Towards a Descriptive Model of Agent Strategy Search

It is argued that due to the complexity of most economic phenomena, the chances of deriving correct models from a priori principles are small. Instead are more descriptive approach to modelling should be pursued. Agent-based modelling is characterised as a step in this direction. However many agent-based models use off-the-shelf algorithms from computer science without regard to their descriptive accuracy. This paper attempts an agent model that describes the behaviour of subjects reported by Joep Sonnemans as accurately as possible. It takes a structure that is compatible with current thinking cognitive science and explores the nature of the agent processes that then match the behaviour of the subjects. This suggests further modelling improvements and experiments

Metacognition using classifier system: A step approaching intelligent agents

Meta-cognition allows one to monitor and adaptively control cognitive processes. It guides people to select, evaluate, revise, and abandon cognitive tasks, goals, and strategies. Also, meta-cognition can play an important role in human-like software agents. It includes meta-cognitive knowledge, meta cognitive monitoring, and meta cognitive regulation. The main purpose of this research paper is to understand the principles of natural minds and adopt these principles to simulate artificial minds. We consider the conscious software agent, “CMattie” which has its cognitive science side (cognitive modelling) as well as its computer science side (intelligent software). We describe the incorporation of meta cognition in CMattie using fuzzy classifier system including Genetic algorithm and Probabilistic approaches

Constraints and Language

More information on the Publisher's webpage: http://www.cambridgescholars.com/constraints-and-languageInternational audienceThe concept of "constraint" is widely used in linguistics, computer science, and psychology. However, its implementation varies widely depending on the research domain: namely, language description, knowledge representation, cognitive modelling, and problem solving. These various uses of constraints offer complementary views on intelligent mechanisms. For example, in-depth descriptions implementing constraints are used in linguistics to filter out syntactic or discursive structures by means of dedicated description languages and constraint ranking. In computer science, the constraint programming paradigm views constraints as a whole, which can be used, for example, to build specific structures. Finally, in psycholinguistics, experiments are carried out to investigate the role of constraints within cognitive processes (both in comprehension and production), with various applications such as dialog modelling for people with disabilities. In this context, Constraints and Language builds an extended overview of the use of constraints to model and process language

Medical students' cognitive load in volumetric image interpretation:Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum

Physics education with interactive computational modelling

UID/CED/02861/2019The development of knowledge and cognition in physics and other fields of contemporary science, technology, engineering and mathematics (STEM) is based on modelling processes increasingly requiring advanced methods of scientific computation. Physics education for STEM education should then involve learning sequences featuring modelling activities with computational knowledge and technologies. Such sequences should manifest epistemological and cognitive balance between theory, experimentation and computation, be interactively collaborative, and ensure the development of meaningful knowledge in physics, mathematics and scientific computation, appropriately considering the diversity of STEM contexts. To address this challenge we have proposed an approach based on the creation of sequences of interactive engagement learning activities with computational modelling that explore different kinds of modelling, introduce scientific computation progressively, generate and resolve cognitive conflicts in the understanding of physics and mathematics, and comparatively analyze the various complementary representations of the mathematical models of physics. In this work we discuss a learning sequence about fluid mechanics for introductory physics students of STEM university courses, during which they built and explored in the computer mathematical physics models and animations helping them resolve difficulties persisting after theoretical lectures and problem-solving paper and pen activities.publishersversionpublishe

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Exploring Minds. Modes of Modelling and Simulation in Artificial Intelligence

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. This taxonomy cuts across the traditional dichotomies between symbolic / embodied AI, general intelligence / cognitive simulation and human / non-human-like AI. According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programmes: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). In continuation of pragmaticist views of the modes of modelling and simulating world affairs (Humphreys, Winsberg), this taxonomy of approaches to modelling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research – and what made that research programme uniquely dependent on them

Exploring Minds. Modes of Modelling and Simulation in Artificial Intelligence

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. This taxonomy cuts across the traditional dichotomies between symbolic / embodied AI, general intelligence / cognitive simulation and human / non-human-like AI. According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programmes: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). In continuation of pragmaticist views of the modes of modelling and simulating world affairs (Humphreys, Winsberg), this taxonomy of approaches to modelling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research – and what made that research programme uniquely dependent on them

Biomechanical Modelling of Musical Performance: A Case Study of the Guitar

Merged with duplicate record 10026.1/2517 on 07.20.2017 by CS (TIS)Computer-generated musical performances are often criticised for being unable to match the expressivity found in performances by humans. Much research has been conducted in the past two decades in order to create computer technology able to perform a given piece music as expressively as humans, largely without success. Two approaches have been often adopted to research into modelling expressive music performance on computers. The first focuses on sound; that is, on modelling patterns of deviations between a recorded human performance and the music score. The second focuses on modelling the cognitive processes involved in a musical performance. Both approaches are valid and can complement each other. In this thesis we propose a third complementary approach, focusing on the guitar, which concerns the physical manipulation of the instrument by the performer: a biomechanical approach. The essence of this thesis is a study on capturing, analyzing and modelling information about motor and biomechanical processes of guitar performance. The focus is on speed, precision, and force of a guitarist's left-hand. The overarching questions behind our study are: 1) Do unintentional actions originating from motor and biomechanical functions during musical performance contribute a material "human feel" to the performance? 2) Would it be possible determine and quantify such unintentional actions? 3) Would it be possible to model and embed such information in a computer system? The contributionst o knowledgep ursued in this thesis include: a) An unprecedented study of guitar mechanics, ergonomics, and playability; b) A detailed study of how the human body performs actions when playing the guitar; c) A methodologyt o formally record quantifiable data about such actionsin performance; d) An approach to model such information, and e) A demonstration of how the above knowledge can be embeddedin a system for music performance