract knowledge. Together these dimensions provide a framework for classifying learning objectives according to the type of cognitive process and knowledge required. Tables further explain the categories within each dimension and provide examples of learning objectives.
This document summarizes research on how technical illustrations can best represent physical tasks and movements. It discusses how mental imagery and mental rotation are important for understanding complex spatial relationships. The research aims to understand how people comprehend images from different perspectives and camera positions when showing physical tasks. Based on literature, illustrations showing tasks from a performer's viewpoint may be easier to understand, as this provides constant body position cues. Images should also maximize viewpoints across the display plane for easier judgment of distances and angles between objects.
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
Object recognition with cortex like mechanisms pami-07dingggthu
This document summarizes a new framework for robust object recognition inspired by the visual cortex. It describes a hierarchical system that builds an increasingly complex and invariant feature representation through alternating template matching and maximum pooling operations. The approach demonstrates strong performance on single object recognition, multiclass categorization, and scene understanding tasks. Given its biological constraints, it performs surprisingly well and competes with state-of-the-art systems while learning from few examples. The success of this cortex-like model provides plausibility for feedforward models of object recognition in the visual cortex.
Snips and snails and puppy dog tails: the need to preserve complexity in math...Universidade de Lisboa
A Reply to “The Use of Digital Tools in Web-based Mathematical Problem Solving: different levels of sophistication in Solving-and-Expressing” (Jacinto, Nobre, Carreira & Amado, 2014) at the International Conference Problem@Web, Vilamoura, Portugal, 2-4 May 2014
This document provides an overview of cognitive architectures. It discusses several cognitive architectures including Soar, ACT-R, IBM's Watson, and TrueNorth. Soar and ACT-R are described as two of the earliest and most influential cognitive architectures. They model aspects of human cognition like problem solving, learning, perception and more. IBM's Watson demonstrated natural language processing abilities, while TrueNorth is IBM's neuromorphic computing architecture that aims to simulate the human brain more efficiently. The document also discusses classifications of cognitive architectures and their applications in areas like autonomous vehicles.
The document discusses modeling cognition from an embodied perspective. It argues that cognition emerges from adaptive sensorimotor loops involving the external world, body, and brain. To truly understand complex cognitive systems, one must take a systemic approach and study the whole, integrated system in natural behavioral situations. The critical elements for cognition may not be a certain number of neurons, brain size, or specific structures, but rather the embodied interactions and experience of an entity with its environment.
Cognitive modeling is a process that develops computational models of human cognition. It involves integrating a computer program with a cognitive architecture based on how the human brain works. The goal is to develop programs that can exhibit human-like problem solving and decision making. Cognitive models are described formally, derived from principles of cognition, and make precise quantitative predictions. They aim to scientifically explain cognitive processes and how those processes interact.
This document provides an overview of Brian Fisher's background and research in visual analytics as a cognitive science. Some key points:
- Fisher has a background in experimental psychology and cognitive science and does research at the intersection of visualization, human-computer interaction, and cognitive science.
- He discusses the challenges of analyzing "big data" and how visual analytics can help by drawing on theories from cognitive science. Visual analytics needs to be built on theories of cognition, perception, and interaction.
- Fisher advocates for visual analytics to become a translational cognitive science by bridging fields like informatics, visualization, and psychology through collaborative work and shared research questions. His approach involves starting collaborative projects in the intersection of these fields.
This document provides an overview of Brian Fisher's background and research in visual analytics as a cognitive science. Some key points:
- Fisher has a background in experimental psychology and cognitive science and does research at the intersection of visualization, human-computer interaction, and cognitive science.
- He discusses the challenges of analyzing "big data" and how visual analytics can help by drawing on theories from cognitive science. Visual analytics needs to be built on theories of cognition, perception, and interaction.
- Fisher advocates for visual analytics to become a translational cognitive science by bridging fields like informatics, visualization, and psychology through collaborative work and shared research questions. His approach involves starting collaborative projects in the intersection of these fields.
This document discusses experimental categorization and deep visualization as approaches to analyzing culture using techniques from visual computing and data analysis. It describes cultural analytics as using heterogeneous data and visualization tools to analyze relationships and patterns in global culture. Experimental categorization involves developing innovative and provisional dimensions for describing visual images based on characteristics like color, shape, and texture. Deep visualization makes the underlying technical processes behind images and visualizations more transparent. Examples of projects applying these approaches are provided.
