Toward truly intelligent information systems-from expert systems to automatic programming
TL;DR: A way of designing intelligent systems that assure autonomy, generality and practicality in problem solving to the greatest extent and can solve complex problems is discussed.
Abstract: The objective of this paper is to discuss a way of designing intelligent systems that assure autonomy, generality and practicality in problem solving to the greatest extent and can solve complex problems. There are various types of problem solving, depending on the object, and often more than one subjects concern the same problem with different roles in complex problem solving. In order to achieve our goal in this environment, a new architecture for the system and a new modeling scheme for representing problems including human activity are discussed, as well as a way of generating problem-specific problem solving systems. Several new concepts are included in this paper: a multi-level function structure and its corresponding knowledge structure, multiple meta-level operations, a multi-strata model to represent problems including human activity, etc. It is shown that the system realizes not only the generality but also the practicality of problem solving by enabling automatic programming.
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01 Jan 1977
TL;DR: This book presents papers from the 26th International Conference on Information Modelling and Knowledge Bases (EJC), which took place in Tampere, Finland, in June 2016, and covers state-of- the-art research and practice.
Abstract: Information modelling and knowledge bases are now essential, not only to academics working in computer science, but also wherever information technology is applied. This book presents papers from the 26th International Conference on Information Modelling and Knowledge Bases (formerly the European Japanese Conference – EJC), which took place in Tampere, Finland, in June 2016. The conference provides a platform to bring together researchers and practitioners working with information modelling and knowledge bases, and the 33 accepted papers cover topics including: conceptual modelling; knowledge and information modelling and discovery; linguistic modelling; cross-cultural communication and social computing; environmental modelling and engineering; and multimedia data modelling and systems. All papers were improved and resubmitted for publication after the conference. Covering state-of- the-art research and practice, the book will be of interest to all those whose work involves information modelling and knowledge bases.
2 citations
11 Oct 2000
TL;DR: A way of generating a multi-agent system with examples is discussed to discuss a problem of creating an organization of the agents dynamically that is suited for coping with the specific problem.
Abstract: As human society glows large and complex problems which human being must solve is also becoming large and complex In many cases, a problem must be solved cooperatively by many people There arise a problem of decomposing the problem into sub-problems, distributing these sub-problems to number of persons and organizing these people in such a way that the problem can be solved most efficiently This organization is not universal but is made specific to the given problem It is possible to create a multi-agent system to correspond to the cooperative work by persons Here is a problem of creating an organization of the agents dynamically that is suited for coping with the specific problem It is the major objective of this paper to discuss a way of generating a multi-agent system with examples
2 citations
Cites methods from "Toward truly intelligent informatio..."
...In this paper, Multi-strata model[5] is used to describe problem solving process, and multi-agent systems are created based on this model....
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15 Dec 2006
TL;DR: This paper defines intelligence as the capability to upgrading information and notes that there have been four important phases in the progress of intelligence: language acquisition, knowledge discovery, conceptualization and granulation.
Abstract: Intelligence concerns many aspects of human mental activity and is considered difficult to be defined clearly. Apart from its relation to mental activity, however, it is possible to discuss intelligence formally based on information it deals with. This paper defines intelligence as the capability to upgrading information and notes that there have been four important phases in the progress of intelligence. These are: (1) language acquisition, (2) knowledge discovery, (3) conceptualization and (4) granulation. These phases are discussed in this paper.
1 citations
Cites background from "Toward truly intelligent informatio..."
...Many complex problems involve human and, since human capability and characteristics affect the solution, it must be solved taking human characteristics in account [15,16,17]....
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11 Oct 2000
TL;DR: A basic representation scheme for agent modules which reflects the Gibson's view of information resources and uses knowledge processing language KAUS (knowledge acquisition and utilization system) based on first order logic and axiomatic set theory.
Abstract: This paper describes a method to construct multiagent systems. The method proposed here is explored accounting for the Gibson's ecological view of information, i.e. affordance. We apply the idea of affordance not only to the reactive models of agents but also to the deliberative models of the agents. By this approach, we can avoid the frame problems that emerge from the dynamic environment including the agent's mental world. We describe a basic representation scheme for agent modules which reflects the Gibson's view of information resources. As a system description language, we use knowledge processing language KAUS (knowledge acquisition and utilization system) based on first order logic and axiomatic set theory. We consider as an example the multi-strata modeling scheme for developing human-computer interactive problem solving systems that are essentially multiagent systems.
