“Knowledge Sifting” for Preliminary Design

1990 ◽  
Author(s):  
Victor Todd Miller ◽  
Gale E. Nevill
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Direct Synthesis ◽  
Scaling Up ◽  
Preliminary Design ◽  
Associative Memories ◽  
Constraint System ◽  
Reasoning System ◽  
Symbolic Reasoning ◽  
Real World Problems

Abstract This article describes the use of a new symbolic reasoning methodology, based on the use of matrix associative memories, in the domain of preliminary design. A candidate preliminary design is first formulated in terms of a symbolic constraint system. The symbolic reasoner can then derive information about free variables based on the constraints in the system. One of the major difficulties with current reasoning methodologies in artificial intelligence is the scalability problem; as a problem increases in size, interactions between the elements hinder the reasoning process. Initial investigation indicates that this new symbolic reasoning system provides a means to avoid some of the complications that AI search and logic techniques have when scaling up to real world problems. Also, the methodology appears applicable to both analysis and direct synthesis tasks.

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

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.

Towards an Intelligent Biomedical Engineering With Nature-Inspired Artificial Intelligence Techniques

2018 ◽  
pp. 1-26
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Biomedical Engineering ◽  
Theoretical Discussion ◽  
Artificial Intelligent ◽  
Remarkable Effect ◽  
Engineering Research ◽  
Artificial Intelligence Techniques ◽  
Real World Problems

Artificial intelligence has a remarkable effect on many different fields with its flexible and comprehensive solution approaches to solve real-world problems. In this context, the field of biomedical engineering has also been affected by employment of different artificial intelligence-based techniques. This chapter aims to give a theoretical discussion on using nature-inspired artificial intelligent techniques for obtaining intelligent applications within biomedical engineering. As it is known, techniques within the field of artificial intelligence are inspired from nature. So, it is a good approach to focus on nature-inspired techniques for discussing intelligent biomedical engineering research works. Readers will have a chance to understand some ways of using artificial intelligence for achieving better results in biomedical engineering and the related developments associated with this field.

Towards an Intelligent Biomedical Engineering With Nature-Inspired Artificial Intelligence Techniques

Biotechnology ◽  
2019 ◽  
pp. 1733-1758
Author(s):  
Utku Kose
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Biomedical Engineering ◽  
Theoretical Discussion ◽  
Artificial Intelligent ◽  
Remarkable Effect ◽  
Engineering Research ◽  
Artificial Intelligence Techniques ◽  
Real World Problems

Artificial intelligence has a remarkable effect on many different fields with its flexible and comprehensive solution approaches to solve real-world problems. In this context, the field of biomedical engineering has also been affected by employment of different artificial intelligence-based techniques. This chapter aims to give a theoretical discussion on using nature-inspired artificial intelligent techniques for obtaining intelligent applications within biomedical engineering. As it is known, techniques within the field of artificial intelligence are inspired from nature. So, it is a good approach to focus on nature-inspired techniques for discussing intelligent biomedical engineering research works. Readers will have a chance to understand some ways of using artificial intelligence for achieving better results in biomedical engineering and the related developments associated with this field.

Special issue HAIS 2012: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

2015 ◽  
Vol 163 ◽  
pp. 1-2
Author(s):  
Héctor Quintián ◽  
Emilio Corchado ◽  
Ajith Abraham ◽  
André C.P.L.F. de Carvalho ◽  
Michal Wozniak ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Special Issue ◽  
Hybrid Artificial Intelligence ◽  
Artificial Intelligence Systems ◽  
Real World Problems

LEARNING STEPPINGSTONES FOR PROBLEM SOLVING

1993 ◽  
Vol 07 (03) ◽  
pp. 527-540
Author(s):  
DAVID RUBY ◽  
DENNIS KIBLER
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
General Problem ◽  
Real World ◽  
Logic Synthesis ◽  
Vlsi Design ◽  
Problem Solver ◽  
Problem Difficulty ◽  
Learning Problem ◽  
Real World Problems

One goal of Artificial Intelligence is to develop and understand computational mechanisms for solving difficult real-world problems. Unfortunately, domains traditionally used in general problem-solving research lack important characteristics of real-world domains, making it difficult to apply the techniques developed. Most classic AI domains require satisfying a set of Boolean constraints. Real-world problems require finding a solution that meets a set of Boolean constraints and performs well on a set of real-valued constraints. In addition, most classic domains are static while domains from the real world change. In this paper we demonstrate that SteppingStone, a general learning problem solver, is capable of solving problems with these characteristics. SteppingStone heuristically decomposes a problem into simpler subproblems, and then learns to deal with the interactions that arise between the subproblems. In lieu of an agreed upon metric for problem difficulty, we choose significant problems that are difficult for both people and programs as good candidates for evaluating progress. Consequently we adopt the domain of logic synthesis from VLSI design to demonstrate SteppingStone’s capabilities.

Special issue HAIS 2014: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

2019 ◽  
Vol 326-327 ◽  
pp. 1-2
Author(s):  
Marios Polycarpou ◽  
André de Carvalho ◽  
Jeng-Shyang Pan ◽  
Michal Woźniak ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Special Issue ◽  
Hybrid Artificial Intelligence ◽  
Artificial Intelligence Systems ◽  
Real World Problems

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.

Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

2016 ◽  
Vol 176 ◽  
pp. 1-2
Author(s):  
Emilio Corchado ◽  
Ajith Abraham ◽  
André de Carvalho ◽  
Michał Woźniak ◽  
Sung-Bae Cho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Hybrid Artificial Intelligence ◽  
Artificial Intelligence Systems ◽  
Real World Problems
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