称号及び氏名

博士(学術) Dedie Tooy

学位授与の日付

平成 1９年９月３０日

論「Intelligent Reasoning Systems for Decision Support in文

名Farming Mechanization Management（農作業の機械化管理における意思決定支援のための知的推論システム）」

論文審査委員

主査

村瀬治比古

副査

藤浦建史

副査

池田英男

副査

小山修平

論文要旨
1. Introduction
Study has been shown that farm mechanization accounts for thedrudgery of farming works aside from increasing the farm efficiency andproductivity in some countries. However, socio-culture and behavior andfarming management systems are barriers in increasing the adoption level offarm mechanization systems in some South East Asia countries. In Indonesia,
these barriers are nature since to increasing the recent subsistence agricultural
system to the commercial agriculture would transform the recent socio-culture
and behavior of the local people about adoption the relatively new system suchas farm machinery. Therefore, it needs reformulation of the farming
management system to consider the socio-culture and behavior to avoid
inappropriate machinery management offered in a given region.
The
problem arises in acquiring the data observed and analyzing the socio-culture
and behavior variables to the decision making process. It is because
socio-culture and behavior problem in planning decisions is a complex and fuzzy
problem without a cut and dried solution, while such kind of various criteria andalternatives of different decision makers must be considered for optimal decision
making. The difference in these approaches is usually related to particular
problem aspects such as the number involved in decision making and the degreeof vagueness (fuzziness) of human judgment. To coping this problem decision
support system software is developed to acquire the socio-culture and behavior

of the local people’s conditions and analysis such kind of data. It is designed toassist the decision maker to identify the important socio-culture and behavior
criteria that underlie farmers to make a decision, and to give advice on theappropriate farm machineries in a given region. It is hoped to supporting afarm mechanization plan before it is applied to farmers, to avoid fault of work,
and wasting subsidy if it failed after its application in mechanization project.
2.

Decision Support System in Farming Mechanization Management
The developed Decision Support System (DSS) is a computer based
system for aiding decision makers who deal with farm mechanization planningsuch as local government and manufacturers of farm machineries. The DSS
used matlab and C++ for program based and comma separated variables fortabular data. The intelligent system is developed to cope with the problem ofquickly perceiving and computing the complex data since the problem is acomplex and fuzzy problem without a cut and dried solution. The inputs aresocio-culture and behavior data observed from a given region. The outputs arepresenting the set of alternatives with explanations to support the decision
maker in making decisions.
3.

Intelligent Reasoning Systems for Decision Support System
In order to enhance the analyzing process acquiring the humanreasoning, the DSS uses inference method that can reason more efficiently
about occurring kinds of data such as what underlies the farmer making a
selection of farm machineries that the decision maker can analyze for better
understanding and better decision for a given region problem. Such systems are
utilizing to the ANFIS system (Adaptive Neuro-fuzzy Inference System), andreferring the FAHP (Fuzzy Analytic Hierarchy Process), and the ACO-F (AntColony Optimization with Fuzzy restricted goals).
The ANFIS model is utilized to select the important perceptional andbehavioral variables or criteria and determine the farmer’s interest in farmequipment. From some variables from the questionnaire, the output is fed to theFAHP model for assessing farmer’s perceptions and calculating their perceived
important criteria to propose the best appropriate selection. The triangularfuzzy member is utilized for the fuzzification of the crisp Pair-wise ComparisonMatrix (PCM). The Given crisp PCM, having the values ranging from 1/7 to 7,
are fuzzified using the triangular fuzzy number f=(l,m,u). The alpha cut analysis
is applied to transform the total weighted performance matrices into interval
performance matrices to account for the uncertainty in the fuzzy range chosen.
The confidence value ranges from 0 to 1, from the least confident to the mostconfident. The alpha cut analysis is used to transform the fuzzy performancematrix representing the overall performance of alternatives with respect to eachcriterion into an interval performance matrix to rank the level of importance ofeach criterion of behavioral judgments for each criterion from fuzzy

