A knowledge assimilation method for logic databases

In this paper we consider a deductive question-answering system for relational databases as a logic database system, and propose a knowledge assimilation method suitable for such a system. The concept of knowledge assimilation for deductive logic is constructed in an implementable form based on the notion of amalgamating object language and metalanguage. This concept calls for checks to be conducted on four subconcepts, provability, contradiction, redundancy, independency, and their corresponding internal database updates. We have implemented this logic database knowledge assimilation program in PROLOG, a logic programming language, and have found PROLOG suitable for knowledge assimilation implementation.     

Object oriented programming in Concurrent Prolog

It is shown that the basic operations of object-oriented programming languages — creating an, object, sending and receiving messages, modifying an object’s state, and forming class-superclass hierarchies — can be implemented naturally in Concurrent Prolog. In addition, a new object-oriented programming paradigm, called incomplete messages, is presented. This paradigm subsumes stream communication, and greatly simplifies the complexity of programs defining communication networks and protocols for managing shared resources. Several interesting programs are presented, including a multiple-window manager. All programs have been developed and tested using the Concurrent Prolog interpreter described in.¹⁾     

Flood Management in Japan—From Rivers to Basins

Abstract

The River Commission of the Ministry of Construction in Japan recommended a basin response to floods in its December 2000 Interim Report to Minister of Construction. This is practically an official declaration of the policy accepting inundation in habited areas. By this policy shift, it is anticipated that more basin response measures and non-structural measures against floods will be promoted to complement structural measures such as continuous levees and dams. Such a policy is not new in the world but is a significant governmental move in Japan, a densely populated, highly developed, and highly flood hazardous country. Although living with floods is a universal coping strategy, the implementation of measures should be different from region to region reflecting region's nature and socio-economic conditions. This paper reviews the flood fighting history of Japan and her statutory evolution against floods and shows the process by which the ever-increasing flood damage potential and the recent increase of flood damages necessitated the official move in flood control management from rivers to basins.     

A global localization approach based on Line-segment Relation Matching technique

This paper presents a novel global localization approach for mobile robots by exploring line-segment features in any structured environment. The main contribution of this paper is an effective data association approach, the Line-segment Relation Matching (LRM) technique, which is based on a generation and exploration of an Interpretation Tree (IT). A new representation of geometric patterns of line-segments is proposed for the first time, which is called as Relation Table. It contains relative geometric positions of every line-segment respect to the others (or itself) in a coordinate-frame independent sense. Based on that, a Relation-Table-constraint is applied to minimize the searching space of IT therefore greatly reducing the processing time of LRM. The Least Square algorithm is further applied to estimate the robot pose using matched line-segment pairs. Then a global localization system can be realized based on our LRM technique integrated with a hypothesis tracking framework which is able to handle pose ambiguity. Sufficient simulations were specially designed and carried out indicating both pluses and minuses of our system compared with former methods. We also presented the practical experiments illustrating that our approach has a high robustness against uncertainties from sensor occlusions and extraneous observation in a highly dynamic environment. Additionally our system was demonstrated to easily deal with initialization and have the ability of quick recovery from a localization failure.     

First principles total energy studies of the adsorption of germane on Ge(001)-c(2 × 4)

We perform first principles total energy calculations to study the energetics, and the atomic structure of the adsorption of germane (GeH₄) molecules on the Ge(001)-c(2 × 4) surface. The adsorption of a GeH₄ unit occurs after its dissociation into a germanium trihydride (GeH₃) and a hydrogen atom and a subsequent decomposition into a germanium dihydride (GeH₂) subunit and H atoms. Consequently, we first consider the adsorption of GeH₂ in two different configurations; the on-dimer and the intra-row geometries. Similar to the adsorption of SiH₂ and GeH₂ on Si(001), it is found that the on-dimer site is more stable than the intra-row geometry by 0.13 eV. However, in the adsorption of a GeH₂ fragment together with two H atoms we find that the intra-row geometry is energetically more favorable, again, similar to the adsorption of SiH₂ and GeH₂ (plus two H atoms) on the Si(001) surface.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.