1994 Fiscal Year Final Research Report Summary
Machine Learning Approaches to the Analysis of Organizational Behaviors
| Project/Area Number |
05680287
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | University of Tsukuba |
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Principal Investigator |
TERANO Takao The University of Tsukuba, Dept.Socio-Economic Planning, Associate Professor, 社会工学系, 助教授 (20227523)
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| Project Period (FY) |
1993 – 1994
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| Keywords | Artificial Intelligence / Distributed Artifical Intelligence / Machine Learning / Organizational Theory / Organizational Behaviors / Organizational Learning / Communication / Logic Programming |
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| Research Abstract |
The high productivity of Japanese production systems are well-known. Various analyzes have been carried out to explain the principles. However, conventional organization and management theory has not succeeded in the explanation. The theory should formally explain the mechanisms of such typical activities in Japanese companies as Kaizen, Nemawashi, and so on. The important but difficult features of these activities are that they heavily rely on informal information processing among members and cannot be quanititatively measured.
Recent advances in Artificial Intelligence have made it possible to re-examine Simon's approaches with physical symbol systems hypothesis. To develop a rigorous theory on organizational learing, therefore, AI symbolic approaches are promising because of their descriptive powers and capabilities of computer simulation.
In this project, first we discuss the requirements of AI models applicable to organizational theory.Second, in order to facilitate the analyzes, we propose a computational model : LPC.The model consists of a set of agents with (a) a knowledge base for learned concepts, (b) a knowledge base for the problem solving, (c) a prolog-based inference mechanisms, and (d) a set of beliefs on the reliability of the other agents. Each agent can improve its own problem solving capabilities by inductive and/or deductive learning on the given problems and by reinforcement learing on the reliability of communications among the other agents.
Several experimental systems of the model have been implemented in CESP and Prolog languages. Experiments were carried out to examine the feasibility of the machine learning mechanisms of agent for problem solving and communication capabilities. The experimental results suggest that the proposed model is executable for analyzing the learning mechanisms applicable to distributed knowledge systems.
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