scholarly journals TouIST: a Friendly Language for Propositional Logic and More

2020 ◽  
Author(s):  
Jorge Fernandez ◽  
Olivier Gasquet ◽  
Andreas Herzig ◽  
Dominique Longin ◽  
Emiliano Lorini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Propositional Logic ◽  
Combinatorial Problems ◽  
Smt Solvers ◽  
Logical Formalization ◽  
Problem Description ◽  
Simple Language

This work deals with logical formalization and problem solving using automated solvers. We present the automatic translator TouIST that provides a simple language to generate logical formulas from a problem description. Our tool allows us to model many static or dynamic combinatorial problems and to benefit from the regular improvements of SAT, QBF or SMT solvers in order to solve these problems efficiently. In particular, we show how to use TouIST to solve different classes of planning tasks in Artificial Intelligence.

Teaching Language Rules as Solutions to Language Problems

1987 ◽  
Vol 18 (3) ◽  
pp. 194-205 ◽  
Author(s):  
Phil J. Connell
Keyword(s):  
Problem Solving ◽  
Language Teaching ◽  
Teaching Language ◽  
Language Problem ◽  
Language Problems ◽  
Simple Language ◽  
Approach To Teaching

The teaching procedures that are commonly used with language-disordered children do not entirely match the goals that they are intended to achieve. By using a problem-solving approach to teaching language rules, the procedures and goals of language teaching become more harmonious. Such procedures allow a child to create a rule to solve a simple language problem created for the child by a clinician who understands the conditions that control the operation of a rule.

Robots as cognitive tools

2002 ◽  
Vol 1 (1) ◽  
pp. 125-143 ◽  
Author(s):  
Rolf Pfeifer
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Main Tool ◽  
Human Cognition ◽  
Abstract Thinking ◽  
Cognitive Tools ◽  
Developmental Robotics ◽  
Information Theoretic ◽  
Natural Forms ◽  
Over Time

Artificial intelligence is by its very nature synthetic, its motto is “Understanding by building”. In the early days of artificial intelligence the focus was on abstract thinking and problem solving. These phenomena could be naturally mapped onto algorithms, which is why originally AI was considered to be part of computer science and the tool was computer programming. Over time, it turned out that this view was too limited to understand natural forms of intelligence and that embodiment must be taken into account. As a consequence the focus changed to systems that are able to autonomously interact with their environment and the main tool became the robot. The “developmental robotics” approach incorporates the major implications of embodiment with regard to what has been and can potentially be learned about human cognition by employing robots as cognitive tools. The use of “robots as cognitive tools” is illustrated in a number of case studies by discussing the major implications of embodiment, which are of a dynamical and information theoretic nature.

scholarly journals Grounding and Solving in Answer Set Programming

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 25-32 ◽  
Author(s):  
Benjamin Kaufmann ◽  
Nicola Leone ◽  
Simona Perri ◽  
Torsten Schaub
Keyword(s):  
Problem Solving ◽  
Propositional Logic ◽  
Logic Program ◽  
Answer Set Programming ◽  
Key Issues ◽  
Answer Sets ◽  
Answer Set

Answer set programming is a declarative problem solving paradigm that rests upon a workflow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in a effective way, and solving, or how to compute the answer sets of a propositional logic program obtained by grounding.

scholarly journals Artificial intelligence in the synthesis of creative solutions

10.31519/1404 ◽  
2019 ◽  
Author(s):  
Александр Андрейчиков ◽  
Aleksandr Andreychikov ◽  
Ольга Андрейчикова ◽  
Olga Andreichicova
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Present Article ◽  
High Efficiency ◽  
Knowledge Bases ◽  
Computing Technology ◽  
Creative Solutions ◽  
Computer Aided ◽  
Physical Effects ◽  
Technical Systems

