Akira Ishino

Efficient algorithms to compute compressed longest common substrings and compressed palindromes

This paper studies two problems on compressed strings described in terms of straight line programs (SLPs). One is to compute the length of the longest common substring of two given SLP-compressed strings, and the other is to compute all palindromes of a given SLP-compressed string. In order to solve these problems efficiently (in polynomial time w.r.t. the compressed size) decompression is never feasible, since the decompressed size can be exponentially large. We develop combinatorial algorithms that solve these problems in O(n4logn) time with O(n3) space, and in O(n4) time with O(n2) space, respectively, where n is the size of the input SLP-compressed strings.

Musical sequence comparison for melodic and rhythmic similarities

We address the problem of musical sequence comparison for melodic similarity. Starting with a very simple similarity measure, we improve it step-by-step to finally obtain an acceptable measure. While the measure is still simple and has only two tuning parameters, it is better than that proposed by Mongeau and Sankoff (1990) in the sense that it can distinguish variations on a particular theme from a mixed collection of variations on multiple themes by Mozart, more successfully than the Mongeau-Sankoff measure. We also present a measure for quantifying rhythmic similarity and evaluate its performance on popular Japanese songs.

Modelling Semi-structured Documents with Hedges for Deduction and Induction

Semi-structured documents are now commonly used for exchanging information. The aim of this research is to apply deductive and inductive reasoning to semi-structured documents. From our observation that first-order terms are inadequate for modelling semi-structured documents, we model them with hedges. After defining semi-structured documents and hedges so that they can contain logical variables, we introduce hedge logic programs, in which every argument of an atom is a hedge. We give a method for transforming hedge logic programs into original logic programs. We also give an algorithm for computing minimal common anti-unifications of hedges, with aiming inductive reasoning of hedge logic programs from sets of semi-structured data.

Autonomous Learning of Ball Trapping in the Four-Legged Robot League

This paper describes an autonomous learning method used with real robots in order to acquire ball trapping skills in the four-legged robot league. These skills involve stopping and controlling an oncoming ball and are essential to passing a ball to each other. We first prepare some training equipment and then experiment with only one robot. The robot can use our method to acquire these necessary skills on its own, much in the same way that a human practicing against a wall can learn the proper movements and actions of soccer on his/her own. We also experiment with two robots, and our findings suggest that robots communicating between each other can learn more rapidly than those without any communication.

Computing longest common substring and all palindromes from compressed strings

This paper studies two problems on compressed strings described in terms of straight line programs (SLPs). One is to compute the length of the longest common substring of two given SLP-compressed strings, and the other is to compute all palindromes of a given SLP-compressed string. In order to solve these problems efficiently (in polynomial time w.r.t. the compressed size) decompression is never feasible, since the decompressed size can be exponentially large. We develop combinatorial algorithms that solve these problems in O(n4 log n) time with O(n3) space, and in O(n4) time with O(n2) space, respectively, where n is the size of the input SLP-compressed strings.

A Method of Extracting Related Words Using Standardized Mutual Information

Techniques of automatic extraction of related words are of great importance in many applications such as query expansion and automatic thesaurus construction. In this paper, a method of extracting related words is proposed basing on the statistical information about the co-occurrences of words from huge corpora. The mutual information is one of such statistical measures and has been used for application mainly in natural language processing. A drawback is, however, the mutual information depends mainly on frequencies of words. To overcome this difficulty, we propose as a new measure a normalize deviation of mutual information. We also reveal a correspondence between word ambiguity and related words using word relation graphs constructed using this measure.

AVERAGE VALUE OF SUM OF EXPONENTS OF RUNS IN A STRING

A substring w[i.j] in w is called a repetition of period p if w[k] = w[k + p] for any i ≤ k ≤ j - p. Especially, a maximal repetition, which cannot be extended neither to left nor to right, is called a run. The ratio of the length of the run to its period, i.e. , is called an exponent. The sum of exponents of runs in a string is of interest. The maximal value of the sum is still unknown, and the current upper bound is 2.9n given by Crochemore and Ilie, where n is the length of a string. In this paper we show a closed formula which exactly expresses the average value of it for any n and any alphabet size, and the limit of this value per unit length as n approaches infinity. For binary strings, the limit value is approximately 1.13103. We also show the average number of squares in a string of length n and its limit value.

Light-weight Acceleration for Streaming XML Document Filtering

We present a light-weight streaming XML document filtering tool named XAXEN, which is scalable with respect to the number of queries. XAXEN consists of (1) an XML file transformer on data-sending server, which transforms an XML file into a trie representing the tag-name sequences of the root-to-leaf paths in XML tree and the binary XML file where every start- and end-tag is replaced with a special symbol followed by the corresponding path trie node ID and with another symbol, and (2) a query processor on receivers. Computational experiments show that XAXEN is 2 ~ 6 times faster and 6 times space-efficient in comparison with XMLTK, a well-known scalable streaming XML document filter.

Development of an Augmented Environment and Autonomous Learning for Quadruped Robots

This paper describes an interactive experimental environment for autonomous soccer robots, which is a soccer field augmented by utilizing camera input and projector output. This environment, in a sense, plays an intermediate role between simulated environments and real environments. We can simulate some parts of real environments, e.g., real objects such as robots or a ball, and reflect simulated data into the real environments, e.g., to visualize the positions on the field, so as to create a situation that allows easy debugging of robot programs. As an application in the augmented environment, we address the learning of goalie strategies on real quadruped robots in penalty kicks. Our robots learn and acquire sophisticated strategies in a fully simulated environment, and then they autonomously adapt to real environments in the augmented environment.

Generalizations in typed equational programming and their application to learning functions

In this paper we investigate generalization methods in typed equational programming and apply them to inductive inference of functions. We are interested in inducing programs from given examples which are input-output pairs. Our main contribution is a new generalization algorithm which uses type polymorphism. With the algorithm we introduce, for the first time, a generalization phase to Summers’ method. Moreover, we present a new bottom-up inference method which combines elements of the generalization algorithm, a minimal multiple generalization algorithm, and Summers’ method. This integration is enabled with the adaptation of equational programming.