Artificial intelligence and operations research: challenges and opportunities in planning and scheduling

2000 ◽  
Vol 15 (1) ◽  
pp. 1-10 ◽  
Author(s):  
CARLA P. GOMES
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Operations Research ◽  
Expressive Power ◽  
Combinatorial Problems ◽  
The Other ◽  
Planning And Scheduling ◽  
Functional Programming Languages ◽  
Other Hand ◽  
Real World Problems

Both the Artificial Intelligence (AI) and the Operations Research (OR) communities are interested in developing techniques for solving hard combinatorial problems, in particular in the domain of planning and scheduling. AI approaches encompass a rich collection of knowledge representation formalisms for dealing with a wide variety of real-world problems. Some examples are constraint programming representations, logical formalisms, declarative and functional programming languages such as Prolog and Lisp, Bayesian models, rule-based formalism, etc. The downside of such rich representations is that in general they lead to intractable problems, and we therefore often cannot use such formalisms for handling realistic size problems. OR, on the other hand, has focused on more tractable representations, such as linear programming formulations. OR-based techniques have demonstrated the ability to identify optimal and locally optimal solutions for well-defined problem spaces. In general, however, OR solutions are restricted to rigid models with limited expressive power. AI techniques, on the other hand, provide richer and more flexible representations of real-world problems, supporting efficient constraint-based reasoning mechanisms as well as mixed initiative frameworks, which allow the human expertise to be in the loop. The challenge lies in providing representations that are expressive enough to describe real-world problems and at the same time guaranteeing good and fast solutions.

scholarly journals Introduction to the special issue on constraint satisfaction for planning and scheduling

2016 ◽  
Vol 31 (5) ◽  
pp. 415-416
Author(s):  
Miguel A. Salido ◽  
Roman Barták
Keyword(s):  
Artificial Intelligence ◽  
Resource Allocation ◽  
Constraint Satisfaction ◽  
Real World ◽  
Real Life ◽  
Special Issue ◽  
Planning And Scheduling ◽  
Artificial Intelligence Planning ◽  
Life Problems ◽  
Real World Problems

AbstractThe areas of Artificial Intelligence planning and scheduling have seen important advances thanks to the application of constraint satisfaction models and techniques. Especially, solutions to many real-world problems need to integrate plan synthesis capabilities with resource allocation, which can be efficiently managed by using constraint satisfaction techniques. Constraint satisfaction plays an important role in solving such real life problems, and integrated techniques that manage planning and scheduling with constraint satisfaction are particularly useful.

scholarly journals El sesgo de la máquina en la toma de decisiones en el proceso penal.

2020 ◽  
Vol 6 (2) ◽  
pp. 54-71
Author(s):  
Raquel Borges Blázquez
Keyword(s):  
Artificial Intelligence ◽  
The Other ◽  
Human Being ◽  
Criminal Process ◽  
Ethical Problems ◽  
Intelligent Machines ◽  
Other Hand ◽  
Toma De Decisiones ◽  
Legal Thinking ◽  
The One

Artificial intelligence has countless advantages in our lives. On the one hand, computer’s capacity to store and connect data is far superior to human capacity. On the other hand, its “intelligence” also involves deep ethical problems that the law must respond to. I say “intelligence” because nowadays machines are not intelligent. Machines only use the data that a human being has previously offered as true. The truth is relative and the data will have the same biases and prejudices as the human who programs the machine. In other words, machines will be racist, sexist and classist if their programmers are. Furthermore, we are facing a new problem: the difficulty to understand the algorithm of those who apply the law.This situation forces us to rethink the criminal process, including artificial intelligence and spinning very thinly indicating how, when, why and under what assumptions we can make use of artificial intelligence and, above all, who is going to program it. At the end of the day, as Silvia Barona indicates, perhaps the question should be: who is going to control global legal thinking?

scholarly journals Applicability of Artificial Intelligence in Decision-Making for Land Forces

2021 ◽  
Vol 30 (2) ◽  
pp. 039-054
Author(s):  
Paul Tudorache
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Research Methodology ◽  
Research Paper ◽  
The Other ◽  
Military Operations ◽  
Other Hand ◽  
The Military

Similar to other fields, also in the military one, the Artificial Intelligence has become recently an evident solution for optimizing specific processes and activities. Therefore, this research paper aims to highlight the potential uses of Artificial Intelligence in the military operations carried out by the Land Forces. In this regard, analysing the framework of the operations process and applying suitable research methodology, the main findings are related to AI’s contributions in optimizing commander’s decisions during the progress of planning and execution. On the other hand, picturing the AI upgrated combat power of the Land Forces is another significant result of this study.

