Method for generating a multi-strata model and an intellectual information processing device
AssigneeAdin Research, Inc.
InventorSetsuo Ohsuga
Life of this patent
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AssigneeAdin Research, Inc.
InventorSetsuo Ohsuga
Estimating the term…
The objective of this disclosure is to propose a method of designing an intelligent system assuring autonomy in problem solving to the largest extent. An architecture of a general purpose problem solving system and also a new modeling scheme for representing an object which may include human activity are discussed first. Then a special purpose problem solving system dedicated for a given problem is generated. It is extracted from a general purpose system using the object model as a template. Several new concepts are included in this disclosure to achieve this goal; a multi-level function structure and its corresponding knowledge structure, multiple meta-level operations, a level manager for building general purpose problem solving systems, a concept of multi-strata model to represent objects including human activity, and a method of extracting a special purpose system from a general purpose system and so on. This idea induces a very different view to computer systems from the conventional ones and systems designed according to this idea extend the scope of computer applications to a large extent, e.g., they can solve problems which require exploratory operations. In particular, it is important that this idea leads us to the concept of automatic programming instead of the conventional style of using computers.
A method for solving a problem in an intelligent information processing device having an intellectual function in a form of a structured knowledge base, the method comprising the steps of:
An intellectual information processing device for solving a problem represented in a form of a model, comprising an intellectual function knowledge base which is structured such that an upper layer function corresponding to a more complex function is represented by lower layer corresponding to less complex function, the device further comprising:
Dependencies are inferred from the claim text.
No related US applications are recorded in the indexed filing history.
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Computing arrangements using knowledge-based models: knowledge representation; Symbolic representation
Application using ai with detail of the ai system: education or instruction
AI and machine learning
Technical subjects covered by former uspc cross-reference art collections [xracs] and digests
Term ended 18 Jun 2017 (estimated)
USPTO file history isn’t available for this patent, so only indexed dates are shown.
(1) Field of the Invention
The present invention relates to a method for intellectually processing an information and an apparatus for carrying out the method and, in particular, a method for building knowledge base and an intellectual information processing device used in solving problems.
The objective of this disclosure is to propose a new computer system which is truly intelligent and behaves like a person at problem solving. Here the term problem solving is used in a very wide sense to mean every activity to derive answer meeting given conditions in a given environment.
The above and other objects, features, and advantages of the present invention will be more apparent from the following detailed description when read in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic diagram illustrating an exploratory problem solving method;
FIG. 2 illustrates an object model that fits into a common scheme;
1. INTRODUCTION
The basic concept underlying the former part of the work presented here is similar with PROTEGE II, a knowledge-acquisition shell that uses problem-solving methods to derive the modeling of tasks, as described in A. R. Puerta, S. W. Tu, and M. A. Musen, Modeling Tasks with Mechanisms, International J. of Intelligent Systems, Vol. 8, 129-152, 1993. This system is rooted on the former research works such as J. McDermott, Preliminary Steps toward a Taxonomy of Problem Solving Methods, Automating Knowledge Acquisition for Expert Systems, (S. Marcus ed.) Kluwer Academic, 225-256, 1988 and M. A. Musen, Conceptual Models of Interactive Knowledge Acquisition Tools, Knowledge Acquisition, 1. 73-88, 1989. Those works, and the work presented in this disclosure too, build a method of problem solving for the given problem. There is however a big difference between them.
The major difference is in the way of making the methods of problem solving and accordingly the modeling concept itself. In the works referred above the method is made by human knowledge engineer.