From: rick@cs.arizona.edu (Rick Schlichting)
Newsgroups: comp.research.japan,comp.ai
Subject: Kahaner Report: Hitachi activities in AI and Expert systems
Date: 26 Aug 92 22:20:19 GMT
Followup-To: comp.research.japan


  [Dr. David Kahaner is a numerical analyst on sabbatical to the 
   Office of Naval Research-Asia (ONR Asia) in Tokyo from NIST.  The 
   following is the professional opinion of David Kahaner and in no 
   way has the blessing of the US Government or any agency of it.  All 
   information is dated and of limited life time.  This disclaimer should 
   be noted on ANY attribution.]

  [Copies of previous reports written by Kahaner can be obtained using
   anonymous FTP from host cs.arizona.edu, directory japan/kahaner.reports.]

To: Distribution
From: 
 David K. Kahaner
 US Office of Naval Research Asia
 (From outside US):  23-17, 7-chome, Roppongi, Minato-ku, Tokyo 106 Japan
 (From within  US):  Unit 45002, APO AP 96337-0007
  Tel: +81 3 3401-8924, Fax: +81 3 3403-9670
  Email: kahaner@cs.titech.ac.jp
Re: Hitachi activities in AI and Expert systems.
26 August 1992
This file is named "hitachi.ai"

ABSTRACT. AI and Expert System activities at Hitachi are reviewed.
Current and research projects are mentioned.

In March 1992, a JTEC (Japanese Technical Evaluation Center) team,
headed by Prof. Edward Feigenbaum, Stanford U 
[Email: FEIGENBAUM@SUMEX-AIM.STANFORD.EDU], visited Japan to assess and 
report on Japanese activities in the areas of Expert Systems. An oral 
summary of their report was presented in June 1992 at the NSF in 
Washington DC, and the written report will be available soon. The JTEC 
team visited a large number of laboratories at Universities, government, 
and industrial R&D centers. I was able to accompany this group on many of 
their visits. The following report is an overview of one particular 
research organization, Hitachi, and their specific programs.  I have 
organized this to first catalog some specific examples of expert systems 
that have been developed by/for Hitachi customers. Thus these illustrate 
some expert systems that are in use here in Japan. Next we describe major 
research projects underway related to the topics of AI/ES and connections 
to  other research around the world.

The main software tool provided by Hitachi is a version of ES/KERNEL. 
(Separately named versions exist for workstation, mainframes, PCs, and 
for On-line processing. There is also a new workstation version ES/KERNEL 
2 which runs under X-Windows and Motif, which is claimed to be very user 
friendly.) More than 4,000 copies of ES/KERNEL have been sold in Japan. 
Source was written in C.  Users can make use of various reasoning 
strategies, including the following.  

  Production system: Reasoning proceeds through repetitive sequences of 
search for selection and execution of applicable rules.  

  Object oriented: Reasoning proceeds by repetitive sequences of passing 
messages to a method within a frame, and method execution.  

  Assumption-based: Reasoning starts with a number of possible solutions 
and solutions which do not meet the specified conditions are discarded.  
This can be used in the production system as a frame.  

  Multi-layered: This is a communication mechanism, executing reasoning 
through cooperation between different expert systems.  

Knowledge is represented by a combination of rules and facts. A rule is 
essentially an IF (condition) THEN (execute something). Facts are either 
represented as frames or private memos. A frame is used to represent 
static facts. In ES/KERNEL 2 these are implemented in a hierarchical 
structure via an object-oriented representation. Private memos represent 
unstructured knowledge, suitable for the description of transient facts 
that may be obtained in the course of reasoning.  

ES/KERNEL 2 also comes with a graphical knowledge editor, as well as a 
knowledge tailor, which allows knowledge to be moved from one 
representation to another.  

In addition, there are two domain shells, ES/Promote/W-Diag, for 
diagnosis applications and based on ES/KERNEL, and ES/Promote/W-Plan, for 
planning and scheduling applications which is based on Whispa. (I was 
told that the next generation of a domain scheduling shell will be based 
on Tosca, and incorporate extensive user feedback.) Other packages 
include a knowledge acquisition tool ES/Tool/W-RI for rule induction from 
examples, a shell for real-time process control, another for plant 
operation, and a user interface building tool UIBT which has some multi-
media capability. There is also ES/Guide for Expert System building 
support based on the spiral and waterfall model. Unit sales of these 
other tools are a small percentage of the ES/KERNEL sales.  Some fuzzy 
reasoning has been implemented in this package.