Vertical integration of computational architectures - the mediator problemYehor Churilov
1. The document discusses the problem of integrating computational architectures for artificial intelligence. There is a major gap between low-level sensory representations and higher-level cognitive functions that cannot be bridged by existing two-tier architectures alone.
2. It proposes that a conceptually independent architectural layer is needed to act as a mediator between the different representation levels. This would help address issues around increasing cognitive abilities that demand greater integration across architectures.
3. A second problem is the height of the integration platform - a fully integrated platform is needed at a higher level than currently exists for modular hybrid systems. The document outlines approaches to solving the mediator problem and facilitating greater platform integration through more unified computing methods.
Cognitive Paradigm in AI - Invited Lecture - Kyiv/Kyev - LietoAntonio Lieto
1) The document discusses the cognitive paradigm in artificial intelligence research and cognitively inspired AI systems.
2) Cognitively inspired AI systems are designed based on insights from human and animal cognition, using structural constraints from cognitive science.
3) Examples of cognitively inspired AI systems discussed include GPS, semantic networks, the RM model of past-tense acquisition, and cognitive architectures like Soar and ACT-R.
Computational Explanation in Biologically Inspired Cognitive Architectures/Sy...Antonio Lieto
Computational models of cognition can have explanatory power when they are structurally valid models of the natural systems that inspired them. The document discusses different approaches to modeling knowledge in cognitive architectures and humans. It analyzes how ACT-R, CLARION, and LIDA represent concepts, and suggests that humans likely use heterogeneous representations including prototypes, exemplars, and other conceptual structures. Models should account for this heterogeneity to better explain human cognition.
KMD 1001 Design Brief and Ontology TaskStian Håklev
The document outlines the assignments for a course on knowledge media design, including an ontology task defining key concepts and their relationships, presentations analyzing frameworks and design challenges, and a final report proposing a solution to a design challenge. It provides examples of framework visualizations and discussions of concepts like design, knowledge, and media to guide the assignments. Students are asked to analyze relationships between pairs of concepts and propose innovative approaches to addressing knowledge media design problems.
Bioinspired Character Animations: A Mechanistic and Cognitive ViewSimpson Count
Unlike traditional animation techniques, which attempt to copy human movement, ‘cognitive’ animation solutions mimic the brain's approach to problem solving, i.e., a logical (intelligent) thinking structure. This procedural animation solution uses bio-inspired insights (modelling nature and the workings of the brain) to unveil a new generation of intelligent agents. As with any promising new approach, it raises hopes and questions; an extremely challenging task that offers a revolutionary solution, not just in animation but to a variety of fields, from intelligent robotics and physics to nanotechnology and electrical engineering. Questions, such as, how does the brain coordinate muscle signals? How does the brain know which body parts to move? With all these activities happening in our brain, we examine how our brain ‘sees’ our body and how it can affect our movements. Through this understanding of the human brain and the cognitive process, models can be created to mimic our abilities, such as, synthesizing actions that solve and react to unforeseen problems in a humanistic manner. We present an introduction to the concept of cognitive skills, as an aid in finding and designing a viable solution. This helps us address principal challenges, such as: How do characters perceive the outside world (input) and how does this input influence their motions? What is required to emulate adaptive learning skills as seen in higher life-forms (e.g., a child's cognitive learning process)? How can we control and ‘direct’ these autonomous procedural character motions? Finally, drawing from experimentation and literature, we suggest hypotheses for solving these questions and more. In summary, this article analyses the biological and cognitive workings of the human mind, specifically motor skills. Reviewing cognitive psychology research related to movement in an attempt to produce more attentive behavioural characteristics. We conclude with a discussion on the significance of cognitive methods for creating virtual character animations, limitations and future applications.
Diagrams are an effective way to communicate complex information because they map conceptual relationships to spatial relationships, taking advantage of the visual processing system. Diagrams work by organizing information spatially so that related pieces of information and logical inferences are presented near each other, allowing problems to be solved through smooth traversal rather than extensive searching. Effective diagrams match their representation to the task, using minimal elements and highlighting relationships through proximity and connection to reduce cognitive load.