environments. In the other side, the ACO is developed as a learning system tool
using a biologically-inspired agent that is set up to emulate decision-making
behaviors by demonstrating the implications of various decision model types,
with various ant internal behaviors toward the targets. In ACO model, the finite
state automata engine is designed to simulate the agents’ searching for foods inspecified boundaries, demonstrates the agents’ behavior in finding the foods,
and calculates the successful number of agents finding the foods, to compare theimplications of each ant behavior. Then the ACO is set to gather the information
about the variables based on the objective function stated using our developed
heuristic algorithm. The objective function is stated based on three mostimportant variables: cost, preference and profit expectation. The level ofimportant (high, moderate and low) of the attributes combination is set as
subjective weight using eight defined objective fuzzy rules to satisfy theconstraints for the goals (the profit expectation should be higher than goal profit
expectation ( f PE ≥ G PE ), the cost should be less than goal cost ( f C ≤ GC ), and thepreference level should be higher than goal preference level ( f P ≥ GP )). The
preferable mix combination selections are determined by specified fuzzy
subjective classification of the decision maker using fuzzy logic reasoningsubsystem.
4. Validation of the Proposed Decision Support System
Each method is tested and evaluated using real data from North
Sulawesi Province, Indonesia. The data used were 51 farmers observed and 74
students (representing young people) observed from three upper secondaryschools in North Sulawesi Province Indonesia using questionnaires. Three kindsof interest perception to the hand tractors, small tractors and cow and plow are
the alternatives of selection in this attempt as they are the most known farmmachineries in the observed area to evaluate the system. The farmers’ opinion
was also assessed to obtain their subjective opinion without direction withquestionnaire for making comparison the negative and positive perception
impact to the farm machineries selection model.
The proposed DSS has been testified with resulting satisfactory solution
as the set up rules and the objective function fed in the models with the datagathered. The ANFIS model gives satisfaction to identifying the importantvariables of the interest in farming jobs of the young people, and the importantvariables to the prediction of farmers’ interest in farm machinery as set up using
some rules and ANFIS parameters. The FAHP model is also tested withsatisfactory to acquire the objective function in the algorithm with subjective

perception of farm machinery interest to the farmers to explain the criteria
contribution to the ranking of selection. The ACO-F is satisfactorily simulatingthe virtual ants as stated in our optimal heuristic algorithm to finding optimal
restricted mix attributes of machineries’ criteria as stated in the optimization
function with fuzzy goals we developed.
5.

Results and Discussions
The results show that the proposed intelligent DSS are sophisticated forsocio-culture and behavioral complex problem solution. The ANFIS approachproduces reliable results in selecting the important variables of humanperception and behavior which are complex and non linear. The FAHP
numerical examples with data observed are demonstrated and the results of thehierarchical assessment approach are subsequently proposed. The problem indetermining appropriate farm machinery in relation to the dynamics of farmer
preferences is addressed with showing the optimistic, pessimistic, and moderate(kansei words) of decision makers’ attitude behavior to the alternatives. The
results of ACO-F show that the efficacy of biologically-inspired agents as alearning simulator demonstrates the implications of the behavioral decisions
meta-heuristically to find the optimal mix combination as specified in fuzzy
restricted functions.
The implementation of the ACO-F is useful for
determining appropriate farm machinery and guiding the planning andevaluation of such kind of farm machinery being offering in selected regions
according to the local preferences and decision maker limitations.
6.

Conclusions and Future Works
More informative decisions for several models are recognized and themore appropriately accurate selected farm machine can be modeled using thisDSS by acquiring the socio-culture and behavior of the local people condition
and giving advice for appropriate machinery. The advantage of this system is inintelligent analysis of the complex data for identification, prediction,determination, and classification with excelling in reasoning out such complex
problems to the optimal decision support. It would aid problematic imprecise
judgment of decision makers to incorporating some parties’ perception, somecriteria with some alternatives for determining appropriate relatively new
systems offered in a given region.
Future work would be in developing a bio-economic model toincorporating this system to give more information to the impact of socio-culture& behavior change to the economic outcome in an integrated planning system of
specific farming crops in a given region.