Hitachi has implemented Expert Systems (mostly via ES/KERNEL) in a large 
variety of application environments, including the following which are 
briefly described. Hitachi points out that "even though the range of 
application of expert systems is very wide, only advanced firms are 
engaging in their development, and then in limited areas." Thus, like the 
US, smaller companies have not yet embraced this technology.  The first 
list below contains material given to us by Hitachi in very abbreviated 
form. The second lists the titles and organization of expert systems in 
use that were provided to us in much more detail (omitted here). Thus we 
see that major application areas are in high volume large transaction 
processing, where the actual reasoning is shallow.  Finally, Hitachi 
pointed out that currently the most important application areas for 
Expert Systems are for scheduling. Less important, but still significant, 
are for diagnostics. This is also the case in the US.


(Brief descriptions from Hitachi)-----------------------

MANUFACTURING.

Blast furnace power generation support: User enters information on the 
status of turbines or other equipment, and system displays diagnostic 
messages and results of inferencing.  

Melting (steel production) planning support: Optimum stock of raw 
materials and most economical melting plan is created according to 
required monthly ingot production volume.  

Drainage system construction: User enters size of field and quality of 
soil, and system produces design drawings and estimate of expense and 
time required to complete job.  

Resin production planning support: User enters sales data and furnace 
operating status, and system produces integrated production plan, from 
sales to detailed production process. Knowledge base formed from 
production conditions and raw material composition.  

Boiler breakdown diagnosis: User (repairman) enters details of breakdown, 
and system produces breakdown cause interactively.  


CONSTRUCTION.  

Shield tunnel planning support: User enters construction period, diameter 
of bore, route, soil quality, volume of groundwater and other conditions, 
and system determines ideal shape of cutter, number of cutters and 
positioning of shield boring machine. This system required about 60 
person months to complete and a cost of about $500K US. It contains about 
9K lines of C, 100 frames, 850 rules, relates to about 250 parts on a 
shield machine, and accesses a relational database of about 15K items.  

Foundation improvement method selection support: User enters the status of 
subsidence and the structure to be built, and system produces optimum 
foundation improvement method.  


DISTRIBUTION.  

Work scheduling: Personnel management system for supermarkets. Performs 
inference based on weather, local conditions and Point of Sale data.  

Facing (merchandise distribution) control: Creates merchandise 
distribution (display) and configuration plans.  

Dispatch plans: Predicts delivery volume for the day from past data and 
also displays required number of trucks for each area.  

TV channel selection budgeting: Plans for TV commercials are created and 
budgeting is performed for specific regions.  


GOVERNMENT.  

Oil transformer breakdown diagnosis: User enters type and volume of gas.  
(Gas contained in a sample of insulation from transformer located in 
office or building is extracted and analyzed.) Inference is performed to 
pinpoint cause of trouble and next inspection date is determined.  

Landslide diagnosis: Power generation facility sites are checked to see 
if they are prone to landslides.  

Soil nematode diagnosis: Diagnoses whether or not crop damage is due to 
nematodes. If so, determines nematode type and displays method to 
eliminate the problem.  

Building inspection: Supports building inspection in areas of design, 
structure and facilities, to check whether or not they conform to permits 
and applicable codes.  

Elderly welfare consultation: User enters personal data, which then 
judges status and procedures. System also outputs nursing home entrance 
eligibility.  

SECURITIES.  

Investment portfolio creation support: Sales support system to create 
investment portfolio for clients from a large variety of products.  

Terminal configuration management: Intelligent terminal management for 
online system. Automates setting of parameters when new terminal is 
introduced.  

Stock investment consultation: Technical analysis of a large number of 
indices and the experience of stock experts is used to conduct stock 
consultation.  

INSURANCE.  

Claims assessment: Judges whether or not claim made by client for 
hospitalization, etc., should be paid.  

New contract assessment: Calculates contract conditions when a new 
contract is made, based on status of various illnesses.  

Health insurance assessment: Supports judgement of amount paid for 
hospitalization and operations.  

Plant fire/explosion risk diagnosis: User enters company and plant name, 
etc., and risk of fire or explosion is predicted.  

Fire prevention diagnosis report generation: Possible risks, foreseeable 
dangers and improvements are displayed to facilitate creation of fire 
prevention diagnosis reports.  

FINANCE.  

Budget assessment: Budget requests from branches for equipment are 
checked.  

Retirement consultation: Calculation of retirement allowance is performed 
as well as consultation on early payment of retirement.  

Optimum plans for land use: User enters location and surrounding 
environment.  

High-return money management plans: User enters return, convertibility 
and safety issues, system creates asset management plans.  

Asset management consultation: Interactive input of client's family and 
assets is performed. This data is used to make advice on asset 
management.  

Credit check: Checks applications for credit cards for eligibility.  


TRANSPORTATION.  