This document outlines Serena Pollastri's year 1 research plan. The research aims to map visualization processes that can contribute to designing future scenarios of sustainable, livable cities. The theoretical framework is based on a "metadesign" approach of collaboratively designing design tools to enable systemic change. The research structure involves literature reviews on visualizations, future scenarios, and cities/liveability. Design experiments are planned, including future visioning workshops and a foresight report. The timeline shows literature reviews and design experiments occurring through 2014-2016, culminating in publications and conferences.
An ontology for semantic modelling of virtual worldijaia
This article presents a new representation of semantic virtual environments. We propose to use the ontology as a tool for implementation. Our model, called SVHsIEVs1 provides a consistent representation of the following aspects: the simulated environment, its structure, and the knowledge items using ontology, interactions and tasks that virtual humans can perform in the environment. In SVHsIEVs, we find two type of ontology: the global ontology and the local ontology for Virtual Human. Our architecture has been successfully tested in 3D dynamic environments.
Visualization Methods Overview Presentation Cambridge University Eppler Septe...epplerm
The document provides an overview of various visualization methods that can be used by managers. It discusses classifications of visualizations, including quantitative diagrams, qualitative business diagrams, and an activity-based view that categorizes visualization methods into envisioning, sketching, expressing, diagramming, mapping, materializing, and exploring. The document advises managers to consider the purpose, content, audience, and communication situation when choosing the right visualization method.
Invited Tutorial - Cognitive Design for Artificial Minds AI*IA 2022Antonio Lieto
This document provides an overview of cognitive design for artificial minds. It discusses how cognitive artificial systems are inspired by human and natural cognition. The key points made are:
- Cognitive artificial systems are inspired by human and natural cognition to be more general and versatile than standard AI systems.
- Examples of cognitively inspired AI systems include ACT-R, Soar, and systems developed using the subsumption architecture.
- Cognitively inspired systems differ from standard AI in that they aim to have explanatory power for human cognition through structural models of cognitive processes and representations.
- Such systems can be used to test cognitive theories, provide human-like capabilities, and potentially lead to more general artificial intelligence.
Bio-Inspired Animated Characters A Mechanistic & Cognitive ViewSimpson Count
Unlike traditional animation techniques, which attempt
to copy human movement, ‘cognitive’ animation solutions mimic
the brain’s approach to problem solving, i.e., a logical (intelligent)
thinking structure. This procedural animation solution uses bioinspired insights (modelling nature and the workings of the brain)
to unveil a new generation of intelligent agents. As with any
promising new approach, it raises hopes and questions; an extremely
challenging task that offers a revolutionary solution, not just in
animation but to a variety of fields, from intelligent robotics and
physics to nanotechnology and electrical engineering. Questions,
such as, how does the brain coordinate muscle signals? How does
the brain know which body parts to move? With all these activities
happening in our brain, we examine how our brain ‘sees’ our body
and how it can affect our movements. Through this understanding
of the human brain and the cognitive process, models can be
created to mimic our abilities, such as, synthesizing actions that
solve and react to unforeseen problems in a humanistic manner.
We present an introduction to the concept of cognitive skills, as
an aid in finding and designing a viable solution. This helps us
address principal challenges, such as: How do characters perceive
the outside world (input) and how does this input influence their
motions? What is required to emulate adaptive learning skills as
seen in higher life-forms (e.g., a child’s cognitive learning process)?
How can we control and ‘direct’ these autonomous procedural
character motions? Finally, drawing from experimentation and
literature, we suggest hypotheses for solving these questions and
more. In summary, this article analyses the biological and cognitive
workings of the human mind, specifically motor skills. Reviewing
cognitive psychology research related to movement in an attempt
to produce more attentive behavioural characteristics. We conclude
with a discussion on the significance of cognitive methods for creating
virtual character animations, limitations and future applications.