審査結果の要旨

東南アジア諸国ではインドネシアをはじめ多くの国々がそれぞれの実情に合わせて農業の機械化を進めている．大型の国家予算を必要とする機械化による農業振興の大きな課題の一つが適切な農業機械の導入である．農業機械への過剰投資が発生しないように機械の組織的利用をはじめとして，一定の地域的広がりの中での機械の能力に対応した作業面積が確保されることなどが基本的に求められる．適正な機械を導入するためには，地形や気象条件，圃場の区画，土地条件，作業条件に応じた作業能率，実作業率，対象作物，地域における農業労賃，利用規模，機械利用経費などの多くの条件を考慮しなければならない．
以上のような諸条件を考慮して地域の農業構造等の実情に即した農業機械を導入することで地域の農業生産力の増進と農業経営の改善が期待できる．このような複雑な条件を満たす最適な農業機械を選択することは容易ではなく意思決定支援システムといったソフトウェアが利用されるケースもある．また，更にこれらの条件に加え，インドネシアなどでは地域ごとの民族や文化の差異，あるいは教育水準の違い等のヒューマンファクターを考慮しなければならないという課題がある．そこで本研究は，農作業の機械化管理を対象課題として感性工学の手法を導入した意思決定支援のための知的推論システムの構築を目的としている．
第 1 章では，機械導入による農業振興を図る際の適正農業機械の選択を例に，地域の社会構造や文化あるいは地域の農業従事者の価値観の差異などが適正農業機械を選択する上でいかに重要であるかについて言及し，その課題解決のために目的とする意思決定支援のための知的推論システムの構築を行う手順を示した．
第 2 章では，農作業の機械化管理における意思決定支援について概観し，ソフトウェアの開発環境と知的推論を実現するためのシステム構築に取り入れる各種アルゴリズムを明らかにした．
第3章では，ヒューマンファクターを扱う推論システムについて詳述し，新規開発アルゴリズムの特徴，適用手順および数学的誘導について明らかにした．適応型ニューロ・ファジー推論(ANFIS)，ファジー階層分析法(FAHP)およびファジー目標型蟻コロニー最適化法(ACO-F)を主要アルゴリズムとして開発した．ANFISは，農業機械導入に関して考慮すべき諸条件の中で，人の思考パターン，嗜好あるいは感受性などに影響される条件選択に適用するアルゴリズムとして開発した．アンケート調査などにおける質問項目の重要度を自動的に分析し，次段でファジー階層分析法を適用して目的とする適正農業機械を推定可能なソフトウェアとした．本研究では階層分析法の総合評価値の加重和の計算を，ファジィ積分に置き換え，評価項目間の相互作用を加えた総合評価を行えるファジー階層分析法に改良した．評価項目間の一対比較をし，各評価項目の重要度を求め，各評価項目について代替案間の一対比較を行い，各評価項目についての各代替案の個別評価値を求める手法を開発した． 用いたファジーメンバーシップ関数は，いずれも三角形型とした．ファジー目標型蟻コロニー最適化法の探査プロセスでは有限オートマトンを用いた．
第 4 章では，開発したアルゴリズムの動作確認を行った．実際にインドネシアで 125 人を対象にアンケート調査を実施し，小型乗用トラクター，耕運機および家畜を選定対象として開発ソフトウェアのテストを行ったところアンケート調査を実施した地域の実情からその結果が妥当であると判断できた．
第 5 章では，開発したアルゴリズムのそれぞれ ANFIS, FAHP および ACO-F に入手した調査データを与え，結果を求めた．ANFIS は重要変数の自動選択を担うプロセスであるが，農業機械を導入しようとする作業者が必要と感ずる変数として耕運の必要度，作業の熟練度および機械導入に関してのリスクの 3 変数が選択された．変数間に非線形性の強い関連が存在する感性由来の多変数からこれらの 3 変数を正しく導くことができた．ステップサイズ関数を適応的に 1.0～0.5 に変化させることで出力誤差が 14.4 から 3.8 に収束し良好な結果が得られた．FAHP モデルにより得られた結果では最も導入すべき農業機械として耕運機(ランク 0.777)つづいて小型乗用トラクタ(ランク 0.720)最後に家畜による鋤(ランク0.27)という順序が示され開発した FAHP 意思決定支援システムが正しく回答していることが確認できた．ACO-F についてはα=0.6 β=0.4 において第 8 番トラクタ(管理費 US$50, 利益約 US$60000, 満足度 7) が推薦され，導入条件である管理費 US$1000 以下, 利益約US$2000 以上, 満足度 4 以上を満足しており，良好な結果となった．
以上のように，インドネシアにおける適正農業機械導入に関わる問題を例として，地域の多様な文化や価値観などの特殊な条件を考慮した意思決定支援のための知的推論システムの構築を試み目的の成果を得た．これらは情報工学や感性工学などの基礎分野ばかりでなく，最適化手法や意思決定支援システムに関わる応用分野にも寄与するところが大きい．
よって最終試験の結果とあわせて，博士（学術）の学位を授与することを適当と認める．