Scheduling: Supports creation of schedules for trains and buses.  

Operation management: Supports management of train operation.  


COMPUTER 

Computer breakdown diagnosis: Based on a database of experience from 
daily maintenance, system judges cause of trouble and instructs repair 
personnel on proper measures.  

Electronic data processing design support: Determines configuration of 
hardware and software and automatically generates system parameters from 
estimates of memory and disk capacity.  


(Examples where detailed descriptions are available) 

FINANCE.

  Budget Assessment Expert System
       The Dai-Ichi Kangyo Bank, Ltd.

  Design Support System for Financial Planning
       The Dai-Ichi Kangyo Bank, Ltd.

  Expert System for Business Analysis
       Hitachi Credit Corporation

  Expert System for Inheritance Advisory Service
       The Nishi-Nippon Bank, Ltd.

  Development Forecast Expert System
       The San-in Godo Bank, Limited

  Pension Consulting Expert System
       The Sanwa Bank, Ltd.

  Private Placement Straight Bond Advice System
       The Sanwa Bank, Ltd.

INSURANCE.

  Expert Medical Underwriting System
       Daido Mutual Life Insurance Company

Manufacturing

  Defect Diagnosis Expert System for Color Picture Tube Manufacturing
       Hitachi Electronic Devices (Singapore) Pte.  Ltd.

  Development of a Trouble Diagnostic Expert System for Water Heaters
       Toto Ltd.

STEEL/METAL.

  AI Applications for Ship Loading Instructions in the Transport Field
       Hokkaido ENICOM Co., Ltd.

  Application of Expert System Assisting Plant Operation in a Steel Mill
       Nippon Steel Corporation, Nagoya Works

  Application of AI to Process Scheduling
       Sanyo Special Steel Co., Ltd.

DISTRIBUTION.

  Planning Expert System for Television Spot, Level 2
       Daiko Advertising, Inc.

  Truck Operation Planning Expert Systems
       Nichirei Corporation

GOVERNMENT.

  Expert Systems for Dissolved Gas Analysis for Power Apparatus
            Insulating Oil
       The Kansai Electric Power Company, Inc.

--------------------------------------------------

NEW RESEARCH AREAS

Details of research projects were described to us by

    Dr. Hiroshi Motoda, Chief Researcher
    Hitachi Advanced Research Laboratory
    Hatoyama, Saitama 350-03, Japan
     Tel: (0492) 96-6111, Fax: (0492) 96-6005
     Email: MOTODA@HARL.HITACHI.CO.JP  or MOTODA@VM.USC.EDU 

Dr. Motoda was my host on a visit to Hitachi's Advanced Research Lab
(ARL), and I reported at that time ["hitachi", 12 Dec 1990] about
ARL. Several of the same projects were also presented to JTEC although
at a much later stage of research. There were some new activities too.
As readers will discover by perusing the list below, research here is
very high class and at an advanced state.

Automatic Knowledge Reformation: Basically this is research with the goal 
of providing tools to make knowledge base building easier.  Specifically, 
the plan is to automate the generation of an hierarchical, multilevel 
representation of complex data. The major activity has been to implement 
a program called CLIP (Concept Learning from Inference Pattern). The 
major application area is the electric circuit domain.  CLIP analyzes the 
inference process about the behavior of the physical device, and extracts 
the repetition of the typical inference pattern (i.e. the typical 
behavior of the device). It generates new abstract level descriptions by 
replacing the set of descriptions relating to a typical behavior with a 
single new description. The algorithm is similar to a genetic algorithm 
in order to avoid exhaustive search.  CLIP can restructure a single 
knowledge base into a more efficient form. It is claimed that inference 
costs are reduced to one half, or in some cases to less than one tenth.  
A major remaining research task is to study how to utilize multiple 
knowledge bases. (In the circuit case, these could be mechanical, 
thermodynamical, electromagnetic, etc.) Another research issue is to 
unify the induction from data with CLIP. I was also told that a second 
major application domain is solving first order ordinary differential 
equations, but I have had no opportunity to see this in action.  

Discovering characteristic chunks. This research is based on the observation 
that when we reason, we always think of some "imagistic" chunks relevant 
to the reasoning process. (Imagistic reasoning is sometimes called visual 
reasoning.) The goal is to study how and in what situations these 
imagistic chunks should be acquired and utilized, hoping thereby to 
realize sophisticated reasoning in a machine. The domain area is plane 
geometry.  The idea is that experts can recognize a whole figure as a 
composition of several characteristic figure chunks, giving them a 
perspective view.  The set of chunks will be stored in the knowledge base 
to be utilized in solving future problems. The proposed learning theory 
can be applied to derive these chunks out of the problem solving process. 
Plans are to collect a set of these chunks and evaluate quantitatively 
how much improvement in the reasoning can be gained by utilizing them in 
solving unseen problems in the future. A related research area is 
Explanation-based learning (EBL). Hitachi's idea is to use a new concept 
to address this problem. They call this recognizability, see Masaki Suwa 
& Hiroshi Motoda, "Learning Abductive Strategies from an Example", Ohio-
State Univ Tech Report, 91-JJ-Workshop.  