Face Emotion Analysis Using Gabor Features In Image Database for Crime Invest...Waqas Tariq
The face is the most extraordinary communicator, which plays an important role in interpersonal relations and Human Machine Interaction. . Facial expressions play an important role wherever humans interact with computers and human beings to communicate their emotions and intentions. Facial expressions, and other gestures, convey non-verbal communication cues in face-to-face interactions. In this paper we have developed an algorithm which is capable of identifying a person’s facial expression and categorize them as happiness, sadness, surprise and neutral. Our approach is based on local binary patterns for representing face images. In our project we use training sets for faces and non faces to train the machine in identifying the face images exactly. Facial expression classification is based on Principle Component Analysis. In our project, we have developed methods for face tracking and expression identification from the face image input. Applying the facial expression recognition algorithm, the developed software is capable of processing faces and recognizing the person’s facial expression. The system analyses the face and determines the expression by comparing the image with the training sets in the database. We have followed PCA and neural networks in analyzing and identifying the facial expressions.
Bridging the gap between AI and UI - DSI Vienna - full versionLiad Magen
This is a summary of the latest research on model interpretability, including Recurrent neural networks (RNN) for Natural Language Processing (NLP) in terms of what's in an RNN.
In addition, it contains suggestion to improve machine learning based user interface, to engage users and encourage them to contribute data to adapt the models to them.
Cognitive Architectures Comparision based on perceptual processingSumitava Mukherjee
The document compares several cognitive architectures - ACT-R/PM, SOAR, EPIC, CHREST, and ICARUS - in terms of their approaches to perceptual processing. It analyzes factors like whether initial perceptual information needs to be programmed or learned, the modularity of perception, how expectations are handled, and the granularity of visual representations. While the architectures include some perceptual abilities, the document argues they need to more fully incorporate object-level perception, depth perception, scene perception based on distributed attention theories, and the effects of emotion on perception and attention. More learning from experience is also needed to better ground cognition in perception.
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
1. The document proposes modifications to the ACT-R cognitive architecture to model mental rotation and imagery using explicit spatial representations and computational geometry processes.
2. It describes two mental rotation strategies - holistic and piecemeal - and presents productions that instantiate each strategy using the modified ACT-R representations and processes.
3. The models are able to qualitatively replicate the response time patterns found in human mental rotation experiments using different stimuli to encourage holistic or piecemeal strategies.
Multiple representations and visual mental imagery in artificial cognitive sy...University of Huddersfield
1) The document discusses multiple representations in artificial cognitive systems, including both external representations like diagrams and internal mental representations like visual mental imagery.
2) It presents examples of how problems can be solved using either a mathematical/propositional representation or a visual/imagery-based representation.
3) Leading cognitive architectures are discussed in terms of how they have begun to incorporate multiple representations, with some exploring non-symbolic, array-based representations to model processes involved in visual mental imagery.
Two methods are described for optimizing cognitive model parameters: differential evolution (DE) and high-throughput computing with HTCondor. DE is a genetic algorithm that uses a population of models to explore the parameter space in parallel. It is well-suited for models with few parameters or short run times. HTCondor allows running a population of models over a computer network, making it suitable for larger, more complex models or simulating many participants. Examples of using each method with an ACT-R paired associate model are provided.
Machine Learning, Artificial General Intelligence, and Robots with Human MindsUniversity of Huddersfield
The document discusses different types of artificial intelligence and outlines a new project to install the ACT-R cognitive architecture onto a NAO robot to create a robot with human-level general intelligence and flexible goal-directed behavior through embodied cognition, perception, motor skills, communication, learning and adaptation. The goal is to gain insights into building advanced autonomous agents by modeling key aspects of human cognition and intelligence.
This document summarizes two studies on how people orient themselves using maps in urban environments. The first study found that people often make errors in orientation when relying on highly visually salient objects that are not clear on the map, ignoring important ground-level cues, or misjudging object distances. The second study found that strong 2D ground cues on maps can improve accuracy, but the presence of a salient 3D landmark can confuse people and reduce accuracy. A process model of map-based orientation is proposed based on these findings. The studies have implications for how to design maps to best support orientation.
Graph comprehension model talk, Birkbeck and Toulouse Le Mirail, February 2012University of Huddersfield
The document describes a computational model of graph comprehension built within the ACT-R cognitive architecture. The model simulates how experts and novices interpret interaction graphs by encoding spatial relationships between plotted points and applying prior knowledge about graphical representations. It identifies variables, encodes distances between points symbolically, and recognizes patterns to describe effects. While focused on expert-level understanding, the model represents an initial step toward accounting for individual differences and a broader range of graph types.
