Modeling motivations and emotions as a basis for intelligent behavior

1997 ◽  
Author(s):  
Dolores Cañamero
Keyword(s):  
Intelligent Behavior

Subjective factors affecting gun shooting accuracy among employees of internal affairs agencies in process of learning

2020 ◽  
Vol 2020 (4) ◽  
pp. 198-206
Author(s):  
Anton Rozhkov ◽  
Anton Popov ◽  
Vitaliy Balahonskiy
Keyword(s):  
Subjective Experience ◽  
Statistical Significance ◽  
Self Regulation ◽  
Sensorimotor Coordination ◽  
Factors Affecting ◽  
Intelligent Behavior ◽  
Experience Of Stress ◽  
Firing Line ◽  
High Level ◽  
In Fire

The article is devoted to the study of subjective factors affecting shooting accuracy of law enforcement officers. The empirical study identified some subjective factors reducing gun shooting accuracy and effectiveness among law enforcers. These characteristics include sensorimotor coordination and subjective experience of stress during the shooting process. Scientific analysis made it possible to determine statistical significance of the influence of these factors on the accuracy of shooting. To increase the effectiveness of shooting among officers with a low index of sensorimotor coordination, the authors suggest using exercises aimed at cultivating sensorimotor coordination in fire training classes. While working with employees being under a high level of subjectively experienced stress, more attention should be paid to training techniques to overcome stress and form intelligent behavior in extreme situations. The authors also draw readers’ attention to factors increasing the effectiveness of shooting: officers’ ability to determine the subjective level of stress, their knowledge of emotional self-regulation techniques, knowledge of the sequence of their actions in the firing line.

Information and mechanical models of intelligence

2005 ◽  
Vol 13 (3) ◽  
pp. 565-582 ◽  
Author(s):  
Maria Eunice Quilici Gonzalez
Keyword(s):  
Cognitive Science ◽  
Computer Simulations ◽  
Advanced Technology ◽  
Main Topic ◽  
Social Environments ◽  
Meaningful Information ◽  
Intelligent Behavior ◽  
Mechanical Models ◽  
The Mind ◽  
The Impact

The impact of new advanced technology on issues that concern meaningful information and its relation to studies of intelligence constitutes the main topic of the present paper. The advantages, disadvantages and implications of the synthetic methodology developed by cognitive scientists, according to which mechanical models of the mind, such as computer simulations or self-organizing robots, may provide good explanatory tools to investigate cognition, are discussed. A difficulty with this methodology is pointed out, namely the use of meaningless information to explain intelligent behavior that incorporates meaningful information. In this context, it is inquired what are the contributions of cognitive science to contemporary studies of intelligent behavior and how technology may play a role in the analysis of the relationships established by organisms in their natural and social environments.

scholarly journals The Entropy-Cost Function Evaluation Method for Unmanned Ground Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Ya-Nan Zhao ◽  
Kai-Wen Meng ◽  
Li Gao
Keyword(s):  
Cost Function ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Quantitative Result ◽  
Indicator System ◽  
Function Evaluation ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Conservative Strategy ◽  
Intelligent Behavior

To improve the technology of unmanned ground vehicles, it is necessary to conduct a proper evaluation on various technologies. Previous evaluation methods are mainly based on completion of the task; this may mislead most of teams of unmanned ground vehicles using a conservative strategy during the evaluation. In this paper, a new evaluation method is proposed. Based on typical working conditions including intersection, car-following, and obstacle-avoiding, the new evaluation indicator system is established, and the entropy-cost function method is applied to the comprehensive evaluation of unmanned ground vehicles. As reported in a numerical example, the proposed evaluation method can get a quantitative result that authentically reflects the intelligent behavior level of unmanned ground vehicles.

Rethinking intelligent behavior and intelligent character terminologies

2008 ◽  
Author(s):  
Safawi Abdul Rahman ◽  
Mohamad Shanudin Zakaria ◽  
Suhaila Sardi
Keyword(s):  
Intelligent Behavior

Surface Roughness Optimization in Turning Operation Using Hybrid Algorithm of Artificial Bee Colony with RSM

2015 ◽  
Vol 1101 ◽  
pp. 393-396
Author(s):  
Mohammad Ahsan Habib ◽  
Md. Anayet U. Patwari ◽  
Koushik Alam Khan ◽  
A.N.M. Amanullah Tomal
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Artificial Bee Colony ◽  
Removal Rate ◽  
Machining Parameters ◽  
Turning Operation ◽  
Bee Colony ◽  
Optimal Value ◽  
Intelligent Behavior ◽  
Desirability Analysis

For cost reduction and quality improvement of machining products, optimum output machining parameters such as material removal rate, tool wear ratio and surface roughness is very essential. Moreover, these output parameters are strongly depends on the precision of the machine tool as well as the input machining parameters. In this paper, a hybrid model of Artificial Bee Colony (ABC), which is motivated by the intelligent behavior of honey bees with Response Surface Methodology (RSM), has been developed for optimizing the surface roughness of stainless steel during turning operation. The predicted optimal value of surface roughness of stainless steel is further confirmed by conducting supplementary experiments. Finally, the performance of this algorithm is evaluated in comparison with desirability analysis. The performance of ABC is at par with that of desirability analysis for different parametric conditions.

scholarly journals Macphail (1987) Revisited: Pigeons Have Much Cognitive Behavior in Common With Humans

2021 ◽  
Vol 11 ◽  
Author(s):  
Thomas R. Zentall
Keyword(s):  
Cognitive Ability ◽  
Present Article ◽  
Sensory Input ◽  
Motor Output ◽  
Cognitive Behavior ◽  
Intellectual Abilities ◽  
Intelligent Behavior

The hypothesis proposed by Macphail (1987) is that differences in intelligent behavior thought to distinguish different species were likely attributed to differences in the context of the tasks being used. Once one corrects for differences in sensory input, motor output, and incentive, it is likely that all vertebrate animals have comparable intellectual abilities. In the present article I suggest a number of tests of this hypothesis with pigeons. In each case, the evidence suggests that either there is evidence for the cognitive behavior, or the pigeons suffer from biases similar to those of humans. Thus, Macphail’s hypothesis offers a challenge to researchers to find the appropriate conditions to bring out in the animal the cognitive ability being tested.

scholarly journals Model-free, Model-based, and General Intelligence

2018 ◽  
Author(s):  
Hector Geffner
Keyword(s):  
Intelligent Systems ◽  
Human Mind ◽  
Writing Programs ◽  
Scalable Algorithms ◽  
Model Based ◽  
Model Free ◽  
Considerable Success ◽  
Black Boxes ◽  
Free Model ◽  
Intelligent Behavior

During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have indeed close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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