scholarly journals Integrating ACT-R Cognitive Models with the Unity Game Engine

2016 ◽  
pp. 35-64 ◽  
Author(s):  
Paul Richard Smart ◽  
Tom Scutt ◽  
Katia Sycara ◽  
Nigel R. Shadbolt
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Cognitive Models ◽  
The Other ◽  
Human Cognition ◽  
Game Engine ◽  
Virtual Characters ◽  
3D Environments ◽  
2D And 3D ◽  
3D Virtual Environments

The main aim of the chapter is to describe how cognitive models, developed using the ACT-R cognitive architecture, can be integrated with the Unity game engine in order to support the intelligent control of virtual characters in both 2D and 3D virtual environments. ACT-R is a cognitive architecture that has been widely used to model various aspects of human cognition, such as learning, memory, problem-solving, reasoning and so on. Unity, on the other hand, is a very popular game engine that can be used to develop 2D and 3D environments for both game and non-game purposes. The ability to integrate ACT-R cognitive models with the Unity game engine thus supports the effort to create virtual characters that incorporate at least some of the capabilities and constraints of the human cognitive system.

scholarly journals Integrating ACT-R Cognitive Models With the Unity Game Engine

2019 ◽  
pp. 512-535
Author(s):  
Paul Richard Smart ◽  
Tom Scutt ◽  
Katia Sycara ◽  
Nigel R. Shadbolt
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Cognitive Models ◽  
The Other ◽  
Human Cognition ◽  
Game Engine ◽  
Virtual Characters ◽  
3D Environments ◽  
2D And 3D ◽  
3D Virtual Environments

The main aim of the chapter is to describe how cognitive models, developed using the ACT-R cognitive architecture, can be integrated with the Unity game engine in order to support the intelligent control of virtual characters in both 2D and 3D virtual environments. ACT-R is a cognitive architecture that has been widely used to model various aspects of human cognition, such as learning, memory, problem-solving, reasoning and so on. Unity, on the other hand, is a very popular game engine that can be used to develop 2D and 3D environments for both game and non-game purposes. The ability to integrate ACT-R cognitive models with the Unity game engine thus supports the effort to create virtual characters that incorporate at least some of the capabilities and constraints of the human cognitive system.

Cognitive Models as Usability Testing Tools

2008 ◽  
pp. 814-829 ◽  
Author(s):  
Vanja Kljajevic
Keyword(s):  
Mobile Phones ◽  
Cognitive Architecture ◽  
Usability Testing ◽  
Integrated Approach ◽  
Cognitive Models ◽  
The Other ◽  
Testing Tools ◽  
Cognitive Theories ◽  
Traditional Approaches ◽  
Interactive Behaviour

This chapter discusses the idea that using computational cognitive models in usability testing has many benefits over the traditional approaches. It argues that computational cognitive models, anchored in the concept of cognitive architecture, offer an integrated approach to interactive behaviour emerging from the use of mobile phones. A cognitive architecture is a theoretical framework containing a set of relatively independent core constraints that are constant across time and tasks. It constrains models built within the cognitive theories based on the architectures, preventing proliferation of implausible theories. This proliferation, on the other hand, is typical of the traditional approaches to usability testing. In this chapter the benefits of using the model-based approach based on a cognitive architecture in usability testing will be discussed, with a special emphasis on mobile phone interfaces.

Sharing of Knowledge and Problem Solving When Teammates Have Diverse Hearing Status

2021 ◽  
pp. 104649642110102
Author(s):  
Michael Stinson ◽  
Lisa B. Elliot ◽  
Carol Marchetti ◽  
Daniel J. Devor ◽  
Joan R. Rentsch
Keyword(s):  
Problem Solving ◽  
Hard Of Hearing ◽  
The Other ◽  
Postsecondary Students ◽  
Problem Solution ◽  
Group Problem ◽  
Team Members ◽  
Group Problem Solving ◽  
Complete Problem ◽  
Hearing Status

This study examined knowledge sharing and problem solving in teams that included teammates who were deaf or hard of hearing (DHH). Eighteen teams of four students were comprised of either all deaf or hard of hearing (DHH), all hearing, or two DHH and two hearing postsecondary students who participated in group problem-solving. Successful problem solution, recall, and recognition of knowledge shared by team members were assessed. Hearing teams shared the most team knowledge and achieved the most complete problem solutions, followed by the mixed DHH/hearing teams. DHH teams did not perform as well as the other two types of teams.