Invention problem solving is connected to essential expenses of labour and time, which is spent on the procedures of search and ordering of necessary knowledge, on generation of probable vari-ants of projected systems, on the analysis of offered ideas and de-cisions and understanding perspectiveness of them. The present article outlines the results of the developments in the field of cre-ating computing technology of the synthesis of new engineering on the level of invention. The most attention is paid to problem of computer aided designing on initial stages, where synthesis of new on principal technical systems is carried out. Computer-aided con-struction of new technical system is based on using of data- and knowledge bases of physical effects and of technical decisions as well as different heuristic systematization procedures. The synthe-sis of principles of function of the technical new systems is carried out with using experts knowledge and requires the application of the artificial intelligence methods and the methods of the deci-sions making theory for invention's tasks. Considered approach has been used for synthesis of new technical systems of different functional purposes and had shown high efficiency in computer-aided construction.

scholarly journals Automatic Synthesis of Generalized Winning Strategies of Impartial Combinatorial Games Using SMT Solvers

2020 ◽  
Author(s):  
Kaisheng Wu ◽  
Liangda Fang ◽  
Liping Xiong ◽  
Zhao-Rong Lai ◽  
Yong Qiao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Game Theory ◽  
Experimental Results ◽  
Combinatorial Games ◽  
Logical Framework ◽  
Automatic Synthesis ◽  
Integer Arithmetic ◽  
Smt Solvers ◽  
Winning Strategies

Strategy representation and reasoning has recently received much attention in artificial intelligence. Impartial combinatorial games (ICGs) are a type of elementary and fundamental games in game theory. One of the challenging problems of ICGs is to construct winning strategies, particularly, generalized winning strategies for possibly infinitely many instances of ICGs. In this paper, we investigate synthesizing generalized winning strategies for ICGs. To this end, we first propose a logical framework to formalize ICGs based on the linear integer arithmetic fragment of numeric part of PDDL. We then propose an approach to generating the winning formula that exactly captures the states in which the player can force to win. Furthermore, we compute winning strategies for ICGs based on the winning formula. Experimental results on several games demonstrate the effectiveness of our approach.

Advanced Methodologies Descriptions and Applications

2014 ◽  
pp. 57-65
Author(s):  
Brad Morantz
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Science Fiction ◽  
The World ◽  
Fiction Writers ◽  
Mathematical Applications

Artificial intelligence is the stuff of science fiction writers, robots taking over the world, and computers knowing our every thought and action. Advanced methodologies is the utilization of accepted artificial intelligence programs in mathematical applications to solve a variety of problems. In this chapter, many of these methods will be described and sample applications provided to better explain the advantages of this method in problem solving.

Dynamics in Knowledge

2012 ◽  
pp. 1595-1612 ◽  
Author(s):  
Shigeki Sugiyama
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Problem Solving ◽  
Small Area ◽  
Social Study ◽  
Engineering Science ◽  
The Past ◽  
Intelligent Software ◽  
System Method ◽  
Modern Engineering

Since the idea of “artificial intelligence with knowledge” had been introduced, so many thoughts, theories, and ideas in various fields of engineering, science, geology, social study, economics, and management methods have been proposed. Those things have been started as an extension of modern engineering control theories and practices. Firstly, expert system by using IF-Then rules came up to at a production spot in manufacturing, and then agent system method by using intelligent software programs for design, planning, scheduling, production, and management in manufacturing. And then after, the idea of “Knowledge” burst into the artificial intelligence field as a real aid for getting any purpose to be accomplished by having augmented the past key knowledge in terms of management (controlling). However, those augmented knowledge methods used to have usages only in a limited small area. In addition to this, lots of works have to be done before making the systems work for a target problem solving. And what is worse, lots of parts of systems have to be customized for a new application. This chapter introduces a new direction and a method in “Knowledge” by inaugurating the brand new idea of “Dynamics in Knowledge,” which will behave more flexibly and intelligently in real usages.

Artificial Intelligence

2019 ◽  
pp. 13-22
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

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