scholarly journals A Logic Framework for Non-Conscious Reasoning

2017 ◽  
Author(s):  
Felipe Lara-Rosano
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Fuzzy Sets ◽  
Object Oriented ◽  
Knowledge Based Systems ◽  
Multivalued Logic ◽  
Knowledge Based ◽  
Problem Solving Process ◽  
Everyday Problems

Human non-conscious reasoning is one of the most successful procedures developed to solve everyday problems in an efficient way. This is why the field of artificial intelligence should analyze, formalize and emulate the multiple ways of non-conscious reasoning with the purpose of applying them in knowledge based systems, neurocomputers and similar devices for aiding people in the problem-solving process. In this paper, a framework for those non-conscious ways of reasoning is presented based on object-oriented representations, fuzzy sets and multivalued logic.

Explaining reasoning: an overview of explanation in knowledge-based systems

1991 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Richard W. Southwick
Keyword(s):  
Problem Solving ◽  
Annotated Bibliography ◽  
User Requirements ◽  
Knowledge Based Systems ◽  
Reasoning Process ◽  
Knowledge Based ◽  
Problem Solving Process ◽  
System Context

AbstractThere seems to be general agreement amongst those involved in KBS research that in order to be useful, a system must be able to explain its reasoning to a user. This paper reviews the development of explanation facilities in knowledge-based systems. It differentiates between explanation as a problem-solving process, and that which explains a reasoning process. This review concentrates on the latter, identifying and giving examples of three categories of reasoning explanation.We then look at user requirements for explanation. What makes an explanation useful depends on the expectations of a user, which in turn depends on such issues as user background and system context. Several techniques are examined that have been applied to the problem of producing explanations that are appropriately structured and conveyed.Finally, we discuss some of the work that has been done in describing theories of human discourse and explanation, and some issues that will become increasingly important for future explanation systems.An extensive annotated bibliography is provided.

scholarly journals Review of Artificial Intelligence

2020 ◽  
pp. 143-171
Author(s):  
K. P. V. Sai Aakarsh ◽  
Adwin Manhar
Keyword(s):  
Artificial Intelligence ◽  
Computational Intelligence ◽  
Medical Diagnosis ◽  
Computer Application ◽  
Knowledge Based Systems ◽  
Human Race ◽  
Knowledge Based ◽  
Ordinary Person ◽  
Digital Computers

Over many centuries, tools of increasing sophistication have been developed to serve the human race Digital computers are, in many respects, just another tool. They can perform the same sort of numerical and symbolic manipulations that an ordinary person can, but faster and more reliably. This paper represents review of artificial intelligence algorithms applying in computer application and software. Include knowledge-based systems; computational intelligence, which leads to Artificial intelligence, is the science of mimicking human mental faculties in a computer. That assists Physician to make dissection in medical diagnosis.

Task Ontology-Based Human-Computer Interaction

2009 ◽  
pp. 950-960
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/ concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

scholarly journals Solving Mathematical Puzzles: A Challenging Competition for AI

AI Magazine ◽  
2017 ◽  
Vol 38 (3) ◽  
pp. 83-96 ◽  
Author(s):  
Federico Chesani ◽  
Paola Mello ◽  
Michela Milano
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Scientific Community ◽  
Deep Understanding ◽  
Human Intervention ◽  
Foreseeable Future ◽  
Comprehensive Knowledge ◽  
Problem Solving Process ◽  
Significant Attention

Recently, a number of noteworthy results have been achieved in various fields of artificial intelligence, and many aspects of the problem solving process have received significant attention by the scientific community. In this context, the extraction of comprehensive knowledge suitable for problem solving and reasoning, from textual and pictorial problem descriptions, has been less investigated, but recognized as essential for autonomous thinking in Artificial Intelligence. In this work we present a challenge where methods and tools for deep understanding are strongly needed for enabling problem solving: we propose to solve mathematical puzzles by means of computers, starting from text and diagrams describing them, without any human intervention. We are aware that the proposed challenge is hard and of difficult solution nowadays (and in the foreseeable future), but even studying and solving only single parts of the proposed challenge would represent an important step forward for artificial intelligence.

