scholarly journals Strategy selection as rational metareasoning

2016 ◽  
Author(s):  
Falk Lieder ◽  
Tom Griffiths

Many contemporary accounts of human reasoning assume that the mind is equipped with multiple heuristics that could be deployed to perform a given task. This raises the question how the mind determines when to use which heuristic. To answer this question, we developed a rational model of strategy selection, based on the theory of rational metareasoning developed in the artificial intelligence literature. According to our model people learn to efficiently choose the strategy with the best cost-benefit tradeoff by learning a predictive model of each strategy’s performance. We found that our model can provide a unifying explanation for classic findings from domains ranging from decision-making to problem-solving and arithmetic by capturing the variability of people’s strategy choices, their dependence on task and context, and their development over time. Systematic model comparisons supported our theory, and four new experiments confirmed its distinctive predictions. Our findings suggest that people gradually learn to make increasingly more rational use of fallible heuristics. This perspective reconciles the two poles of the debate about human rationality by integrating heuristics and biases with learning and rationality.

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.


Author(s):  
Michael Voskoglou

Artificial intelligence (AI) is the branch of computer science focusing on the creation of intelligent machines that mimic human reasoning and behaviour. Probability theory is among the mathematical tools used in AI applications to deal with situations of uncertainty caused by randomness. In particular, the Markov chain (MC) theory is a smart combination of probability and linear algebra that offers ideal conditions for modelling such situations. International business is about the trade of goods, services, technology, capital, and knowledge at a global level, while decision making (DM) and case-based reasoning (CBR) are among the processes that are frequently used in this field. In this chapter, an absorbing and an ergodic MC model are developed on the steps of DM and CBR respectively for representing mathematically those two processes, thus providing valuable information about their evolution. The examples presented are connected to international business applications.


Author(s):  
Anusha L. ◽  
Nagaraja G S

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Aleksey Polyanskiy

The article is devoted to the theoretical foundations of improving the engineering support of railway construction. One of the main purposes of the existing engineering support system is to evaluate the results of development and monitor the implementation of technological processes for the construction of railway facilities. In the course of the study, it was determined that a number of stages in the development and implementation of technological processes contain tasks for which the use of automated design and control systems is sufficient. However, there are tasks whose solution depends on the experience and intuitive abilities of the engineer (developer of organizational and technological documentation). To solve such problems, in addition to settlement procedures, logical ones are also necessary. In addition, the intensification of railway construction, many restrictions on the production of works and resources, as well as deviations from planned indicators, require prompt decision-making aimed at compliance with design requirements. Obviously, the total amount of information and data on the design and actual technological processes does not guarantee the efficiency and rationality in decision-making by an engineer. In this case, to solve technological, organizational and managerial tasks, it becomes possible to use some methods and means of artificial intelligence. In this regard, it was proposed to supplement the existing system of engineering and technical support for railway construction with a subsystem of engineering and intellectual support for the technological process of constructing a railway track. For the purpose of intellectualization and the formation of a new paradigm of engineering support for railway construction, an analysis was made of modern and promising practices in the development and implementation of technological processes from the perspective of the life cycle of a railway track object. It was found that modern technological processes for the construction of railway facilities should be flexible to changing working conditions. The study showed that this can be achieved through the formation of adaptive digital technological regulations. The basis of the digital regulation is the information model of the technological process. The model formation procedure is divided into stages containing tasks, the solution of which is possible using artificial intelligence tools such as expert systems, artificial neural networks, and genetic algorithms. A fundamental feature of the engineering and intellectual support of the technological process is the possibility of its operational regulation based on the results of monitoring its development over time. A feature of this approach is the need for the operational processing of a large amount of data that determine the development of technological processes over time, the conditions of work, the production capabilities of construction (contracting) organizations. For this, the mathematical and conceptual models of an intelligent automated system have been developed. Its main purpose is the operational solution of the problems of development and implementation of the technological process of construction of railway facilities. The results obtained during the study, as well as the developed tools, made it possible to determine the possibilities of integrating the developed methodology into the existing system for designing and managing the construction of railway facilities. The results given in the article were obtained during the dissertation research performed by the author.


2020 ◽  
Vol 43 ◽  
Author(s):  
Henry M. Cowles ◽  
Jamie Kreiner

Abstract History can help refine the resource-rational model by uncovering how cultural and cognitive forces act together to shape decision-making. Specifically, history reveals how the meanings of key terms like “problem” and “solution” shift over time. Studying choices in their cultural contexts illuminates how changing perceptions of the decision-making process affect how choices are made on the ground.


2018 ◽  
Vol 41 (4) ◽  
Author(s):  
Tania Sourdin

As technology continues to change the way in which we work and function, there are predictions that many aspects of human activity will be replaced or supported by newer technologies. Whilst many human activities have changed over time as a result of human advances, more recent shifts in the context of technological change are likely to have a broader impact on some human functions that have previously been largely undisturbed. In this regard, technology is already changing the practice of law and may for example, reshape the process of judging by either replacing, supporting or supplementing the judicial role. Such changes may limit the extent to which humans are engaged in judging with an increasing emphasis on artificial intelligence to deal with smaller civil disputes and the more routine use of related technologies in more complex disputes.


2020 ◽  
Vol 29 (5) ◽  
pp. 506-512
Author(s):  
Nick Chater ◽  
Jian-Qiao Zhu ◽  
Jake Spicer ◽  
Joakim Sundh ◽  
Pablo León-Villagrá ◽  
...  

In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramatically and systematically people depart from rational norms. In this article, we outline recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities but approximates probabilistic calculations by drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, which offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment.


Author(s):  
Juveriya Afreen

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.


Sign in / Sign up

Export Citation Format

Share Document