Journal of the Japanese Society for Artificial Intelligence
Online ISSN : 2435-8614
Print ISSN : 2188-2266
Volume 3 , Issue 2
Showing 1-27 articles out of 27 articles from the selected issue
Print ISSN:0912-8085 until 2013
  • [in Japanese]
    Type: Preface
    1988 Volume 3 Issue 2 Pages 131
    Published: March 20, 1988
    Released: September 29, 2020
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  • Ken SATOH
    Type: Corner article
    1988 Volume 3 Issue 2 Pages 132-138
    Published: March 20, 1988
    Released: September 29, 2020
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  • Koiti HASIDA
    Type: Corner article
    1988 Volume 3 Issue 2 Pages 139-148
    Published: March 20, 1988
    Released: September 29, 2020
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  • [in Japanese], [in Japanese]
    Type: Cover article
    1988 Volume 3 Issue 2 Pages 149-155
    Published: March 20, 1988
    Released: September 29, 2020
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  • A. Donald NORMAN
    Type: Special issue
    1988 Volume 3 Issue 2 Pages 156-163
    Published: March 20, 1988
    Released: September 29, 2020
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  • Yuichiro ANZAI
    Type: Special issue
    1988 Volume 3 Issue 2 Pages 164-168
    Published: March 20, 1988
    Released: September 29, 2020
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  • Ken GORYO
    Type: Special issue
    1988 Volume 3 Issue 2 Pages 169-177
    Published: March 20, 1988
    Released: September 29, 2020
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  • Hiroshi KAWANO
    Type: Special issue
    1988 Volume 3 Issue 2 Pages 178-185
    Published: March 20, 1988
    Released: September 29, 2020
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  • Tadashi NAGATA, Yoshiaki TERAMOTO
    Type: Technical paper
    1988 Volume 3 Issue 2 Pages 186-195
    Published: March 20, 1988
    Released: September 29, 2020
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    A new technique for the robot planning, by which the action sequence of a robot is automatically produced, is proposed. In a conventional system,if the rules for actions of a robot and the state descriptions for a circumstance are given, then a sequence of actions necessary for achieving the goal state is obtained according to a deduction algorithm. In our system, however, the meta knowledge for determining the order of performing subgoals, of which the goal is composed, is automatically deduced at the first stage and then the planning proceeds by utilizing this meta knowledge. Moreover, the system is easy to understand and the planning algorithm is very simple, since the state is described on the basis of the object-oriented concept. The meta knowledge is derived by using a state transition diagram representing whether two subgoals arbitrarily selected are valid or not. The concepts of COMPLETE and quasi COMPLETE are also utilized in the planning system. These concepts are used in order to pick up the one to be handled from objects which are partially ordered by applying the meta knowledge. Improved STRIPS is used in the main part of the planning system. Some examples are shown and this system is clarified to be efficient by comparing with a conventional planning system in computing time.

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  • Seiichi NISHIHARA, Yoshikazu MATSUO, Katsuo IKEDA
    Type: Technical paper
    1988 Volume 3 Issue 2 Pages 196-205
    Published: March 20, 1988
    Released: September 29, 2020
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    A consistent labeling problem, given an object consisting of many subparts and locally legal interpretations of each subparts, is an NP-complete problem of finding all of the totally consistent interpretations. An inexact consistent labeling problem is an extended version of the exact one as above, in which to each of the local interpretation a weight representing its appropriateness is attached. Thus solving an inexact problem is to find all of the total interpretations, or labelings,such that the sum of the weights of local interpretations does not exceed a certain error budget. Strategies for the inexact consistent labeling problem have been proposed in various ways so far,which fall into two main classes: the depth-first approach and the breadth-first approach. We investigate here another breadth-first approach called a merge method, which repeats local synthesizing operations using a given merge sequence. After giving precise definition of the inexact consistent labeling problem and its equivalent representation,the constraint network, we describe the strategy of the merge method and introduce two factors, the common length and the induction length,which seem to significantly affect the efficiency of the method. It is proved that,to shorten the total processing time,a large common length and a small induction length are preferable. Making use of this characteristic, we develop algorithms that give an optimal and a semioptimal merge sequences. Finally, we make some experiment to prove the efficiency of each algorithm and to clarify the influence of each factor.

