scholarly journals Behaviour and Reasoning Description Language (BRDL)

2020 ◽  
pp. 137-153
Author(s):  
Antonio Cerone
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Interactive Systems ◽  
Human Reasoning ◽  
Language Usage ◽  
Description Language ◽  
Social Scientists ◽  
Formal Modelling ◽  
Basic Language ◽  
Simple Notation

Abstract In this paper we present a basic language for describing human behaviour and reasoning and present the cognitive architecture underlying the semantics of the language. The language is illustrated through a number of examples showing its ability to model human reasoning, problem solving, deliberate behaviour and automatic behaviour. We expect that the simple notation and its intuitive semantics may address the needs of practitioners from non matematical backgrounds, in particular psychologists, linguists and other social scientists. The language usage is twofold, aiming at the formal modelling and analysis of interactive systems and the comparison and validation of alternative models of memory and cognition.

scholarly journals General Environment Description Language

2021 ◽  
Vol 11 (2) ◽  
pp. 740
Author(s):  
Krzysztof Zatwarnicki ◽  
Waldemar Pokuta ◽  
Anna Bryniarska ◽  
Anna Zatwarnicka ◽  
Andrzej Metelski ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
General Purpose ◽  
The Other ◽  
Potential Fields ◽  
General Intelligence ◽  
Language Usage ◽  
Description Language ◽  
It Systems ◽  
New Research ◽  
Future Work

Artificial intelligence has been developed since the beginning of IT systems. Today there are many AI techniques that are successfully applied. Most of the AI field is, however, concerned with the so-called “narrow AI” demonstrating intelligence only in specialized areas. There is a need to work on general AI solutions that would constitute a framework enabling the integration of already developed narrow solutions and contribute to solving general problems. In this work, we present a new language that potentially can become a base for building intelligent systems of general purpose in the future. This language is called the General Environment Description Language (GEDL). We present the motivation for our research based on the other works in the field. Furthermore, there is an overall description of the idea and basic definitions of elements of the language. We also present an example of the GEDL language usage in the JSON notation. The example shows how to store the knowledge and define the problem to be solved, and the solution to the problem itself. In the end, we present potential fields of application and future work. This article is an introduction to new research in the field of Artificial General Intelligence.

Certain models of granular computing based on rough fuzzy approximations

2020 ◽  
Vol 39 (3) ◽  
pp. 2797-2816
Author(s):  
Muhammad Akram ◽  
Anam Luqman ◽  
Ahmad N. Al-Kenani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Granular Computing ◽  
Research Study ◽  
Network Models ◽  
Mathematical Framework ◽  
Human Reasoning ◽  
Accuracy Measures ◽  
Lower And Upper Approximations

An extraction of granular structures using graphs is a powerful mathematical framework in human reasoning and problem solving. The visual representation of a graph and the merits of multilevel or multiview of granular structures suggest the more effective and advantageous techniques of problem solving. In this research study, we apply the combinative theories of rough fuzzy sets and rough fuzzy digraphs to extract granular structures. We discuss the accuracy measures of rough fuzzy approximations and measure the distance between lower and upper approximations. Moreover, we consider the adjacency matrix of a rough fuzzy digraph as an information table and determine certain indiscernible relations. We also discuss some general geometric properties of these indiscernible relations. Further, we discuss the granulation of certain social network models using rough fuzzy digraphs. Finally, we develop and implement some algorithms of our proposed models to granulate these social networks.

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.

On Cognitive Models of Causal Inferences and Causation Networks

2011 ◽  
Vol 3 (1) ◽  
pp. 50-60 ◽  
Author(s):  
Yingxu Wang
Keyword(s):  
Problem Solving ◽  
Mathematical Models ◽  
Causal Inference ◽  
Computational Intelligence ◽  
Theoretical Foundation ◽  
Cognitive Models ◽  
Human Reasoning ◽  
Cognitive Computing ◽  
Causal Inferences ◽  
Human Thought

Human thought, perception, reasoning, and problem solving are highly dependent on causal inferences. This paper presents a set of cognitive models for causation analyses and causal inferences. The taxonomy and mathematical models of causations are created. The framework and properties of causal inferences are elaborated. Methodologies for uncertain causal inferences are discussed. The theoretical foundation of humor and jokes as false causality is revealed. The formalization of causal inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, and computational intelligence.