1 citations
Cites methods from "Toward truly intelligent informatio..."
...In the next chapter we issue the problem for implementing intelligent agent systems using the multi-strata modelling scheme [8, 9 ,10]....
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11 Oct 2000
TL;DR: The final goal of this research is to develop an automatic programming system which can be used easily and to replace the subject of programming from person to computer.
Abstract: This paper discusses a method of translating human activities into a program The final goal of this research is to develop an automatic programming system which can be used easily A new modeling scheme is introduced to allow human-like representation and to replace the subject of programming from person to computer A method of translating rules described based on this modeling scheme into program specification is proposed By using domain which is defined to variables of rules, the optimum program specification can be generated
1 citations
References
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01 Jan 1988
TL;DR: This book describes the principles that guided the expert systems research group's work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise.
Abstract: In June of 1983, our expert systems research group at Carnegie Mellon University began to work actively on automating knowledge acquisition for expert systems. In the last five years, we have developed several tools under the pressure and influence of building expert systems for business and industry. These tools include the five described in chapters 2 through 6 - MORE, MOLE, SALT, KNACK and SIZZLE. One experiment, conducted jointly by developers at Digital Equipment Corporation, the Soar research group at Carnegie Mellon, and members of our group, explored automation of knowledge acquisition and code development for XCON (also known as R1), a production-level expert system for configuring DEC computer systems. This work influenced the development of RIME, a programming methodology developed at Digital which is the subject of chapter 7. This book describes the principles that guided our work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise. of the work, brought out in the introductory chapter, is A common theme that much power can be gained by understanding the roles that domain knowledge plays in problem solving. Each tool can exploit such an understanding because it focuses on a well defined problem-solving method used by the expert systems it builds. Each tool chapter describes the basic problem-solving method assumed by the tool and the leverage provided by committing to the method."
341 citations
02 Jan 1993
TL;DR: This chapter takes a few steps toward creating aTaxonomy of methods -- a taxonomy that identifies some of the discriminating characteristics of the methods expert systems use and that suggests how methods can be mapped onto tasks.
Abstract: Although efforts, some successful, to develop expert systems (application systems that can perform knowledge-intensive tasks) have been going on now for almost 20 years, we are not yet very good at describing the variations in problem-solving methods that these systems use, nor do we have much of an understanding of how to characterize the methods in terms of features of the types of tasks for which they are appropriate. This chapter takes a few steps toward creating a taxonomy of methods -- a taxonomy that identifies some of the discriminating characteristics of the methods expert systems use and that suggests how methods can be mapped onto tasks.
314 citations
01 Jun 1994
TL;DR: A system named AA1 (Articulation Aid 1) which aids human users in the formation of new concepts in the domain of engineering and science and is as nonprescriptive as possible, but gives stimulation for the user to form concepts that he could not by using only pencil and paper.
Abstract: This paper describes a system named AA1 (Articulation Aid 1) which aids human users in the formation of new concepts in the domain of engineering and science. From the viewpoint of concept formation, one main process of creation is divergent thinking in which broad alternatives are searched, and another process is convergent thinking in which a unique solution is sought. From the viewpoint of human activities, creation also includes the aspect of collaboration among people and the aspect of individual reflection, although they are interrelated. AA1, the system presented in this paper, supports divergent thinking during individual reflection. Engineers and scientists usually scrawl many notes on paper while exploring new possible concepts in the divergent thinking process. A system is needed to reflect the fragments of concepts that are not articulated yet and thereby stimulate the formation of new concepts. AA1 builds a two-dimensional space from the words the user provides. Looking at this space and other precedent spaces, the user can form new concepts little by little. The main feature of AA1 different, from existing hypermedia systems and CSCW systems is the strategy for building the space presented to the user. The system is as nonprescriptive as possible, but it gives stimulation for the user to form concepts that he could not by using only pencil and paper. Experimentation has shown that the space which AA1 displays can effectively help the user to build new concepts. The most prominent effect is that empty regions in the space automatically configured by the system often lead to new concepts. >
86 citations
TL;DR: A taxonomy for knowledge-acquisition aids that is based on the terms and relationships that a given tool uses to establish the semantics of a user's entries is described, which has important implications for how a knowledge- Acquisition tool is used and to what degree it can assist its users in analysing new applications at the knowledge level.
71 citations