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  • Seiji YAMADA, Norihiro ABE, Saburo TSUJI
    Type: Technical paper
    1988 Volume 3 Issue 2 Pages 206-215
    Published: March 20, 1988
    Released: September 29, 2020
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    PiL (Paradigm-based inference Learner)is a learning system that can acquire strategy knowledge of problem solving. PiL has basic operators (primitive transformation rules) a priori, but can not solve a problem with only this knowledge. Because the conditions of basic operators are so general, if all possible operators are applied to a problem state, the search space gets too large to reach the goal state. So a teacher gives PiL solving examples and PiL generalizes the examples and specializes the basic operators by adding the generalized solving examples to the conditions as conjunction. PiL generalizes examples with the condition-propagation based generalization method that is a kind of explanation-based generalization. PiL generally consists of two modules, problem solving module, and knowledge maintenance module. The problem solving module solves the problem with production system, and details the given (or solved by itself) solving example. The knowledge maintenance module generalizes a solving example, extracts macro operators and absolute operators from generalized operator sequence, and arranges knowledge base. Further, PiL's knowledge base has hierarchical structure consisting of finish-condition rules, macro operators, absolute operators, heuristic operators and basic operators to control inference. When a problem is given, first PiL tries to solve it by itself, and if it can't, PiL requires a teacher to give a solving example. Next, PiL translates the given solving example to sequence of PiL's operators, and generalizes it with condition-propagation based generalization method. The condition-propagation based generalization is done by regressing the conditions of operators applied in the given solving example through operator sequence. Further more, from generalized operator sequence, PiL is able to extract absolute operators and macro operators that are powerful knowledge in problem solving. In this paper, We will report an application of PiL in an equation & inequality of the first degree.

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  • Hirotaka HARA, Hajime KITAKAMI, Jun NAKAJIMA
    Type: Technical paper
    1988 Volume 3 Issue 2 Pages 216-223
    Published: March 20, 1988
    Released: September 29, 2020
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    It is desirable for expert systems to deal with temporal knowledge. Expert systems such as ONCOCIN, MECS-AI are developed for this purpose, and one of the most populer mechanism was Allen's Interval Logic. In this paper, we propose a temporal knowledge representation and a reasoning mechanism on it which is based on Kowalski's Event Calculus. First, we define a primitive predicate which will enable us to represent many kinds of temporal knowledge uniformly, including the continuity of a state which Event Calculus can't deal with. And we define macro operators such as "since","till", "at" in order to represent the temporal knowledge easily. Next, for the consistency of the knowledge-base, we introduce an inconsistency check mechanism with the constraint about simultaneity. Then we propose a temporal reasoning mechanism with default about time which means "State will continue unless inconsistency occurs". In the process of the default reasoning, we also use the constraint about simultaneity. Finally, we present a simple example of the knowledge-base and the temporal reasoning on our system implemented in Prolog.

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  • [in Japanese]
    Type: Other
    1988 Volume 3 Issue 2 Pages 224-225
    Published: March 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Other
    1988 Volume 3 Issue 2 Pages 225-226
    Published: March 20, 1988
    Released: September 29, 2020
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  • [in Japanese], [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 2 Pages 227-228
    Published: March 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 2 Pages 229
    Published: March 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 2 Pages 230
    Published: March 20, 1988
    Released: September 29, 2020
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  • [in Japanese]
    Type: Corner article
    1988 Volume 3 Issue 2 Pages 231
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Other
    1988 Volume 3 Issue 2 Pages 232
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 2 Pages 233-238
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 2 Pages 239-242
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 2 Pages 243-244
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 2 Pages 245-247
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Activity report
    1988 Volume 3 Issue 2 Pages b001-b005
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Cover page
    1988 Volume 3 Issue 2 Pages c002
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Cover page
    1988 Volume 3 Issue 2 Pages c002_2
    Published: March 20, 1988
    Released: September 29, 2020
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  • Type: Table of contents
    1988 Volume 3 Issue 2 Pages i002
    Published: March 20, 1988
    Released: September 29, 2020
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