Can humans form hierarchically embedded mental representations?

1998 ◽  
Vol 21 (5) ◽  
pp. 687-688
Author(s):  
Denise Dellarosa Cummins
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Developmental Psychology ◽  
Mental Representations ◽  
Domain Specificity ◽  
Human Reasoning ◽  
Goal Structure ◽  
Action Sequences ◽  
Intelligent Behavior

Certain recurring themes have emerged from research on intelligent behavior from literatures as diverse as developmental psychology, artificial intelligence, human reasoning and problem solving, and primatology. These themes include the importance of sensitivity to goal structure rather than action sequences in intelligent learning, the capacity to construct and manipulate hierarchically embedded mental representations, and a troubling domain specificity in the manifestation of each.

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.

What is seen and what is not seen in the economy: An effect of our evolved psychology

2018 ◽  
Vol 41 ◽  
Author(s):  
Pascal Boyer ◽  
Michael Bang Petersen
Keyword(s):  
Evolutionary Psychology ◽  
Human Evolution ◽  
Cognitive Architecture ◽  
Developmental Studies ◽  
Social Scientists ◽  
Folk Beliefs ◽  
Domain Specific ◽  
Original Proposal ◽  
Economic Beliefs

AbstractSpecific features of our evolved cognitive architecture explain why some aspects of the economy are “seen” and others are “not seen.” Drawing from the commentaries of economists, psychologists, and other social scientists on our original proposal, we propose a more precise model of the acquisition and spread of folk-beliefs about the economy. In particular, we try to provide a clearer delimitation of the field of folk-economic beliefs (sect. R2) and to dispel possible misunderstandings of the role of variation in evolutionary psychology (sect. R3). We also comment on the difficulty of explaining folk-economic beliefs in terms of domain-general processes or biases (sect. R4), as developmental studies show how encounters with specific environments calibrate domain-specific systems (sect. R5). We offer a more detailed description of the connections between economic beliefs and political psychology (sect. R6) and of the probable causes of individual variation in that domain (sect. R7). Taken together, these arguments point to a better integration or consilience between economics and human evolution (sect. R8).

Tracing Problem Solving in Real Time: fMRI Analysis of the Subject-paced Tower of Hanoi

2005 ◽  
Vol 17 (8) ◽  
pp. 1261-1274 ◽  
Author(s):  
John R. Anderson ◽  
Mark V. Albert ◽  
Jon M. Fincham
Keyword(s):  
Problem Solving ◽  
Cognitive Architecture ◽  
Cognitive Model ◽  
Relevant Information ◽  
Blood Oxygen Level Dependent ◽  
Brain Regions ◽  
Tower Of Hanoi ◽  
Manual Responses ◽  
Prefrontal Region ◽  
Motor Actions

Previous research has found three brain regions for tracking components of the ACT-R cognitive architecture: a posterior parietal region that tracks changes in problem representation, a prefrontal region that tracks retrieval of task-relevant information, and a motor region that tracks the programming of manual responses. This prior research has used relatively simple tasks to incorporate a slow event-related procedure, allowing the blood oxygen level-dependent (BOLD) response to go back to baseline after each trial. The research described here attempts to extend these methods to tracking problem solving in a complex task, the Tower of Hanoi, which involves many complex steps of cognition and motor actions in rapid succession. By tracking the activation patterns in these regions, it is possible to predict with intermediate accuracy when participants are planning a future sequence of moves. The article describes a cognitive model in the ACT-R architecture that is capable of explaining both the latency data in move generation and the BOLD responses in these three regions.

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