Development of a massive memory workstation. The goal is to explore the 
architecture necessary to develop a personal computer with gigabytes of 
memory.  It seems that large memory requirements are common in AI 
applications, and clearly could be of great use in database, CAD, and 
language processing, among other fields. A prototype workstation has been 
developed with 64MBytes of memory, using an R3000 (MIPS) CPU, and an 
Ethernet interface. Current work involves porting Unix as a operating 
system. There is also work building a non-volatile read/write 4GB memory 
machine using an R4000 (MIPS), also with an Ethernet interface. It is 
expected that this will require one to two years to get running.  
Research plans involve development of a single level storage (integrated 
memory and file space), precise memory protection (200-500KByte object-
wise), and persistency support (incremental execution). In the long run 
the plans are to study a new programming model, as it is claimed that 
current models focus on minimizing code and data size at the expense of 
execution speed. There is no question that if such a model could be 
developed to take advantage of huge memory space, it could have a very 
significant impact on algorithmic development. With memory prices 
dropping, especially for DRAM, this is an important research direction to 
follow. Motoda is thinking of the future, when a workbench for 
intellectual activities will require a terabyte of main memory, and a few 
tens of gigabytes of memory will be widely available within a few years.  

Information processing architecture. Goal is to establish a new framework 
for an architecture suited to simulate the recognition ability of humans 
(observation, identification, decision, action, experience, and 
learning). This is basically theoretical research, but plans are to 
experiment with current architectures and extract their problems. The 
experiments are being done via a small vehicle with information gathering 
and decision capability in a real environment. The vehicle has two video 
cameras that feed to an image processing board on a Sun. Data are 
processed by a software tool kit, which then connects with a control data 
transducer and is eventually transmitted to the servo motors of the 
vehicle.  The software is built on Motif in object oriented style. The 
researchers comment that other labs such as MIT, CMU, and Tsukuba have 
also been working on vehicles for uncontrolled environments. But their 
main stress has been on the vehicles, whereas Hitachi's research is 
mainly toward a new architecture (hardware and software) suited for 
mechanization of higher level recognition, using the vehicles only as 
experimental tools.  

Logical interpretation of English sentences. The goal is to perform 
natural language understanding, through the automatic translation of 
English sentences into logical form. Researchers have made precise the 
quantification of indefinite noun phrases, and are working on ellispis, 
disambiguation, anaphora, plural expressions, etc. It is claimed that 
their work is an improvement over the Core-Language-Engine, developed at 
SRI.  

Formal theory of discourse understanding. The goal is to construct a 
formal (logical) characterization of discourse semantics. The long term 
goal is to implement ideas gained here on machine translation or user 
interface systems, but research so far has concentrated on problems of 
metonymy. Discourse processing is an active research area, with papers 
from universities (Stuttgart, Edinburgh, Stanford), as well as government 
and industry (US Naval Ocean Systems Center and SRI in the US, and ETL, 
NTT, NEC, Fujitsu, and Matsushita in Japan).  

Formalized programming methods. Researchers have proposed a new 
programming model (set theoretic programming) and formulated a systematic 
derivation method of algorithms based on structural induction. Most (not 
all) textbook algorithms can be derived in this way. Current research is 
directed toward making this more powerful by using combinations of 
optimization patterns. Work on formalized programming goes back to 
E.Dijkstra and B.Nordstrom.

Domain theoretic study of data types with equations. Goals are to develop 
more expressible programming/specification languages and new semantic 
theories with more analyzing power. One method, denotational semantics, a 
theory based on domain concepts, has been recognized as very useful. The 
algebraic specification based on equational theories has been thought of 
as a promising method for formal specification of software.  Current 
research is to clarify the domain theoretic aspects of equationally 
defined data types. Work in this area is at a very early stage.  There is 
related work at CMU in the US, Edinburgh in Scotland, and at Pisa and 
Torino in Italy.  

In addition to these projects we were also told that there are activities 
in 
   Case Based Reasoning (Retrieval of reusable cases)
   Large Scale Knowledge Integration (Text based knowledge acquisition)
   A New Fast Inference Algorithm (CREST, Conflict Resolution Strategy
            oriented matching algorithm)

------------------------------END OF REPORT----------------------------

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