Diagrammatic Cognition: Discovery and Design workshop, Humboldt University, B...University of Huddersfield
This workshop is designed to integrate a wide variety of cognitive science perspectives on the roles diagrams play in cognition, addressing various ways in which people design and use diagrams to spatialize thought and make it public, to work through ideas and clarify thinking, to reduce working memory load, to communicate ideas to others, to promote collaborative work by providing an external representation that can be pointed to and animated by gestures and collectively revised. The morning session (talks by Tversky, Healey, and Kirsh) will focus on creating and diagrams and using them to coordinate various activities, the afternoon (talks by Bechtel, Cheng, and Hegarty) will examine uses of diagrams in science. Both session will also include blitz talks presenting one major idea; scholars who would like to present blitz talks should contact the organizer.
http://mechanism.ucsd.edu/diagrammaticcognition.html
A cognitive architecture-based modelling approach to understanding biases in ...University of Huddersfield
Title: "A cognitive architecture-based modelling approach to
understanding biases in visualisation behaviour". A talk given at the "Dealing with Cognitive Biases in Visualisations (DECISIVe 2014) workshop at IEEE VIS, Paris, November 2014.
Title: "Sources of bias when working with visualisations". Introduction to the "Dealing with Cognitive Biases in Visualisations (DECISIVe 2014) workshop at IEEE VIS, Paris, November 2014.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Robert Gordon talk, 21 March 2018
1. Multiple representations and visual mental
imagery in artificial cognitive systems
David Peebles
Reader in Cognitive Science
Department of Psychology
March 21, 2018
2. Outline of the talk
Part 1
Multi-representation cognition
Visual mental imagery
Part 2
Cognitive architectures & the Common Model of Cognition
Multiple representations in cognitive architectures
3. A bit of context and a caveat. . .
PhD, Uni. Birmingham
Experimental psychology,
connectionist modelling
Postdoc, Uni. Nottingham
Diagrammatic Reasoning
(Peter Cheng & Nigel
Shadbolt), ACT-R
University of Huddersfield
Cognitive modelling
ACT-R cognitive
architecture
Reasoning with external
representations
This talk relates to ongoing
‘multi-representation
cognition’ project with Peter
Cheng (Sussex)
Paper at recent AAAI
workshop ‘A Standard
Model of the Mind’ (Peebles
& Cheng, 2017)
Still a work in progress and
my thinking is not fully
developed
4. Multi-representation cognition
Modern human cognition is multi-representational
External (task environment) representations:
Languages (natural and formal)
Diagrams
Maps
Tables
Menus and tool bars in computer applications
Control panels
Specialised abstract notation systems in academic and
technical domains
5. Internal mental representations
Abstract, ‘amodal’, descriptive propositional representations
Depictive representations grounded in perception
Preserve explicitly information about topological and geometric
relations among problem components.
Information format, operators, information indexing methods,
heuristics and goal structures can differ considerably with
alternative representations (Larkin & Simon, 1987).
An example
Imagine a square with sides of one unit. At opposite corners of
the square add circles with radii 2
3 of a unit centred on the
corners. Do the two circles:
1. overlap?
2. just touch?
3. not touch?
6. Mathematics-based solution
1. “According to Pythagoras’ theorem, the length of the diagonal
between the two opposite corners is the square root of 2”.
2. “That’s about 1.4 units so if we divide that by 2, the centre of
the square is about 0.7 units from each corner”.
3. “The radius of each circle is about 0.66 units, so neither
circle’s perimeter will reach the centre of the square”.
4. “Therefore the circles do not touch”.
7. Imagery-based solution
1. “I’m imagining a square and I can
see that circles with unit radius will
definitely overlap; in fact they
intersect each other at the other
corners of the square”.
2. “Now I’m imagining circles of 1
2 unit
and I can see that they clearly
don’t meet. In fact the circles cross
the mid-point of each side of the
square and curve away from the
centre”.
3. “Now I’m thinking of circles with
radii of 2
3 . It’s hard to be certain
how big they should be, but they
seem to just touch each other”.