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

Précis ofUnified theories of cognition

1992 ◽  
Vol 15 (3) ◽  
pp. 425-437 ◽  
Author(s):  
Allen Newell
Keyword(s):  
Cognitive Science ◽  
Full Range ◽  
Cognitive Architecture ◽  
Convincing Evidence ◽  
Syllogistic Reasoning ◽  
Human Cognition ◽  
Unified Theory ◽  
Theoretical Concepts ◽  
Wide Range ◽  
Unified Theories

AbstractThe book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses (stimulus-response compatibility and the Sternberg phenomena); discrete motor skills (transcription typing); memory and learning (episodic memory and the acquisition of skill through practice); problem solving (cryptarithmetic puzzles and syllogistic reasoning); language (sentence verification and taking instructions); and development (transitions in the balance beam task). The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory – there must be multiple attempts. But cognitive science must begin to work toward such unified theories.

scholarly journals Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2171 ◽  
Author(s):  
Xianyong Meng ◽  
Xuesong Zhang ◽  
Mingxiang Yang ◽  
Hao Wang ◽  
Ji Chen ◽  
...  
Keyword(s):  
East Asia ◽  
Assessment Tool ◽  
Swat Model ◽  
Spatial Differentiation ◽  
The Other ◽  
Western China ◽  
Surface Processes ◽  
Human Cognition ◽  
Heihe River ◽  
Observation Data

The temporal and spatial differentiation of the underlying surface in East Asia is complex. Due to a lack of meteorological observation data, human cognition and understanding of the surface processes (runoff, snowmelt, soil moisture, water production, etc.) in the area have been greatly limited. With the Heihe River Basin, a poorly gauged region in the cold region of Western China, selected as the study area, three meteorological datasets are evaluated for their suitability to drive the Soil and Water Assessment Tool (SWAT): China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), Climate Forecast System Reanalysis (CFSR), and Traditional Weather Station (TWS). Resultingly, (1) the runoff output of CMADS + SWAT mode is generally better than that of the other two modes (CFSR + SWAT and TWS + SWAT) and the monthly and daily Nash–Sutcliffe efficiency ranges of the CMADS + SWAT mode are 0.75–0.95 and 0.58–0.77, respectively; (2) the CMADS + SWAT and TWS + SWAT results were fairly similar to the actual data (especially for precipitation and evaporation), with the results produced by CMADS + SWAT lower than those produced by TWS + SWAT; (3) the CMADS + SWAT mode has a greater ability to reproduce water balance than the other two modes. Overestimation of CFSR precipitation results in greater error impact on the uncertainty output of the model, whereas the performances of CMADS and TWS are more similar. This study addresses the gap in the study of surface processes by CMADS users in Western China and provides an important scientific basis for analyzing poorly gauged regions in East Asia.

Problem solving methods and knowledge systems: A personal journey to perceptual images as knowledge

2009 ◽  
Vol 23 (4) ◽  
pp. 331-338
Author(s):  
B. Chandrasekaran
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Mental Image ◽  
Spatial Relations ◽  
Point Of View ◽  
Design Strategies ◽  
Symbolic Form ◽  
Knowledge Based ◽  
High Level ◽  
Use Of Knowledge

AbstractI was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support theconstructionof knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.