Task Ontology-Based Human-Computer Interaction

2006 ◽  
pp. 588-596
Author(s):  
Kazuhisa Seta
Keyword(s):  
Problem Solving ◽  
Real World ◽  
Practical Knowledge ◽  
Building Blocks ◽  
Research Field ◽  
Knowledge Based Systems ◽  
Engineering Research ◽  
Knowledge Based ◽  
Task Ontology ◽  
Problem Solving Task

In ontological engineering research field, the concept of “task ontology” is well-known as a useful technology to systemize and accumulate the knowledge to perform problem-solving tasks (e.g., diagnosis, design, scheduling, and so on). A task ontology refers to a system of a vocabulary/concepts used as building blocks to perform a problem-solving task in a machine readable manner, so that the system and humans can collaboratively solve a problem based on it. The concept of task ontology was proposed by Mizoguchi (Mizoguchi, Tijerino, & Ikeda, 1992, 1995) and its validity is substantiated by development of many practical knowledge-based systems (Hori & Yoshida, 1998; Ikeda, Seta, & Mizoguchi, 1997; Izumi &Yamaguchi, 2002; Schreiber et al., 2000; Seta, Ikeda, Kakusho, & Mizoguchi, 1997). He stated: …task ontology characterizes the computational architecture of a knowledge-based system which performs a task. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. It is useful for describing inherent problem-solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problem. ... The ultimate goal of task ontology research is to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes. (Mizoguchi, 2003) We can also recognize task ontology as a static user model (Seta et al., 1997), which captures the meaning of problem-solving processes, that is, the input/output relation of each activity in a problem-solving task and its effects on the real world as well as on the humans’ mind.

Abduction and Hypothesis Withdrawal in Science

1998 ◽  
pp. 180-187 ◽  
Author(s):  
Lorenzo Magnani
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Scientific Reasoning ◽  
Scientific Discovery ◽  
Data Driven ◽  
Negation As Failure ◽  
Weak Hypothesis ◽  
The Subject ◽  
Epistemological Model ◽  
Problem Solving Process

This paper introduces an epistemological model of scientific reasoning which can be described in terms of abduction, deduction and induction. The aim is to emphasize the significance of abduction in order to illustrate the problem-solving process and to propose a unified epistemological model of scientific discovery. The model first describes the different meanings of the word abduction (creative, selective, to the best explanation, visual) in order to clarify their significance for epistemology and artificial intelligence. In different theoretical changes in theoretical systems we witness different kinds of discovery processes operating. Discovery methods are "data-driven," "explanation-driven" (abductive), and "coherence-driven" (formed to overwhelm contradictions). Sometimes there is a mixture of such methods: for example, an hypothesis devoted to overcome a contradiction is found by abduction. Contradiction, far from damaging a system, help to indicate regions in which it can be changed and improved. I will also consider a kind of "weak" hypothesis that is hard to negate and the ways for making it easy. In these cases the subject can "rationally" decide to withdraw his or her hypotheses even in contexts where it is "impossible" to find "explicit" contradictions and anomalies. Here, the use of negation as failure (an interesting technique for negating hypotheses and accessing new ones suggested by artificial intelligence and cognitive scientists) is illuminating

Reconstructing Medical Problem Solving Competence: MACCORD

1993 ◽  
Vol 32 (04) ◽  
pp. 326-338
Author(s):  
B. Petkoff ◽  
H. Mannebach ◽  
S. Kirkby ◽  
D. Kraus
Keyword(s):  
Problem Solving ◽  
Clinical Medicine ◽  
Medical Knowledge ◽  
Medical Problem ◽  
Knowledge Based Systems ◽  
Levels Of Abstraction ◽  
Knowledge Based ◽  
Methodological Principles ◽  
Problem Solving Behavior ◽  
Medical Reasoning

AbstractThe building of medical knowledge-based systems involves the reconstruction of methodological principles and structures within the various subdomains of medicine. ACCORD is a general methodology of knowledge-based systems, and MACCORD its application to medicine. MACCORD represents the problem solving behavior of the medical expert in terms of various types of medical reasoning and at various levels of abstraction. With MACCORD the epistemic and cognitive processes in clinical medicine can be described in formal terminology, covering the entire diversity of medical reasoning. MACCORD is close enough to formalization to make a significant contribution to the fields of medical knowledge acquisition, medical didactics and the analysis and application of medical problem solving methods.

Altering the SightPlan knowledge-based systems

1992 ◽  
Vol 6 (1) ◽  
pp. 19-37 ◽  
Author(s):  
I. D. Tommelein ◽  
B. Hayes-Roth ◽  
R. E. Levitt
Keyword(s):  
Problem Solving ◽  
Knowledge Bases ◽  
Knowledge Based Systems ◽  
Knowledge Domain ◽  
Task Knowledge ◽  
Knowledge Based ◽  
Site Layout ◽  
Different Types ◽  
Control Knowledge ◽  
Construction Site Layout

SightPlan refers to several knowledge-based systems that address construction site layout. Five different versions were implemented and their components of expertise are described here. These systems are alterations of one another, differing either in the problems they solve, the problem-solving methods they apply, or the tasks they address. Because they share either control knowledge, domain concepts, or heuristics, and such knowledge is implemented in well-defined modular knowledge bases, these systems could easily re-use parts of one another. Experiments like those presented here may clarify the role played by different types of knowledge during problem solving, enabling researchers to gain a broader understanding of the generality of the domain and task knowledge that is embedded in KBSs and of the power of their systems.

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