The Effects of Industry 4.0 on Labor Force Attributes and New Challenges

2020 ◽  
pp. 431-454
Author(s):  
Mehmet Saim Aşçı
Keyword(s):  
Artificial Intelligence ◽  
Labor Force ◽  
Industry 4.0 ◽  
The Other ◽  
Advanced Technology ◽  
Significant Problem ◽  
Production Processes ◽  
Other Hand ◽  
New Challenges

Unmanned factories became a topic of discussion after the concept of Industry 4.0 was first introduced in the Hannover Fair in 2001, and increasing the computerization level in business life and supporting the production processes with advanced technology were determined as targets. In this regard, artificial intelligence and increased automation are expected to create new kinds of jobs in the coming years; however, a significant problem is predicted considering that these changes will invalidate a high number of job types exist today. Thus, the workforce will face a severe unemployment threat. As a result of all of this, radical changes in the work methods, along with means of seeking employment, are now considered. The qualities of the work and the workforce are being transformed along with the organization methods of the production. While on the other hand, it becomes evident that education also has to adapt to this transformation. In this study, the issues the labor might have to face during this period will be discussed, along with what could be done to solve these problems.

Models and Reality

2019 ◽  
pp. 128-153
Author(s):  
Stephen Yablo
Keyword(s):  
Model Theory ◽  
Expressive Power ◽  
The Other ◽  
Hilary Putnam ◽  
Objective World ◽  
Other Hand ◽  
Cast Doubt

The philosopher Hilary Putnam uses model theory to cast doubt on our ability to engage semantically with an objective world. The role of mathematics for him is to prove this pessimistic conclusion. The present chapter, on the other hand, explores how models can help us to engage semantically with the objective world. Mathematics functions here as an analogy. Among their many other accomplishments, numbers boost the language’s expressive power; they give us access to recondite physical facts. Models, among their many other accomplishments, do the same thing. This is the analogy this chapter attempts to develop.

Empirical Comparison of Various Discretization Procedures

1998 ◽  
Vol 12 (07) ◽  
pp. 1017-1032 ◽  
Author(s):  
Petr Berka ◽  
Ivan Bruha
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Machine Learning Algorithm ◽  
Empirical Comparison ◽  
Numerical Attributes ◽  
Real World Problems ◽  
Discretization Procedure

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization corresponds to KEX knowledge acquisition algorithm. Since the categorization for KEX is done "off-line" before using the KEX machine learning algorithm, it can be used as a preprocessing step for other machine learning algorithms, too. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is divided into intervals that may form a complex generated by the algorithm as a part of the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, we also used the discretization procedure of the MLC++ library. Other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.

Special issue HAIS 2015: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

2019 ◽  
Vol 326-327 ◽  
pp. 69-70
Author(s):  
Pablo García Bringas ◽  
Igor Santos ◽  
Enrique Onieva ◽  
Eneko Osaba ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Special Issue ◽  
Hybrid Artificial Intelligence ◽  
Artificial Intelligence Systems ◽  
Real World Problems

scholarly journals Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation

2021 ◽  
Author(s):  
Andreas Fügener ◽  
Jörn Grahl ◽  
Alok Gupta ◽  
Wolfgang Ketter
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Work ◽  
Work Environments ◽  
The Other ◽  
Business Organizations ◽  
Human Capabilities ◽  
Work Arrangements ◽  
Other Hand ◽  
The World ◽  
The Future

A consensus is beginning to emerge that the next phase of artificial intelligence (AI) induction in business organizations will require humans to work with AI in a variety of work arrangements. This article explores the issues related to human capabilities to work with AI. A key to working in many work arrangements is the ability to delegate work to entities that can do them most efficiently. Modern AI can do a remarkable job of efficient delegation to humans because it knows what it knows well and what it does not. Humans, on the other hand, are poor judges of their metaknowledge and are not good at delegating knowledge work to AI—this might prove to be a big stumbling block to create work environments where humans and AI work together. Humans have often created machines to serve them. The sentiment is perhaps exemplified by Oscar Wilde’s statement that “civilization requires slaves…. Human slavery is wrong, insecure and demoralizing. On mechanical slavery, on the slavery of the machine, the future of the world depends.” However, the time has come when humans might switch roles with machines. Our study highlights capabilities that humans need to effectively work with AI and still be in control rather than just being directed.

scholarly journals The linguistic sign: Metonymy and virtuality

2017 ◽  
Vol 80 (1) ◽  
Author(s):  
Adam Glaz
Keyword(s):  
Real World ◽  
The Other ◽  
The Real ◽  
Linguistic Form ◽  
Intimate Connection ◽  
Other Hand ◽  
Linguistic Sign ◽  
The One

Grounded in a rich philosophical and semiotic tradition, the most influential models of the linguistic sign have been Saussure’s intimate connection between the signifier and the signi-fied and Ogden and Richards’ semiotic triangle. Within the triangle, claim the cognitive lin-guists Radden and Kövecses, the sign functions in a metonymic fashion. The triangular semi-otic model is expanded here to a trapezium and calibrated with, on the one hand, Peirce’s conception of virtuality, and on the other hand, with some of the tenets of Langacker’s Cogni-tive Grammar. In conclusion, the question “How does the linguistic sign mean?” is answered thus: it means by virtue of the linguistic form activating (virtually) the entire trapezium-like configuration of forms, concepts, experienced projections, and relationships between all of the above. Activation of the real world remains dubious or indirect. The process is both meto-nymic and virtual, in the sense specified.

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