8. Visual mental imagery and visual working memory
Two solutions rely upon different mental representations
Declarative and mathematical
Visuo-spatial and exploiting imagery in the mind’s eye.
Visual Mental Imagery (VMI). “Representations that produce
the experience of seeing in the absence of visual input”
“Imagery debate”
Pylyshyn All thoughts, including VMI, are propositional.
Kosslyn VMI is an internal, non-perceptual visual experience
caused either by recollecting or conceptualising something.
VMI are structurally analogous to visual representations, and
are caused, at least in part, by psychological processes
shared with the visual system.
VMI has a functional role in planning, (e.g., simulating actions,
particularly when potential costs of error are high).
9. Processes involved in using visual mental imagery
Generation (from knowledge in LTM)
Maintenance (attention)
Inspection, scanning (attention)
Transformation and manipulation
Translation
Rotation
Scaling, zooming
Restructuring and reinterpretation
Synthesis
Composition (e.g., intersection, union, subtraction)
Key question
What form of internal representation allows these
computational processes to be carried out efficiently?
Symbolic/numerical or array-based?
10. More general questions
How can information from different senses, at different levels
of abstraction, be fluidly used in decision making?
What functional role does specialised spatial and visual
processing play in cognition?
How are spatial, visual and abstract symbolic representations
and processes integrated?
What forms of representation are required (necessary and
sufficient) to support human-level capabilities and
performance?
Do visual and spatial cognition (and visual imagery) demand
non-symbolic, depictive representational formats and
operators?
11. Cognitive architectures
Originated in 1950s but active research programme in 1980s.
Cognitive science – differs from mainstream “narrow” AI and
traditional “divide and conquer” approach of experimental
cognitive psychology
Theories of the core, immutable structures and processes of
the human cognitive system.
Aim: general, human level intelligence modelling human
cognition and performance – broad applicability to wide range
of tasks.
Addresses fundamental question of how cognitive, perceptual,
and motor processes interact and integrate to produce
complex, real-world behaviour.
Not simply theoretical constructs but actual running software
systems, often with vision and motor control.
12. An emerging standard model
Several cognitive architectures in existence (20–30)
Two dominant: ACT-R and Soar (both approx. 30 years old).
Much consolidation and convergence over last decade.
‘Common Model of Cognition’ (Laird, Lebiere & Rosenbloom,
2017)
Perception Action
Task
Environment
Learning
Procedural
Perceptual
Learning
Learning
Declarative
Selection
Action
Short−term memory
Long−term memory
Procedural
Long−term memory
Declarative
13. Symbolic approaches to spatial reasoning
Architectures come from traditional symbolic AI tradition
Often ad hoc mechanisms not intrinsic to the architecture
Most use only descriptive representations
CogSketch (Forbus, Usher, Lovett, Lockwood & Wetzel, 2011)
Diagram Representation System (DRS) (Chandrasekaran,
Kurup, Banerjee, Josephson & Winkler, 2004)
ACT-R (Peebles, 2013; Peebles & Cheng, 2003)
Quebec Mississippi
0
10
20
30
40
50
60
70
80
90
100
q q
q
q
Plant CO2 Uptake as a function of Plant Type and Treatment
PlantCO2Uptake
Plant Type
Treatment
Chilled
Non−chilled
14. Attempts at array-based representations
Some architectures have explored non-symbolic, array-based
representations.
Computation with Multiple Representations (CaMeRa) model
(Tabachneck-Schijf, Leonardo & Simon, 1997)
15. Retinotopic Reasoning (R2) architecture
Aims to model the computational properties of mental imagery
(Kunda, McGreggor & Goel, 2013).
Based, in large part, on array based (non-symbolic)
representations and operators.
Successfully applied to: (a) Raven’s Progressive Matrices
test, (b) Embedded Figures test, (c) Block Design test, and (d)
Paper Folding test.
19. Similarities in these non-symbolic approaches
Employ representations consisting of two-dimensional arrays
of pixels.
Operators to manipulate array objects.
Forward and backward connections to higher-level (numerical,
symbolic) representations (CaMeRa, Soar/SVS)
Important in that they allow cognitive modelling of processes
akin to those used in visual mental imagery.
