Use of Finite Markov Chains in Business Problems Involving Decision Making and Case-Based Reasoning

2021 ◽  
pp. 321-338
Author(s):  
Michael Voskoglou
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Probability Theory ◽  
International Business ◽  
Case Based Reasoning ◽  
Human Reasoning ◽  
Global Level ◽  
Intelligent Machines ◽  
Business Applications ◽  
Case Based

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.

scholarly journals Artificial Intelligence-Virtual Trainer: An educative system based on artificial intelligence and designed to produce varied and consistent training lessons

2016 ◽  
Vol 231 (2) ◽  
pp. 110-124 ◽  
Author(s):  
Julien Henriet
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Ordered Set ◽  
Case Based Reasoning ◽  
Adaptation Process ◽  
Multi Agent System ◽  
Multi Agent ◽  
Different Parts ◽  
Case Based ◽  
Underlying Theme

AI-Virtual Trainer is an educative system using Artificial Intelligence to propose varied lessons to trainers. The agents of this multi-agent system apply case-based reasoning to build solutions by analogy. However, as required by the field, Artificial Intelligence-Virtual Trainer never proposes the same lesson twice, whereas the same objective may be set many times consecutively. The adaptation process of Artificial Intelligence-Virtual Trainer delivers an ordered set of exercises adapted to the objectives and sub-objectives chosen by trainers. This process has been enriched by including the notion of distance between exercises: the proposed tasks are not only appropriate but are hierarchically ordered. With this new version of the system, students are guided towards their objectives via an underlying theme. Finally, the agents responsible for the different parts of lessons collaborate with each other according to a dedicated protocol and decision-making policy since no exercise must appear more than once in the same lesson. The results prove that Artificial Intelligence-Virtual Trainer, however perfectible, meets the requirements of this field.

scholarly journals Sistem Cerdas Permainan Papan The Battle Of Honor dengan Decision Making dan Machine Learning

2021 ◽  
Vol 12 (2) ◽  
pp. 136
Author(s):  
Arnan Dwika Diasmara ◽  
Aditya Wikan Mahastama ◽  
Antonius Rachmat Chrismanto
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Intelligent System ◽  
Third Party ◽  
Case Based Reasoning ◽  
Board Game ◽  
Rule Based ◽  
Case Based ◽  
Rule Based Systems

Abstract. Intelligent System of the Battle of Honor Board Game with Decision Making and Machine Learning. The Battle of Honor is a board game where 2 players face each other to bring down their opponent's flag. This game requires a third party to act as the referee because the players cannot see each other's pawns during the game. The solution to this is to implement Rule-Based Systems (RBS) on a system developed with Unity to support the referee's role in making decisions based on the rules of the game. Researchers also develop Artificial Intelligence (AI) as opposed to applying Case-Based reasoning (CBR). The application of CBR is supported by the nearest neighbor algorithm to find cases that have a high degree of similarity. In the basic test, the results of the CBR test were obtained with the highest formulated accuracy of the 3 examiners, namely 97.101%. In testing the AI scenario as a referee, it is analyzed through colliding pieces and gives the right decision in determining victoryKeywords: The Battle of Honor, CBR, RBS, unity, AIAbstrak. The Battle of Honor merupakan permainan papan dimana 2 pemain saling berhadapan untuk menjatuhkan bendera lawannya. Permainan ini membutuhkan pihak ketiga yang berperan sebagai wasit karena pemain yang saling berhadapan tidak dapat saling melihat bidak lawannya. Solusi dari hal tersebut yaitu mengimplementasikan Rule-Based Systems (RBS) pada sistem yang dikembangkan dengan Unity untuk mendukung peran wasit dalam memberikan keputusan berdasarkan aturan permainan. Peneliti juga mengembangkan Artificial Intelligence (AI) sebagai lawan dengan menerapkan Case-Based reasoning (CBR). Penerapan CBR didukung dengan algoritma nearest neighbour untuk mencari kasus yang memiliki tingkat kemiripan yang tinggi. Pada pengujian dasar didapatkan hasil uji CBR dengan accuracy yang dirumuskan tertinggi dari 3 penguji yaitu 97,101%. Pada pengujian skenario AI sebagai wasit dianalisis lewat bidak yang bertabrakan dan memberikan keputusan yang tepat dalam menentukan kemenangan.Kata Kunci: The Battle of Honor, CBR, RBS, unity, AI

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

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.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

scholarly journals Strategy selection as rational metareasoning

2016 ◽  
Author(s):  
Falk Lieder ◽  
Tom Griffiths
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cost Benefit ◽  
Human Reasoning ◽  
Strategy Selection ◽  
Heuristics And Biases ◽  
Rational Model ◽  
The Mind ◽  
Over Time ◽  
Human Rationality

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.

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