Application of Maple on Solving the Differential Problem of Rational Functions

2013 ◽  
Vol 479-480 ◽  
pp. 855-860
Author(s):  
Chii Huei Yu
Keyword(s):  
Problem Solving ◽  
Research Methods ◽  
Research Method ◽  
Rational Functions ◽  
The Other ◽  
Mathematical Software ◽  
Differential Problem ◽  
Binomial Theorem ◽  
Other Hand ◽  
Derivatives Of

This paper uses the mathematical software Maple as the auxiliary tool to study the differential problem of four types of rational functions. We can obtain the closed forms of any order derivatives of these rational functions by using binomial theorem. On the other hand, we propose four examples to do calculation practically. The research methods adopted in this study involved finding solutions through manual calculations and verifying these solutions by using Maple. This type of research method not only allows the discovery of calculation errors, but also helps modify the original directions of thinking from manual and Maple calculations. For this reason, Maple provides insights and guidance regarding problem-solving methods.

Hegel on the Nature of Scepticism

2011 ◽  
Vol 32 (1-2) ◽  
pp. 80-99 ◽  
Author(s):  
Dietmar H. Heidemann
Keyword(s):  
The Other ◽  
Human Cognition ◽  
Philosophical Method ◽  
Philosophical Thinking ◽  
Knock Down ◽  
Human Understanding ◽  
Early View ◽  
The One ◽  
Intrinsic Feature

In the Encyclopaedia Logic, Hegel states that ‘philosophy … contains the sceptical as a moment within itself — specifically as the dialectical moment’ (§81, Addition 2), and that ‘scepticism’ as ‘the dialectical moment itself is an essential one in the affirmative Science’ (§78). On the one hand, the connection between scepticism and dialectic is obvious. Hegel claims that scepticism is a problem that cannot be just removed from the philosophical agenda by knock-down anti-sceptical arguments. Scepticism intrinsically belongs to philosophical thinking; that is to say, it plays a constructive role in philosophical thinking. On the other hand, scepticism has to be construed as the view according to which we cannot know whether our beliefs are true, i.e., scepticism plays a destructive role in philosophy no matter what. It is particularly this role that clashes with Hegel's claim of having established a philosophical system of true cognition of the entirety of reality. In the following I argue that for Hegel the constructive and the destructive role of scepticism are reconcilable. I specifically argue that it is dialectic that makes both consistent since scepticism is a constitutive element of dialectic.In order to show in what sense scepticism is an intrinsic feature of dialectic I begin by sketching Hegel's early view of scepticism specifically with respect to logic and metaphysics. The young Hegel construes logic as a philosophical method of human cognition that inevitably results in ‘sceptical’ consequences in that it illustrates the finiteness of human understanding. By doing so, logic not only nullifies finite understanding but also introduces to metaphysics, i.e., the true philosophical science of the absolute.

scholarly journals Neo-mechanistic explanatory integration for cognitive science: the problem of reduction remains

Sofia ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 124-145 ◽  
Author(s):  
Diego Azevedo Leite
Keyword(s):  
Information Processing ◽  
Cognitive Science ◽  
Cognitive Models ◽  
Human Cognition ◽  
Cognitive Mechanisms ◽  
Cognitive Sciences ◽  
Significant Challenge ◽  
Neural Processes ◽  
Cognitive Theories ◽  
Multi Level

One of the central aims of the neo-mechanistic framework for the neural and cognitive sciences is to construct a pluralistic integration of scientific explanations, allowing for a weak explanatory autonomy of higher-level sciences, such as cognitive science. This integration involves understanding human cognition as information processing occurring in multi-level human neuro-cognitive mechanisms, explained by multi-level neuro-cognitive models. Strong explanatory neuro-cognitive reduction, however, poses a significant challenge to this pluralist ambition and the weak autonomy of cognitive science derived therefrom. Based on research in current molecular and cellular neuroscience, the framework holds that the best strategy for integrating human neuro-cognitive theories is through direct reductive explanations based on molecular and cellular neural processes. It is my aim to investigate whether the neo-mechanistic framework can meet the challenge. I argue that leading neo-mechanists offer some significant replies; however, they are not able yet to completely remove strong explanatory reductionism from their own framework.

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