None explicitly address the issue of multiple representation
cognition
20. Processes involved in using multiple representations
Initial selection of representations
Coordination of simultaneous representations
Switching asynchronously between representations
Distribution of task information between representations,
across task sub-goals and time
Understanding computational costs of each representation
Potential for cognitive off-loading
User’s familiarity with each representation
Compatibility of different representations
21. Metacognitive knowledge and processes
Monitoring and control processes to handle the selection and
monitoring of, transitions between, and integration of different
representations.
Meta-level information about the characteristics of different
representational formats (e.g., level of precision afforded,
ease of computation, suitability for a given problem etc.).
Use–and be able to choose between–alternative
representations within the same modality (e.g., different types
of diagram).
22. ACT-R
ACT-R (Anderson, 2007) purely symbolic
Visual/Spatial information represented as numbers and
symbols.
Insufficent to model visual mental imagery
Array module currently under development
Visual
Module
ACT−R Buffers
Environment
Pattern
Matching
Execution
Production
Module
Motor
Problem
State
Declarative
Memory
Procedural
Memory
Control
State
23. Implications for cognitive models and artificial
human-like agents
1. Models must incorporate alternative problem representations.
2. Must incorporate some form of meta-cognitive monitoring and
control processes to handle the selection and monitoring of,
transitions between, and integration of different
representations.
3. Must be able to incorporate meta-level information about the
characteristics of different representational formats (e.g., level
of precision afforded, ease of computation, suitability for a
given problem etc.).
4. Must also be able to incorporate–and be able to choose
betweenalternative representations within the same modality.
24. References I
Anderson, J. R. (2007). How can the human mind occur in the
physical universe? New York, NY: Oxford University Press.
Chandrasekaran, B., Kurup, U., Banerjee, B., Josephson, J. R. &
Winkler, R. (2004). An architecture for problem solving with
diagrams [Lecture notes in artificial intelligence 2980]. In A.
Blackwell, K. Marriott & A. Shimojima (Eds.), Diagrammatic
representation and inference (pp. 235–256). Berlin:
Springer-Verlag.
Forbus, K., Usher, J., Lovett, A., Lockwood, K. & Wetzel, J. (2011).
CogSketch: Sketch understanding for cognitive science
research and for education. Topics in Cognitive Science,
3(4), 648–666.
Kunda, M., McGreggor, K. & Goel, A. K. (2013). A computational
model for solving problems from the Raven’s Progressive
Matrices intelligence test using iconic visual representations.
Cognitive Systems Research, 22, 47–66.
25. References II
Laird, J. E., Lebiere, C. & Rosenbloom, P. S. (2017). A standard
model of the mind: Toward a common computational
framework across artificial intelligence, cognitive science,
neuroscience, and robotics. AI Magazine. 38(4).
Larkin, J. H. & Simon, H. A. (1987). Why a diagram is (sometimes)
worth ten thousand words. Cognitive Science, 11, 65–100.
Lathrop, S. D., Wintermute, S. & Laird, J. E. (2011). Exploring the
functional advantages of spatial and visual cognition from an
architectural perspective. Topics in Cognitive Science, 3(4),
796–818.
Peebles, D. (2013). Strategy and pattern recognition in expert
comprehension of 2×2 interaction graphs. Cognitive
Systems Research, 24, 43–51.
Peebles, D. & Cheng, P. C.-H. (2003). Modeling the effect of task
and graphical representation on response latency in a graph
reading task. Human Factors, 45, 28–45.
26. References III
Peebles, D. & Cheng, P. C.-H. (2017, September 11). Multiple
representations in cognitive architectures. In AAAI Fall
Symposium 2017: ‘‘A Standard Model of the Mind”.
FS-17-01–FS-17-05. American Association for the
Advancement of Artificial Intelligence. Washington, VA.
Tabachneck-Schijf, H. J. M., Leonardo, A. M. & Simon, H. A.
(1997). CaMeRa: A computational model of multiple
representations. Cognitive Science, 21, 305–350.
Wintermute, S. (2012). Imagery in cognitive architecture:
Representation and control at multiple levels of abstraction.
Cognitive Systems Research, 19, 1–29.